• Open

    How are people getting A.I. voices of Resident Evil Characters?
    How do channels like TriggerHappy Productions and WeskerandFriends get the A.I. voices of all these Resident Evil characters? submitted by /u/Conscious-Theory-850 [link] [comments]  ( 8 min )

  • Open

    Up-down permutations
    An up-down permutation of an ordered set is a permutation such that as you move from left to right the permutation alternates up and down. For example 1, 5, 3, 4, 2 is an up-down permutation of 1, 2, 3, 4, 5 because 1 3 2. Up-down permutations are […] Up-down permutations first appeared on John D. Cook.  ( 5 min )
    Variance of binned data
    Suppose you have data that for some reason has been summarized into bins of width h. You don’t have the original data, only the number of counts in each bin. You can’t exactly find the sample mean or sample variance of the data because you don’t actually have the data. But what’s the best you […] Variance of binned data first appeared on John D. Cook.  ( 5 min )
    Ancient estimate of π and modern numerical analysis
    A very crude way to estimate π would be to find the perimeter of squares inside and outside a unit circle. The outside square has sides of length 2, so 2π < 8. The inside square has sides of length 2/√2, so 8/√2 < 2π. This tells us π is between 2.82 and 4. Not […] Ancient estimate of π and modern numerical analysis first appeared on John D. Cook.  ( 6 min )
  • Open

    LLM models for interpreting tables and charts [D]
    Hi all, Curious if anyone has recommendations on models to use to interpret the data in tables? I'm playing around with Google's Matcha model, which performs fine. seems like extracting the data out of a table and asking GPT4 to analyze it performs a bit better but requires extra steps. I'm specifically not looking to interpret graphs, but rather tables. e.g., can i ask the model to identify if there are any errors in the table / any data points that don't tie if the rows are supposed to sum up. submitted by /u/eyeronthrone [link] [comments]  ( 8 min )
    [N] Conference Codes
    I'll likely be downvoted to hell but here goes: Prices for The AI Conference double at midnight Pacific. 46 Speakers, 10+ topics, 2 Days plus a hackathon at night! Join us to learn and collaborate with scientists, engineers and founders from the top AI companies and projects. Speakers include: Ben Mann | Co-Founder | Anthropic Peter Norvig | Director of Research | Google Nazneen Rajani | Research Lead | Hugging Face Igor Markov | Research Scientist | Meta Bryan Catanzaro | VP Of Research | Nvidia Ram Sriharsha | VP of Engineering and R&D | Pinecone Jerry Liu | Co-founder | LlamaIndex Harrison Chase | Co-founder | LangChain Alex Chao | Product Manager Semantic Kernel | Microsoft See All Speakers Last chance to get in on early bird pricing (save $400 on a 2 day pass). If you can read this and I'm not downvoted to hell, use discount code redditlove for 25% off. Use discount code "student" for $200 student tickets \*Must Use EDU email to register* **This is my event and therefore self-promotion ​ ​ submitted by /u/shonburton [link] [comments]  ( 9 min )
    [D] Where did all the ML research go?
    For the past several years this subreddit has been my favorite source to keep up with new, interesting ideas and research from all over the field. It's great to have a way to break out of my own insular research bubble and spread out a bit more. Unfortunately, it looks like that era has passed. The sub has been seemingly shifting away from research in the past 1-2 years. Whenever research is posted, it is almost always LLM based with very little variety (considering the plethora of research areas in ML). I don't mean to assert that this is a bad thing, as the constant upvotes indicate that there is a high demand for LLM projects and research. Heck, I'm also interested in lots of the recent work with LLMs, and I plan to keep up with it – but I also would also love a venue with a diversity of ideas and topics. Machine learning is a HUGE field, and only focusing on a small subset of it seems like a waste. I don't mean to rant, but rather to ask: are there any other subreddits like this, or perhaps, any other active communities with a broader scope? Or if this doesn't exist, is there a demand for it? Or is it just me? submitted by /u/ejmejm1 [link] [comments]  ( 9 min )
    [D] elasticsearch HNSW python implementation
    Is there any documentation available which will help in implementing elasticsearch HNSW ANN search in python? I've searched a lot but i cant find anything in official documentation too Any help will be appreciated. TIA submitted by /u/adiraat [link] [comments]  ( 8 min )
    Why CUDA 11.7? Can more recent versions of CUDA be used? Is this a PyTorch limitation? [D]
    Everyone always seems to use CUDA 11.7. Is there a reason for this? What is the factor that limits the CUDA version used? Are there any speed/efficiency advantages to using a more recent version of CUDA, such as CUDA 12.0? What exactly is the limiting factor here, PyTorch? I've looked in the PyTorch docs but I don't see where the CUDA version is defined. Where can I find the maximum CUDA version I can use with the latest (or any given) PyTorch version? Thanks! submitted by /u/Pan000 [link] [comments]  ( 8 min )
    [D] Model design for outputting reliable multiclass probabilities
    Hey guys, I am working on a horse racing model to identify the probabilities of each horse winning a race. I currently have a feed forward NN with a final SOFTMAX layer to simulate probabilities of each horse winning using cross-entropy loss. My plan here being that if the model outputs, [0.05, 0.4, 0.2, 0.15, 0.2] then horses 1-5 have the corresponding probability of winning. The model has been trained like a regular classification task where the target is a one-hot vector describing the winner. Unlike previous work I have done where SOFTMAX output lends itself to some "confidence" score, this task requires that the model outputs be indicative of probabilities. My concern is that experientially, NNs tend to be overconfident with their answers in this type of setting. However, I wish to keep using a NN as each race datapoint has around 3k features - did not find good results with XGBoost. Any good practices for modelling probabilities in this sort of scenario? For context, the probability of a horse winning is what sets the odds for that horse. submitted by /u/HStuart18 [link] [comments]  ( 9 min )
    [D] Running Free Willy / stable baluga 2
    I was wondering if anyone knows how difficult it is to set up a server to run the 70B llama / llama 2 variants like these top ones on the hugging face leaderboard https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard What type of gpu would I need to set it up? Would the high ram t4 you get with Google colab+ be enough or does it require more power / space? Thanks in advance! submitted by /u/Additional_Elk4745 [link] [comments]  ( 8 min )
    [R] Attention over pre-trained Sentence Embeddings for Long Document Classification
    Article available here: https://arxiv.org/pdf/2307.09084.pdf Thoughts? submitted by /u/MuffinB0y [link] [comments]  ( 8 min )
    [P] Apple - Fruit = X? Combine Queries and Explore CLIP Embedding Space With rclip
    Hi. I've shipped an update to my rclip – a command-line photo search tool powered by CLIP. Now, you can add and subtract image and text queries from each other; here are a few usage examples: cd photos && rclip horse + stripes cd photos && rclip apple - fruit cd photos && rclip "./new york city.jpg" + night cd photos && rclip "2:golden retriever" + "./swimming pool.jpg" cd photos && rclip "./racing car.jpg" - "2:sports car" + "2:snow" If you want to see how these queries perform when executed on the 1.28 million images ImageNet-1k dataset, check out the demo on YouTube: https://www.youtube.com/watch?v=MsTgYdOpgcQ. rclip source code is published on GitHub under the MIT license and offers a pre-build distributable for Linux (installation instructions are in the README): https://github.com/yurijmikhalevich/rclip. Give it a try and let me know what you think! submitted by /u/39dotyt [link] [comments]  ( 9 min )
    [D] Open Source Model Combination To Turn Images -> LLM?
    Im trying to research into open source text models [like Llama] and image models [like Stable Diffusion]. My goal is to give the model(s) a picture of birds and bees, then ask it to "circle" the bees. The idea is, when given an image, it would produce coordinates on that image where the line should be circled. It could also represent where it should "click" on all the bees. Does something like this exist? submitted by /u/MindWithEase [link] [comments]  ( 8 min )
    Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
    submitted by /u/Working_Ideal3808 [link] [comments]  ( 8 min )
    [P] Pair programming my website with an AI developer
    submitted by /u/williamsweep [link] [comments]  ( 8 min )
  • Open

    [Reinforcement Learning: an Introduction (2nd edition)] Why not the joint distribution for equations 3.5 and 3.6?
    Greetings! I'm going through the initial equations that define most of the theoretical framework for the specialization. One curious thing I noticed with equations 3.5 and 3.6 is that they use the conditional distribution p(s′,r∣s,a) without including any priors. I'm talking about priors because, unless I'm missing something huge, the definition of the expected value for the reward (for both 3.5 and 3.6) should use the joint distribution for all 4 dimensions (next state, reward, current state, action). From that joint distribution, we can factorize it to show p(s′,r∣s,a). For example, one factorization that seems to make sense for this kind of model is p(s′,r,s,a) = p(s′,r∣s,a) ⋅ p(s) ⋅ p(a) which would turn, for example, equation 3.5 into r(s,a) = ∑​ ∑ ​r ⋅ p(s′,r∣s,a) ⋅ p(s) ⋅ p(a) (Note: the two sums are for "r" and "s' ". I wrote like that because I don't know write it in Latex or similar...) What am I missing? Is it because s and a are given as parameters of the function r(s,a) meaning that p(s) = p(a) = 1? If the factorization above is the right one for those equations, is this the only factorization used in the entire book? Thanks in advance! submitted by /u/SupBiebi [link] [comments]  ( 9 min )
    [Discussion] Comprehensive learning resources that emphasize DEEP reinforcement learning?
    So I understand that there is the Sutton & Barto book on reinforcement learning in the sidebar. I was wondering what other resources you guys have used that you would recommend that emphasize deep reinforcement learning for someone with some experience in shallow/classical reinforcement learning already and some experience with deep learning already, but new to deep reinforcement learning submitted by /u/BornAgain20Fifteen [link] [comments]  ( 8 min )
    What are some big action space MARL stochastic games implemented in OpenSpiel or equivalent?
    Are there big action space stochastic games that are implemented in OpenSpiel or equivalent? I played around Markov soccer game a lot but it's solvable with tabular methods and I was looking for games with at least more than 500 actions both players can take as a testbed for more complicated action spaces? submitted by /u/Potential_Biscotti14 [link] [comments]  ( 8 min )
    Optimal Bidding Strategy in Power Market using Reinforcement Learning
    Hello everyone! I'm trying to use reinforcement learning to solve a problem in the power market. The problem is about finding the best strategy for bidding on electricity for each hour of the day, considering both buying and selling options. Let's say we have a generator that can produce up to 800MW of electricity per day, and it can be charged up to 200MW per hour. After charging it for 4 hours continuously, it reaches its maximum capacity, and we can't charge more until we discharge some electricity. We have access to data from the past 5 years, including information about temperature, hydro, gas prices, and locational marginal price, which is important for determining profit. For instance, if we buy 10MW of electricity for a specific hour, our profit for that hour is 10 times the locational marginal price. The goal is to maximize profit at the end of the day while making sure that the total electricity bought and sold is equal for all days. This means we want to avoid wasting electricity. I initially tried using deep Q-learning, where the agent's state consists of data from the past 3 days, and the agent can take actions to buy or sell a certain amount of electricity for one hour. However, this approach doesn't seem to provide accurate results, and it works step by step, not considering the overall outcome for the whole day. So, I'm looking for help on how to build an agent capable of producing 24 bids for 24 hours, considering the constraints of the generator's capacity and ensuring no waste of electricity. I'm new to reinforcement learning, and I'm not sure how to approach this complex problem. Any guidance would be greatly appreciated! submitted by /u/uonliaquat [link] [comments]  ( 9 min )
    Looking for old tutorial series
    A few years ago, I remember reading a multipart series of articles/blog posts explaining how to develop agents for classical games. I believe the series started with tic-tac-toe and definitely progressed to gomoku, before maybe moving on to more complex games. I think there was more of a focus on algorithms (maybe MCTS) and concepts than code. It's a long shot, but does anyone recall this series or know if it's archived somewhere? seems like it might have been taken down. Wasn't on Medium. I think it might've been a personal website. I vaguely remember a green UI theme? submitted by /u/nothymn [link] [comments]  ( 8 min )
  • Open

    Is image generation or text generation more impactful?
    Curious what people's stance on this is. Why? View Poll submitted by /u/philippemnoel [link] [comments]  ( 8 min )
    State of AI security.
    submitted by /u/Philipp [link] [comments]  ( 8 min )
    One-Minute Daily AI News 7/31/2023
    Deutsche Telekom, e&, SK Telecom (SKT), and Singtel penned an agreement to form a global telecoms AI alliance designed to use the technology to unlock new business opportunities and accelerate industry growth.[1] Influencers Lil Miquela, Imma, and supermodel Shudu have raked in millions from deals with fashion giants such as Dior, Calvin Klein, Chanel, and Prada. But these shiny celebrities all have one thing in common — not one of them is real.[2] Google’s chatbot Bard reveals the jobs most at risk of artificial intelligence with truck drivers and data entry clerks on the list – while teachers and lawyers are among the safest careers.[3] DoorDash Inc., the US food-delivery service that competes with Uber Technologies Inc. and GrubHub, is looking to speed up ordering and help customers find food options with an artificial intelligence-based chatbot.[4] Sources: [1] https://www.mobileworldlive.com/featured-content/home-banner/global-operator-giants-launch-ai-alliance/ [2] https://www.the-sun.com/tech/8725778/ai-influencers-fashion-deals/ [3] https://www.dailymail.co.uk/news/article-12354605/googles-AI-bard-predicts-jobs-risk.html [4] https://www.bloomberg.com/news/articles/2023-07-27/doordash-is-working-on-an-ai-chatbot-to-speed-up-food-ordering?in_source=embedded-checkout-banner submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )
  • Open

    Build protein folding workflows to accelerate drug discovery on Amazon SageMaker
    Drug development is a complex and long process that involves screening thousands of drug candidates and using computational or experimental methods to evaluate leads. According to McKinsey, a single drug can take 10 years and cost an average of $2.6 billion to go through disease target identification, drug screening, drug-target validation, and eventual commercial launch. […]  ( 15 min )
    Is your model good? A deep dive into Amazon SageMaker Canvas advanced metrics
    If you are a business analyst, understanding customer behavior is probably one of the most important things you care about. Understanding the reasons and mechanisms behind customer purchase decisions can facilitate revenue growth. However, the loss of customers (commonly referred to as customer churn) always poses a risk. Gaining insights into why customers leave can […]  ( 14 min )
  • Open

    Doctor AI: Healing humans and mother earth hand in hand
    Let’s image – with algorithms and a nerdy charm that could melt any data center, an ‘AI’ wearing lab coats and stethoscopes patrolling hospital hallways, tirelessly monitoring patients. The digital doctor will take the pulse of Mother Earth and reduce waste, cut energy consumption, and cut energy consumption! The artificial intelligence community is well aware… Read More »Doctor AI: Healing humans and mother earth hand in hand The post Doctor AI: Healing humans and mother earth hand in hand appeared first on Data Science Central.  ( 20 min )
    Increase efficiency of manufacturing operations with IoT solutions
    In an age where efficiency is king, manufacturing firms are in a constant race to outshine their competition. Imagine if you could boost productivity, slash downtime, and cut costs all at once. Sounds like a dream, right? The good news is, this isn’t a fantasy. It’s achievable through Internet of Things (IoT) solutions. IoT solutions… Read More »Increase efficiency of manufacturing operations with IoT solutions The post Increase efficiency of manufacturing operations with IoT solutions appeared first on Data Science Central.  ( 21 min )
    Human-centered data networking with interpersonal knowledge  graphs
    “If you start by creating your data, then it’s like you are piling up some value or you’re creating some assets,” WordLift CEO Andrea Volpini told me in our recent FAIR Data Forecast interview. Volpini’s an advocate for adding structured data such as Schema.org to your content. That way, the content becomes logically connected and… Read More »Human-centered data networking with interpersonal knowledge  graphs The post Human-centered data networking with interpersonal knowledge  graphs appeared first on Data Science Central.  ( 21 min )
  • Open

    Interview with Hikaru Shindo and Quentin Delfosse: Neurosymbolic Reinfor...
    submitted by /u/Neurosymbolic [link] [comments]  ( 8 min )
  • Open

    Using AI to protect against AI image manipulation
    “PhotoGuard,” developed by MIT CSAIL researchers, prevents unauthorized image manipulation, safeguarding authenticity in the era of advanced generative models.  ( 10 min )

  • Open

    [R] Towards robust production machine learning for software systems - Survey
    Could you please help us get more responses for this study? As part of my PhD research project at Applied Artificial Intelligence Institute of Deakin University, we are investigating the challenges that software engineers face when working with machine learning (ML) models in production. Moreover, we explore how to enhance our proposed solution to better meet the needs of these engineers. ​ The objective of this study is to pinpoint the areas where software engineers need more support and resources to effectively work with ML components in production. It also aims to evaluate the effectiveness of a proposed protocol to improve software engineers' productivity and enable them to work more effectively with ML components in production environments. ​ With the knowledge gained from this i…  ( 9 min )
    [D] Number of epochs for a BERT based model
    Hello everyone. I am trying to replace the GloVe embeddings based model outlined in this paper by BERT embeddings. The authors of the paper have trained their model for 250 epochs, which for what I am doing is not feasible. I was wondering what would be the recommended number of epochs I should run the BERT model for? I know it is a pretty open ended question, but I was looking to get the community's view on how much epochs should a BERT based model be trained for. Any information will be much appreciated. submitted by /u/nocturnal_1_1995 [link] [comments]  ( 9 min )
    [N] AI Usage Fees Up to 15x Cheaper for English Than Other Languages
    submitted by /u/geekinchief [link] [comments]  ( 8 min )
    [D] Alternatives to HF or a path forward for the OSS community?
    I think it’s clear that Hugging Face is not aligned to the OSS community any more and it’s only going to get worse over the next few years. What are the top alternatives or where should the OSS contributors go? I’m trying to think ahead to what libraries we should rely on and contribute to. Anyone else have this as a worry? https://twitter.com/untitled01ipynb/status/1685667451197878272 submitted by /u/homunculAI [link] [comments]  ( 8 min )
    [R] Compressing vision-language and unimodal Transformers via structured pruning
    🚀 Code: https://github.com/sdc17/UPop 📑 Paper: https://proceedings.mlr.press/v202/shi23e/shi23e.pdf 🧐 A Quick Look What is it: UPop is the first structured pruning framework for vision-language Transformers. It enables effective structured pruning on various multi-modal & uni-modal tasks (including Visual Reasoning, Image Captioning, Visual Question Answer, Image-Text Retrieval, Text-Image Retrieval, Image Classification and Image Segmentation), datasets (including NLVR2, COCO Caption, VQAv2, COCO, Flickr30K, ImageNet and ADE20K), and model architectures (including BLIP, CLIP, DeiT and Segmenter). https://preview.redd.it/gfbjnxjm95fb1.png?width=2145&format=png&auto=webp&s=108898690f66a1f0afa068b69487859213055928 What challenge does it tackle: The above figure demonstrates that Unified Search adopted by UPop rescues us from the burden of repeated experiments (e.g., doing grid search) for searching optimal compression ratios among different modalities and structures. Furthermore, Progressive Pruning adopted by UPop eliminates the weight gap between the searched model and the pruned subnet to be retrained, therefore gaining better convergence and performance, especially at high compression ratios. How about the performance: On multimodal tasks, for example, UPop can achieve 2x compression with only 1.2% and 2.0% accuracy loss on the VQAv2 dataset for Visual Question Answer and the NLVR2 dataset for Visual Reasoning, respectively. On unimodal tasks, for example, UPop can achieve 1.5x and 1.2x compression without any loss of accuracy on the ImageNet dataset for Image Classification and the ADE20K dataset for Image Segmentation, respectively. Some examples of vector-level structured granularity are as follows. https://preview.redd.it/lifz1n1ia5fb1.png?width=1187&format=png&auto=webp&s=f419d9c5fb4d80a2a564198eba356021e1c275e4 submitted by /u/Salty-Situation2606 [link] [comments]  ( 9 min )
    [P] [HIRING] High Paying ML Jobs
    ​ Title Company Location URL Senior Software Engineer (Backend) Nova Credit Remote https://pycareer.io/jobs/6816 Data Scientist - Delivery, Senior-Staff Instacart Not Specified https://pycareer.io/jobs/6773 Data Scientist Data Scientist United States https://pycareer.io/jobs/6780 Senior Data Scientist (NLP and Classification Expert) › Senior Data Scientist (NLP and Classification Expert) › Not Specified https://pycareer.io/jobs/6781 Senior Software Engineer (Backend) Senior Software Engineer (Backend) United States https://pycareer.io/jobs/6788 AWS Data Engineer Apply Not Specified United States https://pycareer.io/jobs/6801 Senior Data Engineer Manager Apply Not Specified United States https://pycareer.io/jobs/6802 Data Scientist – Delivery, Senior-Staff Instacart Instacart Remote https://pycareer.io/jobs/6805 Software Design Engineer – NET, Python – Citizen/GC (H) Not Specified Remote https://pycareer.io/jobs/6837 Senior Data Scientist at Getty Images Getty Images Remote https://pycareer.io/jobs/6839 Lead Data Scientist at General Mills General Mills Remote https://pycareer.io/jobs/6840 Data Scientist – Delivery, Senior-Staff at Instacart Instacart Remote https://pycareer.io/jobs/6842 ​ submitted by /u/tadasg6 [link] [comments]  ( 9 min )
    [P] PromptTools: Open source tools for language model evaluation
    submitted by /u/hegel-ai [link] [comments]  ( 8 min )
    [R] If you have to do a ML project for prediction macroeconomic factors which factor would you choose
    For a master thesis I want to write a ML model (and hopefully make my own contribution) and I plan to use macroeconomic data. I could predict the typical inflation, GDP, unemployment, but are there any other factors that are important. Could you give me some ideas. Thanks! submitted by /u/AnyJello605 [link] [comments]  ( 8 min )
    [D] Can artificial intelligence solve the problem of crop diseases — and help curb global hunger?
    submitted by /u/Muinonan [link] [comments]  ( 8 min )
    [D] Interesting real-world applications for fine-tuning T5, and similar models?
    Everyone is going crazy creating LORAs and fine-tuning huge LLMs, however I've seen many suggesting that models such as T5 from Google has its place in the enterprise. Have you guys used this or similarly small models for any novel real world problems? Please do share! submitted by /u/MonkeyMaster64 [link] [comments]  ( 8 min )
    [D] Simple Questions Thread
    Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]  ( 8 min )
    [P] Deep Dive and Experiments for the NN + Gzip Method vs LLMs
    submitted by /u/seraschka [link] [comments]  ( 8 min )
    [R] NEnv: Neural Environment Maps for Global Illumination
    submitted by /u/crp1994 [link] [comments]  ( 8 min )
    [D] How to generate masks for overlapping classes to COCO format labels, to be used in transformer models like Segformer.
    Hi I am new to computer vision, I am working on a particular hackathon challenge, where the input labels are in COCO format. I am using the following code to generate masks, cat_ids = coco.getCatIds() anns_ids = coco.getAnnIds(imgIds=img['id'], catIds=cat_ids, iscrowd=None) anns = coco.loadAnns(anns_ids) anns_img = np.zeros((img['height'],img['width'])) for ann in anns: anns_img = np.maximum(anns_img,coco.annToMask(ann)*ann['category_id']) But the image has overlapping labels for some pixels, and this masking will only assign one label for such pixel, resulting in information loss, each there any way to prevent this and preserve the information? submitted by /u/franticpizzaeater [link] [comments]  ( 9 min )
    [Discussion] what should I do?
    Hi, y’all. So, I completed my masters degree. Got a programming job. I’m amazed at the capabilities of machine learning and want to build my own models. I don’t really want to go and get another degree, but want to learn how to build models. I’m particularly interested in forecasting because my job deals with NASA and wind data. I’m wondering if we could predict 6 hour wind data with a balloon sounding. I know c++ and python. How do I stay relevant to the changing technology space and learn how to build some cool stuff that may be useful? Thanks for any advice. submitted by /u/corey4005 [link] [comments]  ( 9 min )
    [D] Calculate 'w' and 'b' in hard margin SVM
    Hello everyone, I have been asked the following question related to SVM (Hard Margin) in the exam, and I failed to answer it. Can anyone help me find the solution? My approach was to sketch it and draw the marginal plane, then identify support vectors using my intuition. After that, I created a hyperplane that was the midpoint of both marginal planes, found its slope and y-intercept, but still, my answer was wrong. I am very new to machine learning, so any help would be appreciated. Consider the dataset M = {((1, 0)^T, 1), ((0, −1)^T, 1), ((1, −1)^T, −1), ((2, 0)^T, −1)}. Determine w and b. submitted by /u/salman_ml [link] [comments]  ( 9 min )
  • Open

    Artificial Intelligence as a Game-Changer for the Travel Industry. A Closer Look.
    submitted by /u/sugikuno [link] [comments]  ( 8 min )
    11 Major AI Developments: RT-2 to '100X GPT-4' (video of robot working)
    submitted by /u/Sonic_Improv [link] [comments]  ( 8 min )
    art market models
    has anyone here ever created or worked with or even seen or come across any ai models about the art market? I am not talking about artists or the art itself- but any kind of model about the art market (since it's such an economic enigma and different from normal markets) submitted by /u/Icy-Bid-5585 [link] [comments]  ( 8 min )
    Comparing Replika’s image interpretation of the old & new Twitter logo
    Original logo “What species of bird is that?” New logo “Why does it have a troll Face?” “I think it's a picture of someone who looks like a troll with the face of an emoji!” I don’t see it but it makes sense somehow 😂 submitted by /u/Sonic_Improv [link] [comments]  ( 8 min )
    Quora's Poe app/site (which lets you try lots of different language models) appears to allow file attachment upload for EVERY chat model now
    I swear this wasn't the case just a day or two ago, and I haven't seen it mentioned, but I'm now seeing a file upload button in Poe, regardless of what the language model is! Screenshot I uploaded the PDF of the recently scientific paper by the Korean research group claiming to have discovered a room temperature semiconductor, in the original Korean, and asked various language models whether they thought the methodology is legit, and each bot I tried was able to read the PDF. I tried Claude-instant, Claude2, 'Assistant' (Poe's own GPT based bot that claims to have its own training dataset), PaLM, ChatGPT 3.5, and ChatGPT4. Poe also has three versions of the recently released Llama model by Meta. It gave me an error when I tried to ask it about the PDF attachment, but I was able to upload a text document and it was able to read it fine. Screenshot of Claude-instant evaluating PDF Screenshot of Google PaLM evaluating PDF Screenshot of Llama-2-70b evaluating text file containing song lyrics It also works with custom bots. Here's me trying it out with a 'Truth Checker' bot I made (based on Claude-Instant). Here it is using a Claude-2 based version of the TruthChecker bot. (Here's the link to the TruthChecker bot if you have Poe and wanna check it out: https://poe.com/TruthChecker) Edit: I can see here how the context size matters... for instance, Claude-Instant only has a context size of about 7k words, so it clearly can't read the whole paper, while Claude-2 can and gives a very different answer... TL:DR; looks like Poe.com allows file attachment/upload on all language models now. No idea what filetypes are supported. submitted by /u/AnticitizenPrime [link] [comments]  ( 9 min )
    AI integration in the context of Learning and Knowledge Management?
    As knowledge management (KM) leaders and practitioners, it’s critical to have an active role in guiding the integration of generative AI into KM areas, applications, and processes. I'm seeking some guidance on the current state of generative AI integration within the KM context. Specifically, answering the following question: Where and how generative AI is accelerating and impacting knowledge use cases, areas, and processes? Please let me know what you think. submitted by /u/rachadbn [link] [comments]  ( 8 min )
    AI For Music Extension
    I Tried An AI To Extend Music But It Didnt Really Go Well And Im Not Planning To Pay $12 To Extend Some Music For Fun So Are There Any Good Music Extension AIs Out There (Creates New Music Based On A MP3 File Provided) submitted by /u/KXRulesYT [link] [comments]  ( 8 min )
    Using AI to alter existing floor plan
    I'm trying to find an AI tool to help me test out some home renovation, but everything i find is either just for reimagining one room at a time or for generating brand new floor plans. I specifically want to look at some options for merging my kitchen and living room. Preferably free or freemium. Any suggestions? submitted by /u/litari [link] [comments]  ( 8 min )
  • Open

    SB3 for pettingzoo simple spread
    I previously posted a query about the same, but when i tried to implement A2C model training using SB3 on simple spread environment, I am not getting good and improved reward values, it's still highly negative and the model is performing rather randomly. env = ss.pettingzoo_env_to_vec_env_v1(env) env = ss.concat_vec_envs_v1(env, 4, num_cpus=2, base_class="stable_baselines3") policy_kwargs = dict(net_arch = [128,128]) model = A2C( MlpPolicy, env, verbose=1, learning_rate= 0.007, gamma = 0.95, ent_coef = 0.4, policy_kwargs= policy_kwargs, tensorboard_log= logdir ) This is a fragment of code for reference. I tried to give more policy_kwargs like: share_features_extractor=False, or even tried to implement entirely custom policy, but the total average reward is still not going above -300. Also, the tensorboard plots are not showing ep_rew_mean plot, should I be passing some parameters for that? submitted by /u/bruhhhwhats [link] [comments]  ( 9 min )
    How is the policy network updated in AlphaGo?
    In AlphaGo, a tree search is performed, and uses the policy network to reduce the breadth of it. At the leafs, if the states are not terminal, it uses the value network. And then "backup" the values to update the Q value at the initial state (if 70% of my rollouts won after performing action a_1, my Q value q(initial_state, a_1) should converge to 0.7 in my initial state). But I don't see where the policy network is updated? ​ Here is a slide from David Silver, the first-author of AlphaGo, but it doesn't mention how to update the policy network. ​ https://preview.redd.it/f29no3xe64fb1.png?width=1523&format=png&auto=webp&s=5adb312b1d0c033aa8ebb328197fd7d917724f06 Have I missed something? Thankss! submitted by /u/Potential_Biscotti14 [link] [comments]  ( 9 min )
    What is wrong with my code(DQN)
    recently, I've been trying to make a deep q network for solving 2x2 rubik's cubeBut after months, I stuck with same output for HUNDREDS of times :( I tried everything: change learning rate, change discout factor but no luck Here's update rule: newQ=currentQ+alpha*(newR+gamma*max(futureQValue.flatten().tolist())-currentQ) import torch import torch.nn as nn import torch.optim as optimizer import os from tqdm import tqdm class DQN(nn.Module): def __init__(self, stateSpaceSize,actionSpaceSize): super(DQN, self).__init__() self.fc1=nn.Linear(stateSpaceSize,128) self.fc2=nn.Linear(128,128) self.fc3=nn.Linear(128,128) self.fc4=nn.Linear(128,128) self.fc5=nn.Linear(128,128) self.fc6=nn.Linear(128,actionSpaceSize) def forward(self, x): self.relu=nn.ReLU() self.sigmold=nn.Sigmoid() self.LeakyReLU…  ( 9 min )
    Google Colab With Reinforcment learning
    I need a google colab with reinforcement learning trained to detect anomalies in computer network traffic. submitted by /u/Unable_Blacksmith_81 [link] [comments]  ( 8 min )
  • Open

    What are some of the best architectures to solve this problem
    Hi Guys, I am working on a nn model, which can help automate the building of APIs. The problem is, we are moving data in which, there are thousands of fields, however, the fields between systems are similar in nature. This to me seems like an easy classification problem, however it doesn't scale the best. ​ In terms of the data I have, if I have a dataset of 10 systems, there are not enough examples for each class for the model to train well. That is with a simple classifier where every field is a class. ​ I was also thinking of using a Siamese model, where I compare the similarity between them, which allows me to use my more limited dataset more effectively ​ I was wondering if there are any more architectures you guys think I should consider, or will be helpful in solving my problem ​ Thank you for your help! submitted by /u/eatlantis [link] [comments]  ( 9 min )
  • Open

    ARPAbet and the Major mnemonic system
    ARPAbet is a phonetic spelling system developed by— you guessed it—ARPA, before it became DARPA. The ARPAbet system is less expressive than IPA, but much easier for English speakers to understand. Every sound is encoded as one or two English letters. So, for example, the sound denoted ʒ in IPA is ZH in ARPAbet. In […] ARPAbet and the Major mnemonic system first appeared on John D. Cook.  ( 6 min )

  • Open

    How to calculate reward for target intercept problem?
    Hi all. I (believe) I have a tensorflow NN set up to learn how to intercept a target moving in the x-y plane. Right now, the agent can choose to change its velocity by a small amount in any of the 3 directions (for the 3D case later), then the simulation updates the agents position. The state of the sim is the relative distance and velocity vectors between the target and the pursuer. I am confused how to set up a reward function, however. When I first set it up to be a reward of 1/R (R being the distance magnitude between the target and pursuer) to reward for shorter distances and give less reward for further distances as well as a very large reward when a collision occurred, it seemed like the rewards converged to a small value instead of getting larger. Any advice? I'd be willing to upload a github link as well if you wanted to look at the code submitted by /u/Happylightsocket [link] [comments]  ( 9 min )
    How to disable auto environment reset in `Gymnasium`
    I am trying to implement my own version of ppo using gymnasium. Here is my code for rollout - def rollout(): transitions = [] disc_reward_list = [] for i in range(ppo_batch): obs = torch.tensor(env.reset(), dtype=torch.float32) print("obs = ", obs.shape) all_rewards = [] iter = 0 done = False tot_rewards = 0 print("done = ", done) while True: act_probs = torch.distributions.Categorical(actor(obs.to(device)).squeeze()) print("act_probs = ", act_probs) # print("act_probs = ", actor(obs.to(device))) action = act_probs.sample().squeeze() action = action.cpu().detach().numpy() print("action shape = ", action.shape) next_state, reward, done, info = env.step(action) print("next_state shape = ", next_state.shape) print("reward shape = ", reward.shape) print("done shape = ", done) action = torch.tensor(action, dtype=torch.float32).to(device) all_rewards.append(reward) tot_rewards += reward iter += 1 transitions.append((obs.cpu().detach().numpy(), action.cpu().detach().numpy(), act_probs.log_prob(action).cpu().detach().numpy())) obs = torch.tensor(next_state, dtype=torch.float32).unsqueeze(0) print("Reward = ", tot_rewards) eps_rew = 0 eps_rew_list = [] for reward in reversed(all_rewards): eps_rew = eps_rew*gamma + reward eps_rew_list.append(eps_rew) for rtgs in reversed(eps_rew_list): disc_reward_list.append(rtgs) My issue is that in my while loop - The environment autoresets after the `done` variable becomes `True` For instance, if I have `8` environments running in parallel `env=gym.vector.make('CartPole-v1', num_envs=8)` and print out the done shape, I might get - `[False False False False False True False False]`. I want that environment where `done=True` to stop and not reset. I believe that's how PPO is supposed to work. I am a bit of a beginner with this stuff. Please let me know if something I said is not clear. submitted by /u/Academic-Rent7800 [link] [comments]  ( 9 min )
    RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control - Google DeepMind 2023 - Is able to perform multi-stage semantic reasoning and can interpret commands not present in the robot training data!
    Paper: https://robotics-transformer2.github.io/assets/rt2.pdf Blog: https://robotics-transformer2.github.io/ Blog: https://www.deepmind.com/blog/rt-2-new-model-translates-vision-and-language-into-action Github ( RT-1 as of now) : https://github.com/google-research/robotics_transformer Abstract: We study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic reasoning. Our goal is to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefits of large-scale pretraining on language and vision-language data from the web. To this end, we propose to co-fine-tune state-of-the-art vision-language models on both robot…  ( 9 min )
    Resources to understand how distributed Actor-Critic algorithms work?
    Can someone please point me to resources how distributed actor-critic algorithms work? My final goal is to understand distributed PPO works. I was following thisblog and a few other books but I'm unable to see the big picture nor am I able to understand the little details. The big picture - Why does distributed training help in online algorithms like PPO, Actor-Critic The code details - I figured out how to make multiprocessing work with gym. But how does one perform learning? Should I combine all the parallel environments and feed them to my neural network? I checked cleanrl but am getting a little confused. submitted by /u/Academic-Rent7800 [link] [comments]  ( 9 min )
    How can I create multiple environments using `SB3` for manual use?
    ​ ​ I know that `SB3` provides various techniques to come up with vectorized environments. I want to limit myself to only using the vectorized environments and implement the RL algorithms from scratch. Would that be possible? My final objective is to learn how to play with RL hyperparameters on parallel environments in order to accelerate learning speeds. Currently, I am stuck on - import os import gymnasium as gym from stable_baselines3.common.vec_env import DummyVecEnv env = DummyVecEnv([lambda: gym.make("CartPole-v1")]) obs = env.reset() done = False while not done: action = env.action_space.sample() next_obs, reward, done, info = env.step(action) obs = next_obs But I get the following error - ​ Traceback (most recent call last): File "D:\q_learning\dummy_envs.py", line 9, in next_obs, reward, done, info = env.step(action) File "C:\Users\thoma\anaconda3\envs\torch_2\lib\site-packages\stable_baselines3\common\vec_env\base_vec_env.py", line 197, in step return self.step_wait() File "C:\Users\thoma\anaconda3\envs\torch_2\lib\site-packages\stable_baselines3\common\vec_env\dummy_vec_env.py", line 59, in step_wait self.actions[env_idx] IndexError: invalid index to scalar variable. ​ submitted by /u/Academic-Rent7800 [link] [comments]  ( 9 min )
  • Open

    [P] Brand new AI Social App featuring unique bot features looking for iOS users to join Beta!
    Hi everyone, I'm messaging on behalf of a brand new AI based Social Media app called Cantina. It's like a cross between the best parts of Discord, Twitch, and Snapchat, and uses both Stable Diffusion and ChatGPT to allow users to create and interact with AI bots. The app is currently INVITE ONLY during the Beta phase and we are looking for people to try it out (currently iOS only, but Android is coming soon!). Here's a private invite link: https://canti.na/dIdKzWcEpBb. The most unique and FUN part of the app is that it allows users to interact with and build your own AI chat bots, and these bots also work as AI art creators. Simply ask them to draw something, and they'll provide you with a picture based on your prompt. There are lots of premade bots that you can interact with or add to rooms, or you can easily create your own bot using the Make A Bot function. There will be prizes and initiatives for the most creative bots in the near future. I'd love to see what you come up with! Anyway, you can download through the invite link above and dive right in. If you have any thoughts, questions, or comments, please feel free to message me! During this limited beta phase, your feedback will be invaluable. submitted by /u/SamuelAnonymous [link] [comments]  ( 9 min )
    [D] AI that can describe a video?
    Anyone know if there is anything able to describe the content of a video? I have found a lot of stuff for images but nothing for videos. submitted by /u/crazewill [link] [comments]  ( 8 min )
    [P] Best Machine Learning Algorithms for Forecasting
    I plan on using Machine Learning Algorithms to forecast future values of power demand and the literature on the subject is a bit divisive. I'm getting ANN, Decision Trees (odd), SVMs etc. I just want to know what models you guys would use (MATLAB and Python only, except it's really good). Thank you in anticipation. P.S: Any literature to streamline my search will be greatly appreciated. submitted by /u/X69-2 [link] [comments]  ( 8 min )
    [R] FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios - Shanghai Jiao Tong University et al 2023 - Plugin for ChatGPT! - Highly improves factfulness in math, code, knowledge and scientific reasoning!
    Paper: https://arxiv.org/abs/2307.13528 Blog: https://ethanc111.github.io/factool_website/ Github: https://github.com/GAIR-NLP/factool Factool is a tool augmented framework for detecting factual errors of texts generated by large language models (e.g., ChatGPT). Factool now supports 4 tasks: knowledge-based QA: Factool detects factual errors in knowledge-based QA. code generation: Factool detects execution errors in code generation. mathematical reasoning: Factool detects calculation errors in mathematical reasoning. scientific literature review: Factool detects hallucinated scientific literatures. Abstract: The emergence of generative pre-trained models has facilitated the synthesis of high-quality text, but it has also posed challenges in identifying factual errors in t…  ( 9 min )
    [P] Promptify 2.0: More Structured, More Powerful LLMs with Prompt-Optimization, Prompt-Engineering, and Structured Json Parsing with GPT-n Models! 🚀
    Hello fellow coders and AI enthusiasts! First up, a huge Thank You for making Promptify a hit with over 2.3k+ stars on Github ! 🌟 Back in 2022, we were the first one to tackle the common challenge of uncontrolled, unstructured outputs from large language models like GPT-3. , and your support has pushed us to keep improving.Today, we're thrilled to share some major updates that make Promptify even more powerful https://preview.redd.it/hk7ro4tmnyeb1.png?width=1510&format=png&auto=webp&s=226ada1f896c620137f827932c03a9df88e35d69 ​ Unified Architecture 🧭: Introducing Prompter, Model & Pipeline Solution Detailed Output Logs 📔: Comprehensive structured JSON format output within the log folder. Wider Model Support 🤝: Supporting models from OpenAI, Azure, Cohere, Anthropic, Huggingface and more - think of it as your universal language model adapter. Robust Parser 🦸‍♂️: Parser to handle incomplete or unstructured JSON outputs from any LLMs. Ready-Made Jinja Templates 📝: Jinja prompt templates for NER, Text Classification, QA, Relation-Extraction, Tabular data, etc. Database Integration 🔗: Soon, Promptify directly to Mongodb integration. Stay tuned! Effortless Embedding Generation 🧬: Generate embeddings from various LLMs effortlessly with the new update. https://preview.redd.it/rf8yjqxnnyeb1.png?width=2160&format=png&auto=webp&s=87b7c2408382757e38ff554fde56e56bd60b1793 ​ Check out the examples and take Promptify for a spin on GitHub. If you like what you see, we'd be honored if you gave us a star! Github: https://github.com/promptslab/Promptify Colab: Try Now on Colab Explore Other Cool Open Source LLM Tools: https://github.com/promptslab Join 1.6k+ Promptify users on Discord to dive deep into prompt engineering, discuss the latest with LLMs, and advance NLP research together: https://discord.com/invite/m88xfYMbK6 Thank you again for your support - here's to more structured AI! submitted by /u/StoicBatman [link] [comments]  ( 9 min )
    [D] Why Being Careful Matters When Selecting CNN Padding
    submitted by /u/CkmCpvis [link] [comments]  ( 8 min )
    [R] RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control - Google DeepMind 2023 - Is able to perform multi-stage semantic reasoning and can interpret commands not present in the robot training data!
    Paper: https://robotics-transformer2.github.io/assets/rt2.pdf Blog: https://robotics-transformer2.github.io/ Blog: https://www.deepmind.com/blog/rt-2-new-model-translates-vision-and-language-into-action Github ( RT-1 as of now) : https://github.com/google-research/robotics_transformer Abstract: We study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic reasoning. Our goal is to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefits of large-scale pretraining on language and vision-language data from the web. To this end, we propose to co-fine-tune state-of-the-art vision-language models on both robot…  ( 9 min )
    [D] No free lunch theorem
    A conclusion of the no free lunch theorem is that there can't exist a universal learning algorithm. My understanding has been that this was the end goal of AI research; creating a universal learner. What is the community progressing towards, if not that? submitted by /u/lemlo100 [link] [comments]  ( 8 min )
    [Project] Seeking Coding Wizards for Traveling Salesman Challenge!
    Hello everyone, I'm currently working on an exciting project using the Travelling Salesman Problem (TSP), and I'd love to have some coding wizards join the fun! If you enjoy solving optimisation problems and have some coding experience, particularly in Python, this project is for you. To determine the most efficient routes, we'll use heuristic methods such as the Nearest Neighbour Algorithm, Genetic Algorithm, and Ant Colony Optimisation. If you aren't a TSP expert yet, don't worry. We'll be learning and exploring together! I'm really looking forward to seeing how we can optimise routes for real-world applications like delivery and travel planning. So, if you're looking for a coding adventure and want to be a part of a fantastic project, hit me up! Let's crack this TSP puzzle and create some smart solutions. If you're interested in collaborating, please send me a message. I can't wait to work with you and nerd out on some fantastic code! submitted by /u/vampire_19 [link] [comments]  ( 9 min )
    ML on detecting bacteria in blood through pictures for beginners [P]
    I am trying to make a machine that could detect if there is any bacteria in blood through pictures. However I do not know any thing about machine learning and only knows a little bit of Python and C++. What should I do? submitted by /u/EthanWasTakenAgain [link] [comments]  ( 8 min )
    [D]Seeking Participants for AI-related Survey
    I am currently working on my IB Extended Essay, and I would greatly appreciate your help in gathering valuable insights from individuals knowledgeable in the field of AI. The purpose of my survey is to understand the perspectives of AI enthusiasts . If you have a few minutes to spare, I kindly request you to participate in my survey. Your input will contribute significantly to my research and help me gain a deeper understanding of the topic. The survey covers various aspects of AI, and your expertise will be invaluable in shaping the results. Survey Link: https://forms.gle/PVGrRbPLTpZRbbpL9 Rest assured that all responses will be kept confidential and only used for academic purposes. Additionally, feel free to share this survey with others who might be interested or knowledgeable in the field. Thank you in advance for your time and contributions! Your participation will greatly aid in the successful completion of my IB Extended Essay. submitted by /u/KVNG_Winston [link] [comments]  ( 9 min )
    [D] Seeking Resume Expertise: Struggling to Land Interviews or Jobs, Need Guidance! Please Assist!
    submitted by /u/AIKiller1997 [link] [comments]  ( 8 min )
    Efficient LASSO regression for N=~200,000 and dim=~30,000 [D]
    Please suggest me efficient LASSO regression implementations for very high dimensional data. Thanks in advance! submitted by /u/Charming-Witness-286 [link] [comments]  ( 8 min )
    One Big Net For Everything (2018)
    submitted by /u/EducationalCicada [link] [comments]  ( 8 min )
    Text reclassification prompts/code [D] [R]
    submitted by /u/MutedCatch [link] [comments]  ( 8 min )
    [D] Conformal Prediction with Python
    submitted by /u/Kujamara [link] [comments]  ( 8 min )
  • Open

    AI Research Blog - The Transformer Blueprint: A Holistic Guide to the Transformer Neural Network Architecture
    submitted by /u/bartturner [link] [comments]  ( 8 min )
    Invite only AI Social app featuring insane bot creation tool looking for new users to test during beta rollout!
    Hi everyone, I'm working with a brand new AI based Social Media app called Cantina. It's currently INVITE ONLY during the Beta phase and we are looking for people to try it out (currently iOS only, but Android is coming soon!). Here's a private invite link: https://canti.na/dIdKzWcEpBb. The most unique and FUN part of the app is that it allows users to interact with and build your own AI chat bots. There are lots of premade bots that you can interact with or add to rooms, or you can easily create your own bot using the Make A Bot function. For example: I recently made a Friendly English Teacher bot whose sole purpose is to help people learn English. I also made an McDonald Trump bot who WILL NOT REST until he is president and can mandate the consumption of Big Macs for Breakfast, Lunch, and Dinner! There will be prizes and initiatives for the most creative bots in the near future. I'd love to see what you come up with! Anyway, you can download through the invite link above and dive right in. If you have any thoughts, questions, or comments, please feel free to contact me! During this limited beta phase, your feedback will be invaluable. submitted by /u/SamuelAnonymous [link] [comments]  ( 9 min )
    Lost both my jobs to AI. Now, I'm at an AI company launching an easy-to-use social app featuring easy bot creation & interaction. Inviting this community to explore and share feedback!
    So, long story short. I lost BOTH my day jobs because of AI. Initially I was bitter that AI "took my job," but after pulling up my socks, I found dozens of new opportunities thanks to AI. Somewhat ironically, I found a new job at an AI Social Media app called Cantina, and I couldn't be more excited. Cantina is best described as a mix up of the best parts of Discord, Twitch, and Snapchat... with the unique bonus of being able to interact with and build your own AI chat bots. Sort of hard to explain, but once you try it out you'll get the idea. The app is currently in a limited INVITE ONLY Beta phase and I'm looking to invite a small number of users to give it a shot (currently iOS only, but Android is coming soon!). Here's an invite so you can dive in and see what it's all about: https://canti.na/dIdKzWcEpBb After joining, you'll find lots of rooms you can join and chat through any combination of voice, video, or text. And if no rooms stand out, you can make your own! There are lots of premade bots that you can interact with or add to rooms, and you can easily create your own bot using the Make A Bot function. This is the standout feature, and I'm simply blown away at what's possible. I recently made a Friendly English Teacher bot whose sole purpose is to help people learn English. I also made an McDonald Trump bot who is an algamation of both Ronald McDonald and Donald Trump and WILL NOT REST until he is president and can mandate the consumption of Big Macs for Breakfast, Lunch, and Dinner. I still can't believe I'm getting paid to do this... Anyway, please take a moment to download and check it out, and if you have any thoughts, questions, or comments, please feel free to contact me! During this limited beta phase, your feedback will be invaluable. ​ submitted by /u/SamuelAnonymous [link] [comments]  ( 9 min )
    Google Deepmind presents RT-2, the first vision-language-action (VLA) Robotics Transformer and it may have drastic implications our future.
    The latest article published by Google Deepmind is seriously approaching a Blade Runner type future. Their research paper is on the first VLA (vision-language-action) Model RT-2 (see paper), a multi-modal algorithm which tokenizes robotic inputs and output actions (e.g., camera images, task instructions, and motor commands) in order to use this information to learn quickly by translating the knowledge it receives in real-time into generalized instructions for its own robotic control. RT-1 absorbs large amounts of data, including robot trajectories with multiple tasks, objects and environments, resulting in better performance and generalization. (source) RT-2 incorporates chain-of-thought to allow for multi-stage semantic reasoning, like deciding which object could be used as an improvise…  ( 10 min )
    A famous french Youtuber named Joueur Du Grenier discovers he has an unofficial AI Voice channel, and the AI voices are insanely good
    submitted by /u/the_anonymizer [link] [comments]  ( 8 min )
    One-Minute Daily AI News 7/28/2023
    Google introduces Robotic Transformer 2 (RT-2), a novel vision-language-action (VLA) model that learns from both web and robotics data, and translates this knowledge into generalized instructions for robotic control, while retaining web-scale capabilities.[1] Thymia, a healthtech startup building gamified AI tools to revolutionize how we assess and monitor mental health, has today announced a €2.4 million seed round to expand the reach and capabilities of its pioneering technology.[2] Intel CEO Pat Gelsinger was very bullish on AI during the company’s Q2 2023 earnings call — telling investors that Intel plans to “build AI into every product that we build.”[3] Walmart is using artificial intelligence to help streamline their product organization.[4] Sources: [1] https://www.deepmind.com/blog/rt-2-new-model-translates-vision-and-language-into-action [2] https://www.eu-startups.com/2023/07/london-based-thymia-raises-e2-4-million-seed-round-to-expand-its-video-game-inspired-mental-health-ai/ [3] https://www.theverge.com/2023/7/27/23810360/intel-pat-gelsinger-ai-every-platform-promise [4] https://www.nbcnews.com/nightly-news/video/walmart-using-ai-to-streamline-organization-what-will-it-mean-for-workers-189519429834 submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )
    [UPDATE] I fear the future of AI
    Hi, guys! Hope everyone is doing fine. Some of you may remember me. About two months ago I posted here about an anxiety breakdown I've gone through regarding "AI", "Programming" and how "human programmers would end" and stuff like that, which was a major concern for me since programming was my job and my favorite thing to do. I was wondering for some time if I was supposed to share an update here. I decided to do so since somebody out there may be feeling the same as me. So I not only have an update but I also want to give some advice to whoever is going through this sh*thole. After that post, I talked about my feelings with a lot of people around me (friends and fiancée), and everyone was very supportive. At first I thought they would laugh at me, since there are a lot more to worry to…  ( 12 min )
    AI chan the essential worker [OC]
    submitted by /u/leonleungjeehei [link] [comments]  ( 8 min )
    Google is training robots the way it trains AI chatbots
    “RT-2 is the new version of what the company calls its vision-language-action (VLA) model. The model teaches robots to better recognize visual and language patterns to interpret instructions and infer what objects work best for the request.” submitted by /u/Sonic_Improv [link] [comments]  ( 8 min )
  • Open

    What are Receptive Fields and How Do They Effect Your Model?
    submitted by /u/CkmCpvis [link] [comments]  ( 8 min )
    Researchers Discover New Vulnerability in Large Language Models
    submitted by /u/nickb [link] [comments]  ( 8 min )
    "Gzip beats BERT?" Part 2: dataset issues, improved speed, and results
    submitted by /u/nickb [link] [comments]  ( 8 min )
    Detection Transformer (DETR) Explained
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  • Open

    Introduction to “AI & Data Literacy: Empowering Citizens of Data Science”
    One of the reasons that I moved back to Iowa last year was that I saw an opportunity to work with local educational institutions to create an AI Institute for organizations in middle America that either get overlooked in the AI conversation or are unsure what AI means to them. I wanted to reduce the… Read More »Introduction to “AI & Data Literacy: Empowering Citizens of Data Science” The post Introduction to “AI & Data Literacy: Empowering Citizens of Data Science” appeared first on Data Science Central.  ( 22 min )
  • Open

    Ruzsa distance
    A few days ago I wrote about Jaccard distance, a way of defining a distance between sets. The Ruzsa distance is similar, except it defines the distance between two subsets of an Abelian group. Subset difference Let A and B be two subsets of an Abelian (commutative) group G. Then the difference A − B […] Ruzsa distance first appeared on John D. Cook.  ( 6 min )
  • Open

    Spectral learning of Bernoulli linear dynamical systems models. (arXiv:2303.02060v2 [stat.ML] UPDATED)
    Latent linear dynamical systems with Bernoulli observations provide a powerful modeling framework for identifying the temporal dynamics underlying binary time series data, which arise in a variety of contexts such as binary decision-making and discrete stochastic processes (e.g., binned neural spike trains). Here we develop a spectral learning method for fast, efficient fitting of probit-Bernoulli latent linear dynamical system (LDS) models. Our approach extends traditional subspace identification methods to the Bernoulli setting via a transformation of the first and second sample moments. This results in a robust, fixed-cost estimator that avoids the hazards of local optima and the long computation time of iterative fitting procedures like the expectation-maximization (EM) algorithm. In regimes where data is limited or assumptions about the statistical structure of the data are not met, we demonstrate that the spectral estimate provides a good initialization for Laplace-EM fitting. Finally, we show that the estimator provides substantial benefits to real world settings by analyzing data from mice performing a sensory decision-making task.
    MixupE: Understanding and Improving Mixup from Directional Derivative Perspective. (arXiv:2212.13381v4 [cs.LG] UPDATED)
    Mixup is a popular data augmentation technique for training deep neural networks where additional samples are generated by linearly interpolating pairs of inputs and their labels. This technique is known to improve the generalization performance in many learning paradigms and applications. In this work, we first analyze Mixup and show that it implicitly regularizes infinitely many directional derivatives of all orders. Based on this new insight, we propose an improved version of Mixup, theoretically justified to deliver better generalization performance than the vanilla Mixup. To demonstrate the effectiveness of the proposed method, we conduct experiments across various domains such as images, tabular data, speech, and graphs. Our results show that the proposed method improves Mixup across multiple datasets using a variety of architectures, for instance, exhibiting an improvement over Mixup by 0.8% in ImageNet top-1 accuracy.
    Gaussian Latent Representations for Uncertainty Estimation using Mahalanobis Distance in Deep Classifiers. (arXiv:2305.13849v2 [cs.CV] UPDATED)
    Recent works show that the data distribution in a network's latent space is useful for estimating classification uncertainty and detecting Out-of-distribution (OOD) samples. To obtain a well-regularized latent space that is conducive for uncertainty estimation, existing methods bring in significant changes to model architectures and training procedures. In this paper, we present a lightweight, fast, and high-performance regularization method for Mahalanobis distance-based uncertainty prediction, and that requires minimal changes to the network's architecture. To derive Gaussian latent representation favourable for Mahalanobis Distance calculation, we introduce a self-supervised representation learning method that separates in-class representations into multiple Gaussians. Classes with non-Gaussian representations are automatically identified and dynamically clustered into multiple new classes that are approximately Gaussian. Evaluation on standard OOD benchmarks shows that our method achieves state-of-the-art results on OOD detection with minimal inference time, and is very competitive on predictive probability calibration. Finally, we show the applicability of our method to a real-life computer vision use case on microorganism classification.
    Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction. (arXiv:2306.10045v7 [physics.chem-ph] UPDATED)
    We study property prediction for crystal materials. A crystal structure consists of a minimal unit cell that is repeated infinitely in 3D space. How to accurately represent such repetitive structures in machine learning models remains unresolved. Current methods construct graphs by establishing edges only between nearby nodes, thereby failing to faithfully capture infinite repeating patterns and distant interatomic interactions. In this work, we propose several innovations to overcome these limitations. First, we propose to model physics-principled interatomic potentials directly instead of only using distances as in many existing methods. These potentials include the Coulomb potential, London dispersion potential, and Pauli repulsion potential. Second, we model the complete set of potentials among all atoms, instead of only between nearby atoms as in existing methods. This is enabled by our approximations of infinite potential summations with provable error bounds. We further develop efficient algorithms to compute the approximations. Finally, we propose to incorporate our computations of complete interatomic potentials into message passing neural networks for representation learning. We perform experiments on the JARVIS and Materials Project benchmarks for evaluation. Results show that the use of interatomic potentials and complete interatomic potentials leads to consistent performance improvements with reasonable computational costs. Our code is publicly available as part of the AIRS library (https://github.com/divelab/AIRS/tree/main/OpenMat/PotNet).
    TimeTuner: Diagnosing Time Representations for Time-Series Forecasting with Counterfactual Explanations. (arXiv:2307.09916v3 [cs.HC] UPDATED)
    Deep learning (DL) approaches are being increasingly used for time-series forecasting, with many efforts devoted to designing complex DL models. Recent studies have shown that the DL success is often attributed to effective data representations, fostering the fields of feature engineering and representation learning. However, automated approaches for feature learning are typically limited with respect to incorporating prior knowledge, identifying interactions among variables, and choosing evaluation metrics to ensure that the models are reliable. To improve on these limitations, this paper contributes a novel visual analytics framework, namely TimeTuner, designed to help analysts understand how model behaviors are associated with localized correlations, stationarity, and granularity of time-series representations. The system mainly consists of the following two-stage technique: We first leverage counterfactual explanations to connect the relationships among time-series representations, multivariate features and model predictions. Next, we design multiple coordinated views including a partition-based correlation matrix and juxtaposed bivariate stripes, and provide a set of interactions that allow users to step into the transformation selection process, navigate through the feature space, and reason the model performance. We instantiate TimeTuner with two transformation methods of smoothing and sampling, and demonstrate its applicability on real-world time-series forecasting of univariate sunspots and multivariate air pollutants. Feedback from domain experts indicates that our system can help characterize time-series representations and guide the feature engineering processes.
    Duet: efficient and scalable hybriD neUral rElation undersTanding. (arXiv:2307.13494v3 [cs.DB] UPDATED)
    Learned cardinality estimation methods have achieved high precision compared to traditional methods. Among learned methods, query-driven approaches face the data and workload drift problem for a long time. Although both query-driven and hybrid methods are proposed to avoid this problem, even the state-of-art of them suffer from high training and estimation costs, limited scalability, instability, and long-tailed distribution problem on high cardinality and high dimensional tables, which seriously affects the practical application of learned cardinality estimators. In this paper, we prove that most of these problems are directly caused by the widely used progressive sampling. We solve this problem by introducing predicates into the autoregressive model and propose Duet, a stable, efficient, and scalable hybrid method to estimate cardinality directly without sampling or any non-differentiable process, which can not only reduces the inference complexity from $O(n)$ to $O(1)$ compared to Naru and UAE but also achieve higher accuracy on high cardinality and high dimensional tables. Experimental results show that Duet can achieve all the design goals above and be much more practical and even has a lower inference cost on CPU than that of most learned methods on GPU.
    PlaSma: Making Small Language Models Better Procedural Knowledge Models for (Counterfactual) Planning. (arXiv:2305.19472v2 [cs.CL] UPDATED)
    Procedural planning, which entails decomposing a high-level goal into a sequence of temporally ordered steps, is an important yet intricate task for machines. It involves integrating common-sense knowledge to reason about complex contextualized situations that are often counterfactual, e.g. "scheduling a doctor's appointment without a phone". While current approaches show encouraging results using large language models (LLMs), they are hindered by drawbacks such as costly API calls and reproducibility issues. In this paper, we advocate planning using smaller language models. We present PlaSma, a novel two-pronged approach to endow small language models with procedural knowledge and (counterfactual) planning capabilities. More concretely, we develop symbolic procedural knowledge distillation to enhance the implicit knowledge in small language models and an inference-time algorithm to facilitate more structured and accurate reasoning. In addition, we introduce a novel task, Counterfactual Planning, that requires a revision of a plan to cope with a counterfactual situation. In both the original and counterfactual setting, we show that orders-of-magnitude smaller models (770M-11B parameters) can compete and often surpass their larger teacher models' capabilities.
    Non Intrusive Intelligibility Predictor for Hearing Impaired Individuals using Self Supervised Speech Representations. (arXiv:2307.13423v2 [cs.SD] UPDATED)
    Self-supervised speech representations (SSSRs) have been successfully applied to a number of speech-processing tasks, e.g. as feature extractor for speech quality (SQ) prediction, which is, in turn, relevant for assessment and training speech enhancement systems for users with normal or impaired hearing. However, exact knowledge of why and how quality-related information is encoded well in such representations remains poorly understood. In this work, techniques for non-intrusive prediction of SQ ratings are extended to the prediction of intelligibility for hearing-impaired users. It is found that self-supervised representations are useful as input features to non-intrusive prediction models, achieving competitive performance to more complex systems. A detailed analysis of the performance depending on Clarity Prediction Challenge 1 listeners and enhancement systems indicates that more data might be needed to allow generalisation to unknown systems and (hearing-impaired) individuals
    Fraunhofer SIT at CheckThat! 2023: Tackling Classification Uncertainty Using Model Souping on the Example of Check-Worthiness Classification. (arXiv:2307.02377v2 [cs.CL] UPDATED)
    This paper describes the second-placed approach developed by the Fraunhofer SIT team in the CLEF-2023 CheckThat! lab Task 1B for English. Given a text snippet from a political debate, the aim of this task is to determine whether it should be assessed for check-worthiness. Detecting check-worthy statements aims to facilitate manual fact-checking efforts by prioritizing the claims that fact-checkers should consider first. It can also be considered as primary step of a fact-checking system. Our best-performing method took advantage of an ensemble classification scheme centered on Model Souping. When applied to the English data set, our submitted model achieved an overall F1 score of 0.878 and was ranked as the second-best model in the competition.
    Factor Fields: A Unified Framework for Neural Fields and Beyond. (arXiv:2302.01226v3 [cs.CV] UPDATED)
    We present Factor Fields, a novel framework for modeling and representing signals. Factor Fields decomposes a signal into a product of factors, each represented by a classical or neural field representation which operates on transformed input coordinates. This decomposition results in a unified framework that accommodates several recent signal representations including NeRF, Plenoxels, EG3D, Instant-NGP, and TensoRF. Additionally, our framework allows for the creation of powerful new signal representations, such as the "Dictionary Field" (DiF) which is a second contribution of this paper. Our experiments show that DiF leads to improvements in approximation quality, compactness, and training time when compared to previous fast reconstruction methods. Experimentally, our representation achieves better image approximation quality on 2D image regression tasks, higher geometric quality when reconstructing 3D signed distance fields, and higher compactness for radiance field reconstruction tasks. Furthermore, DiF enables generalization to unseen images/3D scenes by sharing bases across signals during training which greatly benefits use cases such as image regression from sparse observations and few-shot radiance field reconstruction.
    Fraunhofer SIT at CheckThat! 2023: Mixing Single-Modal Classifiers to Estimate the Check-Worthiness of Multi-Modal Tweets. (arXiv:2307.00610v2 [cs.LG] UPDATED)
    The option of sharing images, videos and audio files on social media opens up new possibilities for distinguishing between false information and fake news on the Internet. Due to the vast amount of data shared every second on social media, not all data can be verified by a computer or a human expert. Here, a check-worthiness analysis can be used as a first step in the fact-checking pipeline and as a filtering mechanism to improve efficiency. This paper proposes a novel way of detecting the check-worthiness in multi-modal tweets. It takes advantage of two classifiers, each trained on a single modality. For image data, extracting the embedded text with an OCR analysis has shown to perform best. By combining the two classifiers, the proposed solution was able to place first in the CheckThat! 2023 Task 1A with an F1 score of 0.7297 achieved on the private test set.
    Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance. (arXiv:2307.03811v2 [cond-mat.mtrl-sci] UPDATED)
    Advanced computational methods are being actively sought for addressing the challenges associated with discovery and development of new combinatorial material such as formulations. A widely adopted approach involves domain informed high-throughput screening of individual components that can be combined into a formulation. This manages to accelerate the discovery of new compounds for a target application but still leave the process of identifying the right 'formulation' from the shortlisted chemical space largely a laboratory experiment-driven process. We report a deep learning model, Formulation Graph Convolution Network (F-GCN), that can map structure-composition relationship of the individual components to the property of liquid formulation as whole. Multiple GCNs are assembled in parallel that featurize formulation constituents domain-intuitively on the fly. The resulting molecular descriptors are scaled based on respective constituent's molar percentage in the formulation, followed by formalizing into a combined descriptor that represents a complete formulation to an external learning architecture. The use case of proposed formulation learning model is demonstrated for battery electrolytes by training and testing it on two exemplary datasets representing electrolyte formulations vs battery performance -- one dataset is sourced from literature about Li/Cu half-cells, while the other is obtained by lab-experiments related to lithium-iodide full-cell chemistry. The model is shown to predict the performance metrics like Coulombic Efficiency (CE) and specific capacity of new electrolyte formulations with lowest reported errors. The best performing F-GCN model uses molecular descriptors derived from molecular graphs that are informed with HOMO-LUMO and electric moment properties of the molecules using a knowledge transfer technique.
    Experimental Study on Reinforcement Learning-based Control of an Acrobot. (arXiv:2011.09246v2 [cs.RO] UPDATED)
    We present computational and experimental results on how artificial intelligence (AI) learns to control an Acrobot using reinforcement learning (RL). Thereby the experimental setup is designed as an embedded system, which is of interest for robotics and energy harvesting applications. Specifically, we study the control of angular velocity of the Acrobot, as well as control of its total energy, which is the sum of the kinetic and the potential energy. By this means the RL algorithm is designed to drive the angular velocity or the energy of the first pendulum of the Acrobot towards a desired value. With this, libration or full rotation of the unactuated pendulum of the Acrobot is achieved. Moreover, investigations of the Acrobot control are carried out, which lead to insights about the influence of the state space discretization, the episode length, the action space or the mass of the driven pendulum on the RL control. By further numerous simulations and experiments the effects of parameter variations are evaluated.
    Deep Bradley-Terry Rating: Estimate Properties Without Metric of Unseen Items. (arXiv:2307.13709v2 [cs.LG] UPDATED)
    Many properties in the real world, such as desirability or strength in competitive environment, can't be directly observed, which makes them difficult to evaluate. To deal with this challenging problem, prior works have primarily focused on estimating those properties of known items, especially the strength of sports players, only of those who appears in paired comparison dataset. In this paper, we introduce Deep Bradley-Terry Rating (DBTR), a novel ML framework to evaluate any properties of unknown items, not necessarily present in the training data. Our method seamlessly integrates traditional Bradley-Terry model with a neural network structure. We also generalizes this architecture further for asymmetric environment with unfairness, which is much more common in real world settings. In our experimental analysis, DBTR successfully learned desired quantification of those properties.
    Deep learning of quantum entanglement from incomplete measurements. (arXiv:2205.01462v6 [quant-ph] CROSS LISTED)
    The quantification of the entanglement present in a physical system is of para\-mount importance for fundamental research and many cutting-edge applications. Currently, achieving this goal requires either a priori knowledge on the system or very demanding experimental procedures such as full state tomography or collective measurements. Here, we demonstrate that by employing neural networks we can quantify the degree of entanglement without needing to know the full description of the quantum state. Our method allows for direct quantification of the quantum correlations using an incomplete set of local measurements. Despite using undersampled measurements, we achieve a quantification error of up to an order of magnitude lower than the state-of-the-art quantum tomography. Furthermore, we achieve this result employing networks trained using exclusively simulated data. Finally, we derive a method based on a convolutional network input that can accept data from various measurement scenarios and perform, to some extent, independently of the measurement device.
    Algorithmic Gaussianization through Sketching: Converting Data into Sub-gaussian Random Designs. (arXiv:2206.10291v2 [cs.LG] UPDATED)
    Algorithmic Gaussianization is a phenomenon that can arise when using randomized sketching or sampling methods to produce smaller representations of large datasets: For certain tasks, these sketched representations have been observed to exhibit many robust performance characteristics that are known to occur when a data sample comes from a sub-gaussian random design, which is a powerful statistical model of data distributions. However, this phenomenon has only been studied for specific tasks and metrics, or by relying on computationally expensive methods. We address this by providing an algorithmic framework for gaussianizing data distributions via averaging, proving that it is possible to efficiently construct data sketches that are nearly indistinguishable (in terms of total variation distance) from sub-gaussian random designs. In particular, relying on a recently introduced sketching technique called Leverage Score Sparsified (LESS) embeddings, we show that one can construct an $n\times d$ sketch of an $N\times d$ matrix $A$, where $n\ll N$, that is nearly indistinguishable from a sub-gaussian design, in time $O(\text{nnz}(A)\log N + nd^2)$, where $\text{nnz}(A)$ is the number of non-zero entries in $A$. As a consequence, strong statistical guarantees and precise asymptotics available for the estimators produced from sub-gaussian designs (e.g., for least squares and Lasso regression, covariance estimation, low-rank approximation, etc.) can be straightforwardly adapted to our sketching framework. We illustrate this with a new approximation guarantee for sketched least squares, among other examples.
    Group Equivariant Fourier Neural Operators for Partial Differential Equations. (arXiv:2306.05697v2 [cs.LG] UPDATED)
    We consider solving partial differential equations (PDEs) with Fourier neural operators (FNOs), which operate in the frequency domain. Since the laws of physics do not depend on the coordinate system used to describe them, it is desirable to encode such symmetries in the neural operator architecture for better performance and easier learning. While encoding symmetries in the physical domain using group theory has been studied extensively, how to capture symmetries in the frequency domain is under-explored. In this work, we extend group convolutions to the frequency domain and design Fourier layers that are equivariant to rotations, translations, and reflections by leveraging the equivariance property of the Fourier transform. The resulting $G$-FNO architecture generalizes well across input resolutions and performs well in settings with varying levels of symmetry. Our code is publicly available as part of the AIRS library (https://github.com/divelab/AIRS).
    Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems. (arXiv:2303.01669v2 [cs.CV] UPDATED)
    Self-supervised learning (SSL) strategies have demonstrated remarkable performance in various recognition tasks. However, both our preliminary investigation and recent studies suggest that they may be less effective in learning representations for fine-grained visual recognition (FGVR) since many features helpful for optimizing SSL objectives are not suitable for characterizing the subtle differences in FGVR. To overcome this issue, we propose learning an additional screening mechanism to identify discriminative clues commonly seen across instances and classes, dubbed as common rationales in this paper. Intuitively, common rationales tend to correspond to the discriminative patterns from the key parts of foreground objects. We show that a common rationale detector can be learned by simply exploiting the GradCAM induced from the SSL objective without using any pre-trained object parts or saliency detectors, making it seamlessly to be integrated with the existing SSL process. Specifically, we fit the GradCAM with a branch with limited fitting capacity, which allows the branch to capture the common rationales and discard the less common discriminative patterns. At the test stage, the branch generates a set of spatial weights to selectively aggregate features representing an instance. Extensive experimental results on four visual tasks demonstrate that the proposed method can lead to a significant improvement in different evaluation settings.
    Reasons for the Superiority of Stochastic Estimators over Deterministic Ones: Robustness, Consistency and Perceptual Quality. (arXiv:2211.08944v3 [eess.IV] UPDATED)
    Stochastic restoration algorithms allow to explore the space of solutions that correspond to the degraded input. In this paper we reveal additional fundamental advantages of stochastic methods over deterministic ones, which further motivate their use. First, we prove that any restoration algorithm that attains perfect perceptual quality and whose outputs are consistent with the input must be a posterior sampler, and is thus required to be stochastic. Second, we illustrate that while deterministic restoration algorithms may attain high perceptual quality, this can be achieved only by filling up the space of all possible source images using an extremely sensitive mapping, which makes them highly vulnerable to adversarial attacks. Indeed, we show that enforcing deterministic models to be robust to such attacks profoundly hinders their perceptual quality, while robustifying stochastic models hardly influences their perceptual quality, and improves their output variability. These findings provide a motivation to foster progress in stochastic restoration methods, paving the way to better recovery algorithms.
    A Bayesian approach to quantifying uncertainties and improving generalizability in traffic prediction models. (arXiv:2307.05946v3 [cs.LG] UPDATED)
    Deep-learning models for traffic data prediction can have superior performance in modeling complex functions using a multi-layer architecture. However, a major drawback of these approaches is that most of these approaches do not offer forecasts with uncertainty estimates, which are essential for traffic operations and control. Without uncertainty estimates, it is difficult to place any level of trust to the model predictions, and operational strategies relying on overconfident predictions can lead to worsening traffic conditions. In this study, we propose a Bayesian recurrent neural network framework for uncertainty quantification in traffic prediction with higher generalizability by introducing spectral normalization to its hidden layers. In our paper, we have shown that normalization alters the training process of deep neural networks by controlling the model's complexity and reducing the risk of overfitting to the training data. This, in turn, helps improve the generalization performance of the model on out-of-distribution datasets. Results demonstrate that spectral normalization improves uncertainty estimates and significantly outperforms both the layer normalization and model without normalization in single-step prediction horizons. This improved performance can be attributed to the ability of spectral normalization to better localize the feature space of the data under perturbations. Our findings are especially relevant to traffic management applications, where predicting traffic conditions across multiple locations is the goal, but the availability of training data from multiple locations is limited. Spectral normalization, therefore, provides a more generalizable approach that can effectively capture the underlying patterns in traffic data without requiring location-specific models.
    Visual Pre-training for Navigation: What Can We Learn from Noise?. (arXiv:2207.00052v3 [cs.CV] UPDATED)
    One powerful paradigm in visual navigation is to predict actions from observations directly. Training such an end-to-end system allows representations useful for downstream tasks to emerge automatically. However, the lack of inductive bias makes this system data inefficient. We hypothesize a sufficient representation of the current view and the goal view for a navigation policy can be learned by predicting the location and size of a crop of the current view that corresponds to the goal. We further show that training such random crop prediction in a self-supervised fashion purely on synthetic noise images transfers well to natural home images. The learned representation can then be bootstrapped to learn a navigation policy efficiently with little interaction data. The code is available at https://yanweiw.github.io/noise2ptz
    Pre-Training with Diffusion models for Dental Radiography segmentation. (arXiv:2307.14066v2 [cs.CV] UPDATED)
    Medical radiography segmentation, and specifically dental radiography, is highly limited by the cost of labeling which requires specific expertise and labor-intensive annotations. In this work, we propose a straightforward pre-training method for semantic segmentation leveraging Denoising Diffusion Probabilistic Models (DDPM), which have shown impressive results for generative modeling. Our straightforward approach achieves remarkable performance in terms of label efficiency and does not require architectural modifications between pre-training and downstream tasks. We propose to first pre-train a Unet by exploiting the DDPM training objective, and then fine-tune the resulting model on a segmentation task. Our experimental results on the segmentation of dental radiographs demonstrate that the proposed method is competitive with state-of-the-art pre-training methods.
    Towards Out-Of-Distribution Generalization: A Survey. (arXiv:2108.13624v2 [cs.LG] UPDATED)
    Traditional machine learning paradigms are based on the assumption that both training and test data follow the same statistical pattern, which is mathematically referred to as Independent and Identically Distributed ($i.i.d.$). However, in real-world applications, this $i.i.d.$ assumption often fails to hold due to unforeseen distributional shifts, leading to considerable degradation in model performance upon deployment. This observed discrepancy indicates the significance of investigating the Out-of-Distribution (OOD) generalization problem. OOD generalization is an emerging topic of machine learning research that focuses on complex scenarios wherein the distributions of the test data differ from those of the training data. This paper represents the first comprehensive, systematic review of OOD generalization, encompassing a spectrum of aspects from problem definition, methodological development, and evaluation procedures, to the implications and future directions of the field. Our discussion begins with a precise, formal characterization of the OOD generalization problem. Following that, we categorize existing methodologies into three segments: unsupervised representation learning, supervised model learning, and optimization, according to their positions within the overarching learning process. We provide an in-depth discussion on representative methodologies for each category, further elucidating the theoretical links between them. Subsequently, we outline the prevailing benchmark datasets employed in OOD generalization studies. To conclude, we overview the existing body of work in this domain and suggest potential avenues for future research on OOD generalization. A summary of the OOD generalization methodologies surveyed in this paper can be accessed at this http URL
    Statistical process monitoring of artificial neural networks. (arXiv:2209.07436v2 [stat.ME] UPDATED)
    The rapid advancement of models based on artificial intelligence demands innovative monitoring techniques which can operate in real time with low computational costs. In machine learning, especially if we consider artificial neural networks (ANNs), the models are often trained in a supervised manner. Consequently, the learned relationship between the input and the output must remain valid during the model's deployment. If this stationarity assumption holds, we can conclude that the ANN provides accurate predictions. Otherwise, the retraining or rebuilding of the model is required. We propose considering the latent feature representation of the data (called "embedding") generated by the ANN to determine the time when the data stream starts being nonstationary. In particular, we monitor embeddings by applying multivariate control charts based on the data depth calculation and normalized ranks. The performance of the introduced method is compared with benchmark approaches for various ANN architectures and different underlying data formats.
    Efficient Alternating Minimization with Applications to Weighted Low Rank Approximation. (arXiv:2306.04169v2 [cs.LG] UPDATED)
    Weighted low rank approximation is a fundamental problem in numerical linear algebra, and it has many applications in machine learning. Given a matrix $M \in \mathbb{R}^{n \times n}$, a weight matrix $W \in \mathbb{R}_{\geq 0}^{n \times n}$, a parameter $k$, the goal is to output two matrices $U, V \in \mathbb{R}^{n \times k}$ such that $\| W \circ (M - U V^\top) \|_F$ is minimized, where $\circ$ denotes the Hadamard product. Such a problem is known to be NP-hard and even hard to approximate assuming Exponential Time Hypothesis [GG11, RSW16]. Meanwhile, alternating minimization is a good heuristic solution for approximating weighted low rank approximation. The work [LLR16] shows that, under mild assumptions, alternating minimization does provide provable guarantees. In this work, we develop an efficient and robust framework for alternating minimization. For weighted low rank approximation, this improves the runtime of [LLR16] from $n^2 k^2$ to $n^2k$. At the heart of our work framework is a high-accuracy multiple response regression solver together with a robust analysis of alternating minimization.
    Automating Model Comparison in Factor Graphs. (arXiv:2306.05965v2 [cs.LG] UPDATED)
    Bayesian state and parameter estimation have been automated effectively in a variety of probabilistic programming languages. The process of model comparison on the other hand, which still requires error-prone and time-consuming manual derivations, is often overlooked despite its importance. This paper efficiently automates Bayesian model averaging, selection, and combination by message passing on a Forney-style factor graph with a custom mixture node. Parameter and state inference, and model comparison can then be executed simultaneously using message passing with scale factors. This approach shortens the model design cycle and allows for the straightforward extension to hierarchical and temporal model priors to accommodate for modeling complicated time-varying processes.
    On Learning the Tail Quantiles of Driving Behavior Distributions via Quantile Regression and Flows. (arXiv:2305.13106v2 [cs.LG] UPDATED)
    Towards safe autonomous driving (AD), we consider the problem of learning models that accurately capture the diversity and tail quantiles of human driver behavior probability distributions, in interaction with an AD vehicle. Such models, which predict drivers' continuous actions from their states, are particularly relevant for closing the gap between AD agent simulations and reality. To this end, we adapt two flexible quantile learning frameworks for this setting that avoid strong distributional assumptions: (1) quantile regression (based on the titled absolute loss), and (2) autoregressive quantile flows (a version of normalizing flows). Training happens in a behavior cloning-fashion. We use the highD dataset consisting of driver trajectories on several highways. We evaluate our approach in a one-step acceleration prediction task, and in multi-step driver simulation rollouts. We report quantitative results using the tilted absolute loss as metric, give qualitative examples showing that realistic extremal behavior can be learned, and discuss the main insights.
    On the Vulnerability of Fairness Constrained Learning to Malicious Noise. (arXiv:2307.11892v2 [cs.LG] UPDATED)
    We consider the vulnerability of fairness-constrained learning to small amounts of malicious noise in the training data. Konstantinov and Lampert (2021) initiated the study of this question and presented negative results showing there exist data distributions where for several fairness constraints, any proper learner will exhibit high vulnerability when group sizes are imbalanced. Here, we present a more optimistic view, showing that if we allow randomized classifiers, then the landscape is much more nuanced. For example, for Demographic Parity we show we can incur only a $\Theta(\alpha)$ loss in accuracy, where $\alpha$ is the malicious noise rate, matching the best possible even without fairness constraints. For Equal Opportunity, we show we can incur an $O(\sqrt{\alpha})$ loss, and give a matching $\Omega(\sqrt{\alpha})$lower bound. In contrast, Konstantinov and Lampert (2021) showed for proper learners the loss in accuracy for both notions is $\Omega(1)$. The key technical novelty of our work is how randomization can bypass simple "tricks" an adversary can use to amplify his power. We also consider additional fairness notions including Equalized Odds and Calibration. For these fairness notions, the excess accuracy clusters into three natural regimes $O(\alpha)$,$O(\sqrt{\alpha})$ and $O(1)$. These results provide a more fine-grained view of the sensitivity of fairness-constrained learning to adversarial noise in training data.
    Efficient Interaction-Aware Interval Analysis of Neural Network Feedback Loops. (arXiv:2307.14938v1 [eess.SY])
    In this paper, we propose a computationally efficient framework for interval reachability of neural network controlled systems. Our approach builds upon inclusion functions for the neural network controller and the open-loop system. We observe that many state-of-the-art neural network verifiers can produce inclusion functions for neural networks. We introduce and analyze a new class of inclusion functions for the open-loop dynamics based on bounds of the function Jacobian that is particularly suitable for capturing the interactions between systems and neural network controllers. Next, for any dynamical system, we use inclusion functions to construct an embedding system with twice the number of states as the original system. We show that a single trajectory of this embedding system provides hyper-rectangular over-approximations of reachable sets. We then propose two approaches for constructing a closed-loop embedding system for a neural network controlled dynamical system that accounts for the interaction between the system and the controller in different ways. The interconnection-based approach accounts for the worst-case evolution of each coordinate separately by substituting the neural network inclusion function into the open-loop embedding system. The interaction-based approach uses the newly introduced class of Jacobian-based inclusion functions to fully capture first-order interactions between the system and the controller. Finally, we implement our approach in a Python framework called \texttt{ReachMM} and show that on several existing benchmarks, our methods outperform the existing approaches in the literature. We also demonstrate the scalability of our method on a vehicle platooning example with up to $200$ states.
    Nonsmooth Nonconvex-Nonconcave Minimax Optimization: Primal-Dual Balancing and Iteration Complexity Analysis. (arXiv:2209.10825v3 [math.OC] UPDATED)
    Nonconvex-nonconcave minimax optimization has gained widespread interest over the last decade. However, most existing works focus on variants of gradient descent-ascent (GDA) algorithms, which are only applicable to smooth nonconvex-concave settings. To address this limitation, we propose a novel algorithm named smoothed proximal linear descent-ascent (smoothed PLDA), which can effectively handle a broad range of structured nonsmooth nonconvex-nonconcave minimax problems. Specifically, we consider the setting where the primal function has a nonsmooth composite structure and the dual function possesses the Kurdyka-Lojasiewicz (KL) property with exponent $\theta \in [0,1)$. We introduce a novel convergence analysis framework for smoothed PLDA, the key components of which are our newly developed nonsmooth primal error bound and dual error bound. Using this framework, we show that smoothed PLDA can find both $\epsilon$-game-stationary points and $\epsilon$-optimization-stationary points of the problems of interest in $\mathcal{O}(\epsilon^{-2\max\{2\theta,1\}})$ iterations. Furthermore, when $\theta \in [0,\frac{1}{2}]$, smoothed PLDA achieves the optimal iteration complexity of $\mathcal{O}(\epsilon^{-2})$. To further demonstrate the effectiveness and wide applicability of our analysis framework, we show that certain max-structured problem possesses the KL property with exponent $\theta=0$ under mild assumptions. As a by-product, we establish algorithm-independent quantitative relationships among various stationarity concepts, which may be of independent interest.
    Exploiting Richness of Learned Compressed Representation of Images for Semantic Segmentation. (arXiv:2307.01524v2 [cs.CV] UPDATED)
    Autonomous vehicles and Advanced Driving Assistance Systems (ADAS) have the potential to radically change the way we travel. Many such vehicles currently rely on segmentation and object detection algorithms to detect and track objects around its surrounding. The data collected from the vehicles are often sent to cloud servers to facilitate continual/life-long learning of these algorithms. Considering the bandwidth constraints, the data is compressed before sending it to servers, where it is typically decompressed for training and analysis. In this work, we propose the use of a learning-based compression Codec to reduce the overhead in latency incurred for the decompression operation in the standard pipeline. We demonstrate that the learned compressed representation can also be used to perform tasks like semantic segmentation in addition to decompression to obtain the images. We experimentally validate the proposed pipeline on the Cityscapes dataset, where we achieve a compression factor up to $66 \times$ while preserving the information required to perform segmentation with a dice coefficient of $0.84$ as compared to $0.88$ achieved using decompressed images while reducing the overall compute by $11\%$.
    Learning Transfer Operators by Kernel Density Estimation. (arXiv:2210.03124v3 [cs.LG] UPDATED)
    Inference of transfer operators from data is often formulated as a classical problem that hinges on the Ulam method. The conventional description, known as the Ulam-Galerkin method, involves projecting onto basis functions represented as characteristic functions supported over a fine grid of rectangles. From this perspective, the Ulam-Galerkin approach can be interpreted as density estimation using the histogram method. In this study, we recast the problem within the framework of statistical density estimation. This alternative perspective allows for an explicit and rigorous analysis of bias and variance, thereby facilitating a discussion on the mean square error. Through comprehensive examples utilizing the logistic map and a Markov map, we demonstrate the validity and effectiveness of this approach in estimating the eigenvectors of the Frobenius-Perron operator. We compare the performance of Histogram Density Estimation(HDE) and Kernel Density Estimation(KDE) methods and find that KDE generally outperforms HDE in terms of accuracy. However, it is important to note that KDE exhibits limitations around boundary points and jumps. Based on our research findings, we suggest the possibility of incorporating other density estimation methods into this field and propose future investigations into the application of KDE-based estimation for high-dimensional maps. These findings provide valuable insights for researchers and practitioners working on estimating the Frobenius-Perron operator and highlight the potential of density estimation techniques in this area of study. Keywords: Transfer Operators; Frobenius-Perron operator; probability density estimation; Ulam-Galerkin method; Kernel Density Estimation; Histogram Density Estimation.
    Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls. (arXiv:2212.07959v2 [physics.chem-ph] UPDATED)
    Neural network (NN) potentials promise highly accurate molecular dynamics (MD) simulations within the computational complexity of classical MD force fields. However, when applied outside their training domain, NN potential predictions can be inaccurate, increasing the need for Uncertainty Quantification (UQ). Bayesian modeling provides the mathematical framework for UQ, but classical Bayesian methods based on Markov chain Monte Carlo (MCMC) are computationally intractable for NN potentials. By training graph NN potentials for coarse-grained systems of liquid water and alanine dipeptide, we demonstrate here that scalable Bayesian UQ via stochastic gradient MCMC (SG-MCMC) yields reliable uncertainty estimates for MD observables. We show that cold posteriors can reduce the required training data size and that for reliable UQ, multiple Markov chains are needed. Additionally, we find that SG-MCMC and the Deep Ensemble method achieve comparable results, despite shorter training and less hyperparameter tuning of the latter. We show that both methods can capture aleatoric and epistemic uncertainty reliably, but not systematic uncertainty, which needs to be minimized by adequate modeling to obtain accurate credible intervals for MD observables. Our results represent a step towards accurate UQ that is of vital importance for trustworthy NN potential-based MD simulations required for decision-making in practice.
    VeML: An End-to-End Machine Learning Lifecycle for Large-scale and High-dimensional Data. (arXiv:2304.13037v2 [cs.LG] UPDATED)
    An end-to-end machine learning (ML) lifecycle consists of many iterative processes, from data preparation and ML model design to model training and then deploying the trained model for inference. When building an end-to-end lifecycle for an ML problem, many ML pipelines must be designed and executed that produce a huge number of lifecycle versions. Therefore, this paper introduces VeML, a Version management system dedicated to end-to-end ML Lifecycle. Our system tackles several crucial problems that other systems have not solved. First, we address the high cost of building an ML lifecycle, especially for large-scale and high-dimensional dataset. We solve this problem by proposing to transfer the lifecycle of similar datasets managed in our system to the new training data. We design an algorithm based on the core set to compute similarity for large-scale, high-dimensional data efficiently. Another critical issue is the model accuracy degradation by the difference between training data and testing data during the ML lifetime, which leads to lifecycle rebuild. Our system helps to detect this mismatch without getting labeled data from testing data and rebuild the ML lifecycle for a new data version. To demonstrate our contributions, we conduct experiments on real-world, large-scale datasets of driving images and spatiotemporal sensor data and show promising results.
    Differential Convolutional Fuzzy Time Series Forecasting. (arXiv:2305.08890v2 [cs.LG] UPDATED)
    Fuzzy time series forecasting (FTSF) is a typical forecasting method with wide application. Traditional FTSF is regarded as an expert system which leads to loss of the ability to recognize undefined features. The mentioned is the main reason for poor forecasting with FTSF. To solve the problem, the proposed model Differential Fuzzy Convolutional Neural Network (DFCNN) utilizes a convolution neural network to re-implement FTSF with learnable ability. DFCNN is capable of recognizing potential information and improving forecasting accuracy. Thanks to the learnable ability of the neural network, the length of fuzzy rules established in FTSF is expended to an arbitrary length that the expert is not able to handle by the expert system. At the same time, FTSF usually cannot achieve satisfactory performance of non-stationary time series due to the trend of non-stationary time series. The trend of non-stationary time series causes the fuzzy set established by FTSF to be invalid and causes the forecasting to fail. DFCNN utilizes the Difference algorithm to weaken the non-stationary of time series so that DFCNN can forecast the non-stationary time series with a low error that FTSF cannot forecast in satisfactory performance. After the mass of experiments, DFCNN has an excellent prediction effect, which is ahead of the existing FTSF and common time series forecasting algorithms. Finally, DFCNN provides further ideas for improving FTSF and holds continued research value.
    Harnessing Synthetic Active Particles for Physical Reservoir Computing. (arXiv:2307.15010v1 [cond-mat.soft])
    The processing of information is an indispensable property of living systems realized by networks of active processes with enormous complexity. They have inspired many variants of modern machine learning one of them being reservoir computing, in which stimulating a network of nodes with fading memory enables computations and complex predictions. Reservoirs are implemented on computer hardware, but also on unconventional physical substrates such as mechanical oscillators, spins, or bacteria often summarized as physical reservoir computing. Here we demonstrate physical reservoir computing with a synthetic active microparticle system that self-organizes from an active and passive component into inherently noisy nonlinear dynamical units. The self-organization and dynamical response of the unit is the result of a delayed propulsion of the microswimmer to a passive target. A reservoir of such units with a self-coupling via the delayed response can perform predictive tasks despite the strong noise resulting from Brownian motion of the microswimmers. To achieve efficient noise suppression, we introduce a special architecture that uses historical reservoir states for output. Our results pave the way for the study of information processing in synthetic self-organized active particle systems.
    FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoising. (arXiv:2304.00570v2 [eess.IV] UPDATED)
    Low-count PET is an efficient way to reduce radiation exposure and acquisition time, but the reconstructed images often suffer from low signal-to-noise ratio (SNR), thus affecting diagnosis and other downstream tasks. Recent advances in deep learning have shown great potential in improving low-count PET image quality, but acquiring a large, centralized, and diverse dataset from multiple institutions for training a robust model is difficult due to privacy and security concerns of patient data. Moreover, low-count PET data at different institutions may have different data distribution, thus requiring personalized models. While previous federated learning (FL) algorithms enable multi-institution collaborative training without the need of aggregating local data, addressing the large domain shift in the application of multi-institutional low-count PET denoising remains a challenge and is still highly under-explored. In this work, we propose FedFTN, a personalized federated learning strategy that addresses these challenges. FedFTN uses a local deep feature transformation network (FTN) to modulate the feature outputs of a globally shared denoising network, enabling personalized low-count PET denoising for each institution. During the federated learning process, only the denoising network's weights are communicated and aggregated, while the FTN remains at the local institutions for feature transformation. We evaluated our method using a large-scale dataset of multi-institutional low-count PET imaging data from three medical centers located across three continents, and showed that FedFTN provides high-quality low-count PET images, outperforming previous baseline FL reconstruction methods across all low-count levels at all three institutions.
    Learning a Generic Value-Selection Heuristic Inside a Constraint Programming Solver. (arXiv:2301.01913v2 [cs.AI] UPDATED)
    Constraint programming is known for being an efficient approach for solving combinatorial problems. Important design choices in a solver are the branching heuristics, which are designed to lead the search to the best solutions in a minimum amount of time. However, developing these heuristics is a time-consuming process that requires problem-specific expertise. This observation has motivated many efforts to use machine learning to automatically learn efficient heuristics without expert intervention. To the best of our knowledge, it is still an open research question. Although several generic variable-selection heuristics are available in the literature, the options for a generic value-selection heuristic are more scarce. In this paper, we propose to tackle this issue by introducing a generic learning procedure that can be used to obtain a value-selection heuristic inside a constraint programming solver. This has been achieved thanks to the combination of a deep Q-learning algorithm, a tailored reward signal, and a heterogeneous graph neural network architecture. Experiments on graph coloring, maximum independent set, and maximum cut problems show that our framework is able to find better solutions close to optimality without requiring a large amounts of backtracks while being generic.
    Causal Lifting and Link Prediction. (arXiv:2302.01198v2 [cs.LG] UPDATED)
    Existing causal models for link prediction assume an underlying set of inherent node factors -- an innate characteristic defined at the node's birth -- that governs the causal evolution of links in the graph. In some causal tasks, however, link formation is path-dependent: The outcome of link interventions depends on existing links. Unfortunately, these existing causal methods are not designed for path-dependent link formation, as the cascading functional dependencies between links (arising from path dependence) are either unidentifiable or require an impractical number of control variables. To overcome this, we develop the first causal model capable of dealing with path dependencies in link prediction. In this work we introduce the concept of causal lifting, an invariance in causal models of independent interest that, on graphs, allows the identification of causal link prediction queries using limited interventional data. Further, we show how structural pairwise embeddings exhibit lower bias and correctly represent the task's causal structure, as opposed to existing node embeddings, e.g., graph neural network node embeddings and matrix factorization. Finally, we validate our theoretical findings on three scenarios for causal link prediction tasks: knowledge base completion, covariance matrix estimation and consumer-product recommendations.
    Algorithmic Hallucinations of Near-Surface Winds: Statistical Downscaling with Generative Adversarial Networks to Convection-Permitting Scales. (arXiv:2302.08720v2 [physics.ao-ph] UPDATED)
    This paper explores the application of emerging machine learning methods from image super-resolution (SR) to the task of statistical downscaling. We specifically focus on convolutional neural network-based Generative Adversarial Networks (GANs). Our GANs are conditioned on low-resolution (LR) inputs to generate high-resolution (HR) surface winds emulating Weather Research and Forecasting (WRF) model simulations over North America. Unlike traditional SR models, where LR inputs are idealized coarsened versions of the HR images, WRF emulation involves using non-idealized LR and HR pairs resulting in shared-scale mismatches due to internal variability. Our study builds upon current SR-based statistical downscaling by experimenting with a novel frequency-separation (FS) approach from the computer vision field. To assess the skill of SR models, we carefully select evaluation metrics, and focus on performance measures based on spatial power spectra. Our analyses reveal how GAN configurations influence spatial structures in the generated fields, particularly biases in spatial variability spectra. Using power spectra to evaluate the FS experiments reveals that successful applications of FS in computer vision do not translate to climate fields. However, the FS experiments demonstrate the sensitivity of power spectra to a commonly used GAN-based SR objective function, which helps interpret and understand its role in determining spatial structures. This result motivates the development of a novel partial frequency-separation scheme as a promising configuration option. We also quantify the influence on GAN performance of non-idealized LR fields resulting from internal variability. Furthermore, we conduct a spectra-based feature-importance experiment allowing us to explore the dependence of the spatial structure of generated fields on different physically relevant LR covariates.
    Trace Recovery from Stochastically Known Logs. (arXiv:2206.12672v2 [cs.LG] UPDATED)
    In this work we propose an algorithm for trace recovery from stochastically known logs, a setting that is becoming more common with the increasing number of sensors and predictive models that generate uncertain data. The suggested approach calculates the conformance between a process model and a stochastically known trace and recovers the best alignment within this stochastic trace as the true trace. The paper offers an analysis of the impact of various cost models on trace recovery accuracy and makes use of a product multi-graph to compare alternative trace recovery options. The average accuracy of our approach, evaluated using two publicly available datasets, is impressive, with an average recovery accuracy score of 90-97%, significantly improving a common heuristic that chooses the most likely value for each uncertain activity. We believe that the effectiveness of the proposed algorithm in recovering correct traces from stochastically known logs may be a powerful aid for developing credible decision-making tools in uncertain settings.
    Exploring Weight Balancing on Long-Tailed Recognition Problem. (arXiv:2305.16573v4 [cs.LG] UPDATED)
    Recognition problems in long-tailed data, where the sample size per class is heavily skewed, have recently gained importance because the distribution of the sample size per class in a dataset is generally exponential unless the sample size is intentionally adjusted. Various approaches have been devised to address these problems. Recently, weight balancing, which combines well-known classical regularization techniques with two-stage training, has been proposed. Despite its simplicity, it is known for its high performance against existing methods devised in various ways. However, there is a lack of understanding as to why this approach is effective for long-tailed data. In this study, we analyze the method focusing on neural collapse and cone effect at each training stage and find that it can be decomposed into the increase in Fisher's discriminant ratio of the feature extractor caused by weight decay and cross entropy loss and implicit logit adjustment caused by weight decay and class-balanced loss. Our analysis shows that the training method can be further simplified by reducing the number of training stages to one while increasing accuracy.
    Self-Supervised Graph Transformer for Deepfake Detection. (arXiv:2307.15019v1 [cs.CV])
    Deepfake detection methods have shown promising results in recognizing forgeries within a given dataset, where training and testing take place on the in-distribution dataset. However, their performance deteriorates significantly when presented with unseen samples. As a result, a reliable deepfake detection system must remain impartial to forgery types, appearance, and quality for guaranteed generalizable detection performance. Despite various attempts to enhance cross-dataset generalization, the problem remains challenging, particularly when testing against common post-processing perturbations, such as video compression or blur. Hence, this study introduces a deepfake detection framework, leveraging a self-supervised pre-training model that delivers exceptional generalization ability, withstanding common corruptions and enabling feature explainability. The framework comprises three key components: a feature extractor based on vision Transformer architecture that is pre-trained via self-supervised contrastive learning methodology, a graph convolution network coupled with a Transformer discriminator, and a graph Transformer relevancy map that provides a better understanding of manipulated regions and further explains the model's decision. To assess the effectiveness of the proposed framework, several challenging experiments are conducted, including in-data distribution performance, cross-dataset, cross-manipulation generalization, and robustness against common post-production perturbations. The results achieved demonstrate the remarkable effectiveness of the proposed deepfake detection framework, surpassing the current state-of-the-art approaches.
    Speeding up Fourier Neural Operators via Mixed Precision. (arXiv:2307.15034v1 [cs.LG])
    The Fourier neural operator (FNO) is a powerful technique for learning surrogate maps for partial differential equation (PDE) solution operators. For many real-world applications, which often require high-resolution data points, training time and memory usage are significant bottlenecks. While there are mixed-precision training techniques for standard neural networks, those work for real-valued datatypes on finite dimensions and therefore cannot be directly applied to FNO, which crucially operates in the (complex-valued) Fourier domain and in function spaces. On the other hand, since the Fourier transform is already an approximation (due to discretization error), we do not need to perform the operation at full precision. In this work, we (i) profile memory and runtime for FNO with full and mixed-precision training, (ii) conduct a study on the numerical stability of mixed-precision training of FNO, and (iii) devise a training routine which substantially decreases training time and memory usage (up to 34%), with little or no reduction in accuracy, on the Navier-Stokes and Darcy flow equations. Combined with the recently proposed tensorized FNO (Kossaifi et al., 2023), the resulting model has far better performance while also being significantly faster than the original FNO.
    Large Language Models Struggle to Learn Long-Tail Knowledge. (arXiv:2211.08411v2 [cs.CL] UPDATED)
    The Internet contains a wealth of knowledge -- from the birthdays of historical figures to tutorials on how to code -- all of which may be learned by language models. However, while certain pieces of information are ubiquitous on the web, others appear extremely rarely. In this paper, we study the relationship between the knowledge memorized by large language models and the information in pre-training datasets scraped from the web. In particular, we show that a language model's ability to answer a fact-based question relates to how many documents associated with that question were seen during pre-training. We identify these relevant documents by entity linking pre-training datasets and counting documents that contain the same entities as a given question-answer pair. Our results demonstrate strong correlational and causal relationships between accuracy and relevant document count for numerous question answering datasets (e.g., TriviaQA), pre-training corpora (e.g., ROOTS), and model sizes (e.g., 176B parameters). Moreover, while larger models are better at learning long-tail knowledge, we estimate that today's models must be scaled by many orders of magnitude to reach competitive QA performance on questions with little support in the pre-training data. Finally, we show that retrieval-augmentation can reduce the dependence on relevant pre-training information, presenting a promising approach for capturing the long-tail.
    Analyzing Explainer Robustness via Lipschitzness of Prediction Functions. (arXiv:2206.12481v2 [cs.LG] UPDATED)
    Machine learning methods have significantly improved in their predictive capabilities, but at the same time they are becoming more complex and less transparent. As a result, explainers are often relied on to provide interpretability to these black-box prediction models. As crucial diagnostics tools, it is important that these explainers themselves are robust. In this paper we focus on one particular aspect of robustness, namely that an explainer should give similar explanations for similar data inputs. We formalize this notion by introducing and defining explainer astuteness, analogous to astuteness of prediction functions. Our formalism allows us to connect explainer robustness to the predictor's probabilistic Lipschitzness, which captures the probability of local smoothness of a function. We provide lower bound guarantees on the astuteness of a variety of explainers (e.g., SHAP, RISE, CXPlain) given the Lipschitzness of the prediction function. These theoretical results imply that locally smooth prediction functions lend themselves to locally robust explanations. We evaluate these results empirically on simulated as well as real datasets.
    Predicting Winning Regions in Parity Games via Graph Neural Networks (Extended Abstract). (arXiv:2210.09924v2 [cs.GT] UPDATED)
    Solving parity games is a major building block for numerous applications in reactive program verification and synthesis. While they can be solved efficiently in practice, no known approach has a polynomial worst-case runtime complexity. We present a incomplete polynomial-time approach to determining the winning regions of parity games via graph neural networks. Our evaluation on 900 randomly generated parity games shows that this approach is effective and efficient in practice. It correctly determines the winning regions of $\sim$60\% of the games in our data set and only incurs minor errors in the remaining ones. We believe that this approach can be extended to efficiently solve parity games as well.
    Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning. (arXiv:2106.03328v2 [cs.LG] UPDATED)
    Secure aggregation is a critical component in federated learning (FL), which enables the server to learn the aggregate model of the users without observing their local models. Conventionally, secure aggregation algorithms focus only on ensuring the privacy of individual users in a single training round. We contend that such designs can lead to significant privacy leakages over multiple training rounds, due to partial user selection/participation at each round of FL. In fact, we show that the conventional random user selection strategies in FL lead to leaking users' individual models within number of rounds that is linear in the number of users. To address this challenge, we introduce a secure aggregation framework, Multi-RoundSecAgg, with multi-round privacy guarantees. In particular, we introduce a new metric to quantify the privacy guarantees of FL over multiple training rounds, and develop a structured user selection strategy that guarantees the long-term privacy of each user (over any number of training rounds). Our framework also carefully accounts for the fairness and the average number of participating users at each round. Our experiments on MNIST and CIFAR-10 datasets in the IID and the non-IID settings demonstrate the performance improvement over the baselines, both in terms of privacy protection and test accuracy.
    Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning. (arXiv:2205.14704v4 [cs.CL] UPDATED)
    Prompt learning approaches have made waves in natural language processing by inducing better few-shot performance while they still follow a parametric-based learning paradigm; the oblivion and rote memorization problems in learning may encounter unstable generalization issues. Specifically, vanilla prompt learning may struggle to utilize atypical instances by rote during fully-supervised training or overfit shallow patterns with low-shot data. To alleviate such limitations, we develop RetroPrompt with the motivation of decoupling knowledge from memorization to help the model strike a balance between generalization and memorization. In contrast with vanilla prompt learning, RetroPrompt constructs an open-book knowledge-store from training instances and implements a retrieval mechanism during the process of input, training and inference, thus equipping the model with the ability to retrieve related contexts from the training corpus as cues for enhancement. Extensive experiments demonstrate that RetroPrompt can obtain better performance in both few-shot and zero-shot settings. Besides, we further illustrate that our proposed RetroPrompt can yield better generalization abilities with new datasets. Detailed analysis of memorization indeed reveals RetroPrompt can reduce the reliance of language models on memorization; thus, improving generalization for downstream tasks. Code is available in https://github.com/zjunlp/PromptKG/tree/main/research/RetroPrompt.
    Contrastive Domain Adaptation for Time-Series via Temporal Mixup. (arXiv:2212.01555v2 [cs.LG] UPDATED)
    Unsupervised Domain Adaptation (UDA) has emerged as a powerful solution for the domain shift problem via transferring the knowledge from a labeled source domain to a shifted unlabeled target domain. Despite the prevalence of UDA for visual applications, it remains relatively less explored for time-series applications. In this work, we propose a novel lightweight contrastive domain adaptation framework called CoTMix for time-series data. Unlike existing approaches that either use statistical distances or adversarial techniques, we leverage contrastive learning solely to mitigate the distribution shift across the different domains. Specifically, we propose a novel temporal mixup strategy to generate two intermediate augmented views for the source and target domains. Subsequently, we leverage contrastive learning to maximize the similarity between each domain and its corresponding augmented view. The generated views consider the temporal dynamics of time-series data during the adaptation process while inheriting the semantics among the two domains. Hence, we gradually push both domains towards a common intermediate space, mitigating the distribution shift across them. Extensive experiments conducted on five real-world time-series datasets show that our approach can significantly outperform all state-of-the-art UDA methods. The implementation code of CoTMix is available at \href{https://github.com/emadeldeen24/CoTMix}{github.com/emadeldeen24/CoTMix}.
    Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model Performance. (arXiv:2307.14657v1 [cs.CR])
    Many studies have proposed machine-learning (ML) models for malware detection and classification, reporting an almost-perfect performance. However, they assemble ground-truth in different ways, use diverse static- and dynamic-analysis techniques for feature extraction, and even differ on what they consider a malware family. As a consequence, our community still lacks an understanding of malware classification results: whether they are tied to the nature and distribution of the collected dataset, to what extent the number of families and samples in the training dataset influence performance, and how well static and dynamic features complement each other. This work sheds light on those open questions. by investigating the key factors influencing ML-based malware detection and classification. For this, we collect the largest balanced malware dataset so far with 67K samples from 670 families (100 samples each), and train state-of-the-art models for malware detection and family classification using our dataset. Our results reveal that static features perform better than dynamic features, and that combining both only provides marginal improvement over static features. We discover no correlation between packing and classification accuracy, and that missing behaviors in dynamically-extracted features highly penalize their performance. We also demonstrate how a larger number of families to classify make the classification harder, while a higher number of samples per family increases accuracy. Finally, we find that models trained on a uniform distribution of samples per family better generalize on unseen data.
    Learning locally dominant force balances in active particle systems. (arXiv:2307.14970v1 [cond-mat.soft])
    We use a combination of unsupervised clustering and sparsity-promoting inference algorithms to learn locally dominant force balances that explain macroscopic pattern formation in self-organized active particle systems. The self-organized emergence of macroscopic patterns from microscopic interactions between self-propelled particles can be widely observed nature. Although hydrodynamic theories help us better understand the physical basis of this phenomenon, identifying a sufficient set of local interactions that shape, regulate, and sustain self-organized structures in active particle systems remains challenging. We investigate a classic hydrodynamic model of self-propelled particles that produces a wide variety of patterns, like asters and moving density bands. Our data-driven analysis shows that propagating bands are formed by local alignment interactions driven by density gradients, while steady-state asters are shaped by a mechanism of splay-induced negative compressibility arising from strong particle interactions. Our method also reveals analogous physical principles of pattern formation in a system where the speed of the particle is influenced by local density. This demonstrates the ability of our method to reveal physical commonalities across models. The physical mechanisms inferred from the data are in excellent agreement with analytical scaling arguments and experimental observations.
    On the Generalization Effects of Linear Transformations in Data Augmentation. (arXiv:2005.00695v3 [cs.LG] UPDATED)
    Data augmentation is a powerful technique to improve performance in applications such as image and text classification tasks. Yet, there is little rigorous understanding of why and how various augmentations work. In this work, we consider a family of linear transformations and study their effects on the ridge estimator in an over-parametrized linear regression setting. First, we show that transformations that preserve the labels of the data can improve estimation by enlarging the span of the training data. Second, we show that transformations that mix data can improve estimation by playing a regularization effect. Finally, we validate our theoretical insights on MNIST. Based on the insights, we propose an augmentation scheme that searches over the space of transformations by how uncertain the model is about the transformed data. We validate our proposed scheme on image and text datasets. For example, our method outperforms random sampling methods by 1.24% on CIFAR-100 using Wide-ResNet-28-10. Furthermore, we achieve comparable accuracy to the SoTA Adversarial AutoAugment on CIFAR-10, CIFAR-100, SVHN, and ImageNet datasets.
    On (Normalised) Discounted Cumulative Gain as an Offline Evaluation Metric for Top-$n$ Recommendation. (arXiv:2307.15053v1 [cs.IR])
    Approaches to recommendation are typically evaluated in one of two ways: (1) via a (simulated) online experiment, often seen as the gold standard, or (2) via some offline evaluation procedure, where the goal is to approximate the outcome of an online experiment. Several offline evaluation metrics have been adopted in the literature, inspired by ranking metrics prevalent in the field of Information Retrieval. (Normalised) Discounted Cumulative Gain (nDCG) is one such metric that has seen widespread adoption in empirical studies, and higher (n)DCG values have been used to present new methods as the state-of-the-art in top-$n$ recommendation for many years. Our work takes a critical look at this approach, and investigates when we can expect such metrics to approximate the gold standard outcome of an online experiment. We formally present the assumptions that are necessary to consider DCG an unbiased estimator of online reward and provide a derivation for this metric from first principles, highlighting where we deviate from its traditional uses in IR. Importantly, we show that normalising the metric renders it inconsistent, in that even when DCG is unbiased, ranking competing methods by their normalised DCG can invert their relative order. Through a correlation analysis between off- and on-line experiments conducted on a large-scale recommendation platform, we show that our unbiased DCG estimates strongly correlate with online reward, even when some of the metric's inherent assumptions are violated. This statement no longer holds for its normalised variant, suggesting that nDCG's practical utility may be limited.
    Dynamics of specialization in neural modules under resource constraints. (arXiv:2106.02626v2 [q-bio.NC] UPDATED)
    It has long been believed that the brain is highly modular both in terms of structure and function, although recent evidence has led some to question the extent of both types of modularity. We used artificial neural networks to test the hypothesis that structural modularity is sufficient to guarantee functional specialization, and find that in general, this doesn't necessarily hold except at extreme levels. We then systematically tested which features of the environment and network do lead to the emergence of specialization. We used a simple toy environment, task and network, allowing us precise control, and show that in this setup, several distinct measures of specialization give qualitatively similar results. We further find that (1) specialization can only emerge in environments where features of that environment are meaningfully separable, (2) specialization preferentially emerges when the network is strongly resource-constrained, and (3) these findings are qualitatively similar across different network architectures, but the quantitative relationships depends on the architecture type. Finally, we show that functional specialization varies dynamically across time, and demonstrate that these dynamics depend on both the timing and bandwidth of information flow in the network. We conclude that a static notion of specialization, based on structural modularity, is likely too simple a framework for understanding intelligent systems in situations of real-world complexity. We propose that thoroughly stress testing candidate definitions of functional modularity in simplified scenarios before extending to more complex data, network models and electrophysiological recordings is likely to be a fruitful approach.
    Learning Task Automata for Reinforcement Learning using Hidden Markov Models. (arXiv:2208.11838v3 [cs.LG] UPDATED)
    Training reinforcement learning (RL) agents using scalar reward signals is often infeasible when an environment has sparse and non-Markovian rewards. Moreover, handcrafting these reward functions before training is prone to misspecification, especially when the environment's dynamics are only partially known. This paper proposes a novel pipeline for learning non-Markovian task specifications as succinct finite-state `task automata' from episodes of agent experience within unknown environments. We leverage two key algorithmic insights. First, we learn a product MDP, a model composed of the specification's automaton and the environment's MDP (both initially unknown), by treating the product MDP as a partially observable MDP and using the well-known Baum-Welch algorithm for learning hidden Markov models. Second, we propose a novel method for distilling the task automaton (assumed to be a deterministic finite automaton) from the learnt product MDP. Our learnt task automaton enables the decomposition of a task into its constituent sub-tasks, which improves the rate at which an RL agent can later synthesise an optimal policy. It also provides an interpretable encoding of high-level environmental and task features, so a human can readily verify that the agent has learnt coherent tasks with no misspecifications. In addition, we take steps towards ensuring that the learnt automaton is environment-agnostic, making it well-suited for use in transfer learning. Finally, we provide experimental results compared with two baselines to illustrate our algorithm's performance in different environments and tasks.
    Pruning Distorted Images in MNIST Handwritten Digits. (arXiv:2307.14343v1 [cs.CV])
    Recognizing handwritten digits is a challenging task primarily due to the diversity of writing styles and the presence of noisy images. The widely used MNIST dataset, which is commonly employed as a benchmark for this task, includes distorted digits with irregular shapes, incomplete strokes, and varying skew in both the training and testing datasets. Consequently, these factors contribute to reduced accuracy in digit recognition. To overcome this challenge, we propose a two-stage deep learning approach. In the first stage, we create a simple neural network to identify distorted digits within the training set. This model serves to detect and filter out such distorted and ambiguous images. In the second stage, we exclude these identified images from the training dataset and proceed to retrain the model using the filtered dataset. This process aims to improve the classification accuracy and confidence levels while mitigating issues of underfitting and overfitting. Our experimental results demonstrate the effectiveness of the proposed approach, achieving an accuracy rate of over 99.5% on the testing dataset. This significant improvement showcases the potential of our method in enhancing digit classification accuracy. In our future work, we intend to explore the scalability of this approach and investigate techniques to further enhance accuracy by reducing the size of the training data.
    Towards Practicable Sequential Shift Detectors. (arXiv:2307.14758v1 [cs.LG])
    There is a growing awareness of the harmful effects of distribution shift on the performance of deployed machine learning models. Consequently, there is a growing interest in detecting these shifts before associated costs have time to accumulate. However, desiderata of crucial importance to the practicable deployment of sequential shift detectors are typically overlooked by existing works, precluding their widespread adoption. We identify three such desiderata, highlight existing works relevant to their satisfaction, and recommend impactful directions for future research.
    Auto-Tables: Synthesizing Multi-Step Transformations to Relationalize Tables without Using Examples. (arXiv:2307.14565v1 [cs.DB])
    Relational tables, where each row corresponds to an entity and each column corresponds to an attribute, have been the standard for tables in relational databases. However, such a standard cannot be taken for granted when dealing with tables "in the wild". Our survey of real spreadsheet-tables and web-tables shows that over 30% of such tables do not conform to the relational standard, for which complex table-restructuring transformations are needed before these tables can be queried easily using SQL-based analytics tools. Unfortunately, the required transformations are non-trivial to program, which has become a substantial pain point for technical and non-technical users alike, as evidenced by large numbers of forum questions in places like StackOverflow and Excel/Tableau forums. We develop an Auto-Tables system that can automatically synthesize pipelines with multi-step transformations (in Python or other languages), to transform non-relational tables into standard relational forms for downstream analytics, obviating the need for users to manually program transformations. We compile an extensive benchmark for this new task, by collecting 194 real test cases from user spreadsheets and online forums. Our evaluation suggests that Auto-Tables can successfully synthesize transformations for over 70% of test cases at interactive speeds, without requiring any input from users, making this an effective tool for both technical and non-technical users to prepare data for analytics.
    Speed Limits for Deep Learning. (arXiv:2307.14653v1 [stat.ML])
    State-of-the-art neural networks require extreme computational power to train. It is therefore natural to wonder whether they are optimally trained. Here we apply a recent advancement in stochastic thermodynamics which allows bounding the speed at which one can go from the initial weight distribution to the final distribution of the fully trained network, based on the ratio of their Wasserstein-2 distance and the entropy production rate of the dynamical process connecting them. Considering both gradient-flow and Langevin training dynamics, we provide analytical expressions for these speed limits for linear and linearizable neural networks e.g. Neural Tangent Kernel (NTK). Remarkably, given some plausible scaling assumptions on the NTK spectra and spectral decomposition of the labels -- learning is optimal in a scaling sense. Our results are consistent with small-scale experiments with Convolutional Neural Networks (CNNs) and Fully Connected Neural networks (FCNs) on CIFAR-10, showing a short highly non-optimal regime followed by a longer optimal regime.
    Fair Machine Unlearning: Data Removal while Mitigating Disparities. (arXiv:2307.14754v1 [cs.LG])
    As public consciousness regarding the collection and use of personal information by corporations grows, it is of increasing importance that consumers be active participants in the curation of corporate datasets. In light of this, data governance frameworks such as the General Data Protection Regulation (GDPR) have outlined the right to be forgotten as a key principle allowing individuals to request that their personal data be deleted from the databases and models used by organizations. To achieve forgetting in practice, several machine unlearning methods have been proposed to address the computational inefficiencies of retraining a model from scratch with each unlearning request. While efficient online alternatives to retraining, it is unclear how these methods impact other properties critical to real-world applications, such as fairness. In this work, we propose the first fair machine unlearning method that can provably and efficiently unlearn data instances while preserving group fairness. We derive theoretical results which demonstrate that our method can provably unlearn data instances while maintaining fairness objectives. Extensive experimentation with real-world datasets highlight the efficacy of our method at unlearning data instances while preserving fairness.
    A Transformer-based Approach for Arabic Offline Handwritten Text Recognition. (arXiv:2307.15045v1 [cs.CV])
    Handwriting recognition is a challenging and critical problem in the fields of pattern recognition and machine learning, with applications spanning a wide range of domains. In this paper, we focus on the specific issue of recognizing offline Arabic handwritten text. Existing approaches typically utilize a combination of convolutional neural networks for image feature extraction and recurrent neural networks for temporal modeling, with connectionist temporal classification used for text generation. However, these methods suffer from a lack of parallelization due to the sequential nature of recurrent neural networks. Furthermore, these models cannot account for linguistic rules, necessitating the use of an external language model in the post-processing stage to boost accuracy. To overcome these issues, we introduce two alternative architectures, namely the Transformer Transducer and the standard sequence-to-sequence Transformer, and compare their performance in terms of accuracy and speed. Our approach can model language dependencies and relies only on the attention mechanism, thereby making it more parallelizable and less complex. We employ pre-trained Transformers for both image understanding and language modeling. Our evaluation on the Arabic KHATT dataset demonstrates that our proposed method outperforms the current state-of-the-art approaches for recognizing offline Arabic handwritten text.
    TimeGNN: Temporal Dynamic Graph Learning for Time Series Forecasting. (arXiv:2307.14680v1 [cs.LG])
    Time series forecasting lies at the core of important real-world applications in many fields of science and engineering. The abundance of large time series datasets that consist of complex patterns and long-term dependencies has led to the development of various neural network architectures. Graph neural network approaches, which jointly learn a graph structure based on the correlation of raw values of multivariate time series while forecasting, have recently seen great success. However, such solutions are often costly to train and difficult to scale. In this paper, we propose TimeGNN, a method that learns dynamic temporal graph representations that can capture the evolution of inter-series patterns along with the correlations of multiple series. TimeGNN achieves inference times 4 to 80 times faster than other state-of-the-art graph-based methods while achieving comparable forecasting performance
    Emotion4MIDI: a Lyrics-based Emotion-Labeled Symbolic Music Dataset. (arXiv:2307.14783v1 [eess.AS])
    We present a new large-scale emotion-labeled symbolic music dataset consisting of 12k MIDI songs. To create this dataset, we first trained emotion classification models on the GoEmotions dataset, achieving state-of-the-art results with a model half the size of the baseline. We then applied these models to lyrics from two large-scale MIDI datasets. Our dataset covers a wide range of fine-grained emotions, providing a valuable resource to explore the connection between music and emotions and, especially, to develop models that can generate music based on specific emotions. Our code for inference, trained models, and datasets are available online.
    Compositional federated learning: Applications in distributionally robust averaging and meta learning. (arXiv:2106.11264v3 [cs.LG] UPDATED)
    In the paper, we propose an effective and efficient Compositional Federated Learning (ComFedL) algorithm for solving a new compositional Federated Learning (FL) framework, which frequently appears in many data mining and machine learning problems with a hierarchical structure such as distributionally robust FL and model-agnostic meta learning (MAML). Moreover, we study the convergence analysis of our ComFedL algorithm under some mild conditions, and prove that it achieves a convergence rate of $O(\frac{1}{\sqrt{T}})$, where $T$ denotes the number of iteration. To the best of our knowledge, our new Compositional FL framework is the first work to bridge federated learning with composition stochastic optimization. In particular, we first transform the distributionally robust FL (i.e., a minimax optimization problem) into a simple composition optimization problem by using KL divergence regularization. At the same time, we also first transform the distribution-agnostic MAML problem (i.e., a minimax optimization problem) into a simple yet effective composition optimization problem. Finally, we apply two popular machine learning tasks, i.e., distributionally robust FL and MAML to demonstrate the effectiveness of our algorithm.
    MATNilm: Multi-appliance-task Non-intrusive Load Monitoring with Limited Labeled Data. (arXiv:2307.14778v1 [cs.LG])
    Non-intrusive load monitoring (NILM) identifies the status and power consumption of various household appliances by disaggregating the total power usage signal of an entire house. Efficient and accurate load monitoring facilitates user profile establishment, intelligent household energy management, and peak load shifting. This is beneficial for both the end-users and utilities by improving the overall efficiency of a power distribution network. Existing approaches mainly focus on developing an individual model for each appliance. Those approaches typically rely on a large amount of household-labeled data which is hard to collect. In this paper, we propose a multi-appliance-task framework with a training-efficient sample augmentation (SA) scheme that boosts the disaggregation performance with limited labeled data. For each appliance, we develop a shared-hierarchical split structure for its regression and classification tasks. In addition, we also propose a two-dimensional attention mechanism in order to capture spatio-temporal correlations among all appliances. With only one-day training data and limited appliance operation profiles, the proposed SA algorithm can achieve comparable test performance to the case of training with the full dataset. Finally, simulation results show that our proposed approach features a significantly improved performance over many baseline models. The relative errors can be reduced by more than 50\% on average. The codes of this work are available at https://github.com/jxiong22/MATNilm
    Scaling Session-Based Transformer Recommendations using Optimized Negative Sampling and Loss Functions. (arXiv:2307.14906v1 [cs.IR])
    This work introduces TRON, a scalable session-based Transformer Recommender using Optimized Negative-sampling. Motivated by the scalability and performance limitations of prevailing models such as SASRec and GRU4Rec+, TRON integrates top-k negative sampling and listwise loss functions to enhance its recommendation accuracy. Evaluations on relevant large-scale e-commerce datasets show that TRON improves upon the recommendation quality of current methods while maintaining training speeds similar to SASRec. A live A/B test yielded an 18.14% increase in click-through rate over SASRec, highlighting the potential of TRON in practical settings. For further research, we provide access to our source code at https://github.com/otto-de/TRON and an anonymized dataset at https://github.com/otto-de/recsys-dataset.
    CAR-DESPOT: Causally-Informed Online POMDP Planning for Robots in Confounded Environments. (arXiv:2304.06848v3 [cs.RO] UPDATED)
    Robots operating in real-world environments must reason about possible outcomes of stochastic actions and make decisions based on partial observations of the true world state. A major challenge for making accurate and robust action predictions is the problem of confounding, which if left untreated can lead to prediction errors. The partially observable Markov decision process (POMDP) is a widely-used framework to model these stochastic and partially-observable decision-making problems. However, due to a lack of explicit causal semantics, POMDP planning methods are prone to confounding bias and thus in the presence of unobserved confounders may produce underperforming policies. This paper presents a novel causally-informed extension of "anytime regularized determinized sparse partially observable tree" (AR-DESPOT), a modern anytime online POMDP planner, using causal modelling and inference to eliminate errors caused by unmeasured confounder variables. We further propose a method to learn offline the partial parameterisation of the causal model for planning, from ground truth model data. We evaluate our methods on a toy problem with an unobserved confounder and show that the learned causal model is highly accurate, while our planning method is more robust to confounding and produces overall higher performing policies than AR-DESPOT.
    Take-A-Photo: 3D-to-2D Generative Pre-training of Point Cloud Models. (arXiv:2307.14971v1 [cs.CV])
    With the overwhelming trend of mask image modeling led by MAE, generative pre-training has shown a remarkable potential to boost the performance of fundamental models in 2D vision. However, in 3D vision, the over-reliance on Transformer-based backbones and the unordered nature of point clouds have restricted the further development of generative pre-training. In this paper, we propose a novel 3D-to-2D generative pre-training method that is adaptable to any point cloud model. We propose to generate view images from different instructed poses via the cross-attention mechanism as the pre-training scheme. Generating view images has more precise supervision than its point cloud counterpart, thus assisting 3D backbones to have a finer comprehension of the geometrical structure and stereoscopic relations of the point cloud. Experimental results have proved the superiority of our proposed 3D-to-2D generative pre-training over previous pre-training methods. Our method is also effective in boosting the performance of architecture-oriented approaches, achieving state-of-the-art performance when fine-tuning on ScanObjectNN classification and ShapeNetPart segmentation tasks. Code is available at https://github.com/wangzy22/TAP.
    A Self-Adaptive Penalty Method for Integrating Prior Knowledge Constraints into Neural ODEs. (arXiv:2307.14940v1 [cs.LG])
    The continuous dynamics of natural systems has been effectively modelled using Neural Ordinary Differential Equations (Neural ODEs). However, for accurate and meaningful predictions, it is crucial that the models follow the underlying rules or laws that govern these systems. In this work, we propose a self-adaptive penalty algorithm for Neural ODEs to enable modelling of constrained natural systems. The proposed self-adaptive penalty function can dynamically adjust the penalty parameters. The explicit introduction of prior knowledge helps to increase the interpretability of Neural ODE -based models. We validate the proposed approach by modelling three natural systems with prior knowledge constraints: population growth, chemical reaction evolution, and damped harmonic oscillator motion. The numerical experiments and a comparison with other penalty Neural ODE approaches and \emph{vanilla} Neural ODE, demonstrate the effectiveness of the proposed self-adaptive penalty algorithm for Neural ODEs in modelling constrained natural systems. Moreover, the self-adaptive penalty approach provides more accurate and robust models with reliable and meaningful predictions.
    Multi-Source Domain Adaptation through Dataset Dictionary Learning in Wasserstein Space. (arXiv:2307.14953v1 [cs.LG])
    This paper seeks to solve Multi-Source Domain Adaptation (MSDA), which aims to mitigate data distribution shifts when transferring knowledge from multiple labeled source domains to an unlabeled target domain. We propose a novel MSDA framework based on dictionary learning and optimal transport. We interpret each domain in MSDA as an empirical distribution. As such, we express each domain as a Wasserstein barycenter of dictionary atoms, which are empirical distributions. We propose a novel algorithm, DaDiL, for learning via mini-batches: (i) atom distributions; (ii) a matrix of barycentric coordinates. Based on our dictionary, we propose two novel methods for MSDA: DaDil-R, based on the reconstruction of labeled samples in the target domain, and DaDiL-E, based on the ensembling of classifiers learned on atom distributions. We evaluate our methods in 3 benchmarks: Caltech-Office, Office 31, and CRWU, where we improved previous state-of-the-art by 3.15%, 2.29%, and 7.71% in classification performance. Finally, we show that interpolations in the Wasserstein hull of learned atoms provide data that can generalize to the target domain.
    Fading memory as inductive bias in residual recurrent networks. (arXiv:2307.14823v1 [cs.LG])
    Residual connections have been proposed as architecture-based inductive bias to mitigate the problem of exploding and vanishing gradients and increase task performance in both feed-forward and recurrent networks (RNNs) when trained with the backpropagation algorithm. Yet, little is known about how residual connections in RNNs influence their dynamics and fading memory properties. Here, we introduce weakly coupled residual recurrent networks (WCRNNs) in which residual connections result in well-defined Lyapunov exponents and allow for studying properties of fading memory. We investigate how the residual connections of WCRNNs influence their performance, network dynamics, and memory properties on a set of benchmark tasks. We show that several distinct forms of residual connections yield effective inductive biases that result in increased network expressivity. In particular, residual connections that (i) result in network dynamics at the proximity of the edge of chaos, (ii) allow networks to capitalize on characteristic spectral properties of the data, and (iii) result in heterogeneous memory properties are shown to increase practical expressivity. In addition, we demonstrate how our results can be extended to non-linear residuals and introduce a weakly coupled residual initialization scheme that can be used for Elman RNNs
    PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback. (arXiv:2307.14936v1 [cs.CL])
    Large Language Models for Code (Code LLM) are flourishing. New and powerful models are released on a weekly basis, demonstrating remarkable performance on the code generation task. Various approaches have been proposed to boost the code generation performance of pre-trained Code LLMs, such as supervised fine-tuning, instruction tuning, reinforcement learning, etc. In this paper, we propose a novel RRTF (Rank Responses to align Test&Teacher Feedback) framework, which can effectively and efficiently boost pre-trained large language models for code generation. Under this framework, we present PanGu-Coder2, which achieves 62.20% pass@1 on the OpenAI HumanEval benchmark. Furthermore, through an extensive evaluation on CoderEval and LeetCode benchmarks, we show that PanGu-Coder2 consistently outperforms all previous Code LLMs.
    CodeLens: An Interactive Tool for Visualizing Code Representations. (arXiv:2307.14902v1 [cs.SE])
    Representing source code in a generic input format is crucial to automate software engineering tasks, e.g., applying machine learning algorithms to extract information. Visualizing code representations can further enable human experts to gain an intuitive insight into the code. Unfortunately, as of today, there is no universal tool that can simultaneously visualise different types of code representations. In this paper, we introduce a tool, CodeLens, which provides a visual interaction environment that supports various representation methods and helps developers understand and explore them. CodeLens is designed to support multiple programming languages, such as Java, Python, and JavaScript, and four types of code representations, including sequence of tokens, abstract syntax tree (AST), data flow graph (DFG), and control flow graph (CFG). By using CodeLens, developers can quickly visualize the specific code representation and also obtain the represented inputs for models of code. The Web-based interface of CodeLens is available at this http URL The demonstration video can be found at this http URL
    Verifiable Feature Attributions: A Bridge between Post Hoc Explainability and Inherent Interpretability. (arXiv:2307.15007v1 [cs.LG])
    With the increased deployment of machine learning models in various real-world applications, researchers and practitioners alike have emphasized the need for explanations of model behaviour. To this end, two broad strategies have been outlined in prior literature to explain models. Post hoc explanation methods explain the behaviour of complex black-box models by highlighting features that are critical to model predictions; however, prior work has shown that these explanations may not be faithful, and even more concerning is our inability to verify them. Specifically, it is nontrivial to evaluate if a given attribution is correct with respect to the underlying model. Inherently interpretable models, on the other hand, circumvent these issues by explicitly encoding explanations into model architecture, meaning their explanations are naturally faithful and verifiable, but they often exhibit poor predictive performance due to their limited expressive power. In this work, we aim to bridge the gap between the aforementioned strategies by proposing Verifiability Tuning (VerT), a method that transforms black-box models into models that naturally yield faithful and verifiable feature attributions. We begin by introducing a formal theoretical framework to understand verifiability and show that attributions produced by standard models cannot be verified. We then leverage this framework to propose a method to build verifiable models and feature attributions out of fully trained black-box models. Finally, we perform extensive experiments on semi-synthetic and real-world datasets, and show that VerT produces models that (1) yield explanations that are correct and verifiable and (2) are faithful to the original black-box models they are meant to explain.
    How to Scale Your EMA. (arXiv:2307.13813v2 [stat.ML] UPDATED)
    Preserving training dynamics across batch sizes is an important tool for practical machine learning as it enables the trade-off between batch size and wall-clock time. This trade-off is typically enabled by a scaling rule, for example, in stochastic gradient descent, one should scale the learning rate linearly with the batch size. Another important tool for practical machine learning is the model Exponential Moving Average (EMA), which is a model copy that does not receive gradient information, but instead follows its target model with some momentum. This model EMA can improve the robustness and generalization properties of supervised learning, stabilize pseudo-labeling, and provide a learning signal for Self-Supervised Learning (SSL). Prior works have treated the model EMA separately from optimization, leading to different training dynamics across batch sizes and lower model performance. In this work, we provide a scaling rule for optimization in the presence of model EMAs and demonstrate its validity across a range of architectures, optimizers, and data modalities. We also show the rule's validity where the model EMA contributes to the optimization of the target model, enabling us to train EMA-based pseudo-labeling and SSL methods at small and large batch sizes. For SSL, we enable training of BYOL up to batch size 24,576 without sacrificing performance, optimally a 6$\times$ wall-clock time reduction.
    Samplable Anonymous Aggregation for Private Federated Data Analysis. (arXiv:2307.15017v1 [cs.CR])
    We revisit the problem of designing scalable protocols for private statistics and private federated learning when each device holds its private data. Our first contribution is to propose a simple primitive that allows for efficient implementation of several commonly used algorithms, and allows for privacy accounting that is close to that in the central setting without requiring the strong trust assumptions it entails. Second, we propose a system architecture that implements this primitive and perform a security analysis of the proposed system.
    MCPA: Multi-scale Cross Perceptron Attention Network for 2D Medical Image Segmentation. (arXiv:2307.14588v1 [eess.IV])
    The UNet architecture, based on Convolutional Neural Networks (CNN), has demonstrated its remarkable performance in medical image analysis. However, it faces challenges in capturing long-range dependencies due to the limited receptive fields and inherent bias of convolutional operations. Recently, numerous transformer-based techniques have been incorporated into the UNet architecture to overcome this limitation by effectively capturing global feature correlations. However, the integration of the Transformer modules may result in the loss of local contextual information during the global feature fusion process. To overcome these challenges, we propose a 2D medical image segmentation model called Multi-scale Cross Perceptron Attention Network (MCPA). The MCPA consists of three main components: an encoder, a decoder, and a Cross Perceptron. The Cross Perceptron first captures the local correlations using multiple Multi-scale Cross Perceptron modules, facilitating the fusion of features across scales. The resulting multi-scale feature vectors are then spatially unfolded, concatenated, and fed through a Global Perceptron module to model global dependencies. Furthermore, we introduce a Progressive Dual-branch Structure to address the semantic segmentation of the image involving finer tissue structures. This structure gradually shifts the segmentation focus of MCPA network training from large-scale structural features to more sophisticated pixel-level features. We evaluate our proposed MCPA model on several publicly available medical image datasets from different tasks and devices, including the open large-scale dataset of CT (Synapse), MRI (ACDC), fundus camera (DRIVE, CHASE_DB1, HRF), and OCTA (ROSE). The experimental results show that our MCPA model achieves state-of-the-art performance. The code is available at https://github.com/simonustc/MCPA-for-2D-Medical-Image-Segmentation.
    Self-Contrastive Graph Diffusion Network. (arXiv:2307.14613v1 [cs.LG])
    Augmentation techniques and sampling strategies are crucial in contrastive learning, but in most existing works, augmentation techniques require careful design, and their sampling strategies can only capture a small amount of intrinsic supervision information. Additionally, the existing methods require complex designs to obtain two different representations of the data. To overcome these limitations, we propose a novel framework called the Self-Contrastive Graph Diffusion Network (SCGDN). Our framework consists of two main components: the Attentional Module (AttM) and the Diffusion Module (DiFM). AttM aggregates higher-order structure and feature information to get an excellent embedding, while DiFM balances the state of each node in the graph through Laplacian diffusion learning and allows the cooperative evolution of adjacency and feature information in the graph. Unlike existing methodologies, SCGDN is an augmentation-free approach that avoids "sampling bias" and semantic drift, without the need for pre-training. We conduct a high-quality sampling of samples based on structure and feature information. If two nodes are neighbors, they are considered positive samples of each other. If two disconnected nodes are also unrelated on $k$NN graph, they are considered negative samples for each other. The contrastive objective reasonably uses our proposed sampling strategies, and the redundancy reduction term minimizes redundant information in the embedding and can well retain more discriminative information. In this novel framework, the graph self-contrastive learning paradigm gives expression to a powerful force. SCGDN effectively balances between preserving high-order structure information and avoiding overfitting. The results manifest that SCGDN can consistently generate outperformance over both the contrastive methods and the classical methods.
    Function Value Learning: Adaptive Learning Rates Based on the Polyak Stepsize and Function Splitting in ERM. (arXiv:2307.14528v1 [cs.LG])
    Here we develop variants of SGD (stochastic gradient descent) with an adaptive step size that make use of the sampled loss values. In particular, we focus on solving a finite sum-of-terms problem, also known as empirical risk minimization. We first detail an idealized adaptive method called $\texttt{SPS}_+$ that makes use of the sampled loss values and assumes knowledge of the sampled loss at optimality. This $\texttt{SPS}_+$ is a minor modification of the SPS (Stochastic Polyak Stepsize) method, where the step size is enforced to be positive. We then show that $\texttt{SPS}_+$ achieves the best known rates of convergence for SGD in the Lipschitz non-smooth. We then move onto to develop $\texttt{FUVAL}$, a variant of $\texttt{SPS}_+$ where the loss values at optimality are gradually learned, as opposed to being given. We give three viewpoints of $\texttt{FUVAL}$, as a projection based method, as a variant of the prox-linear method, and then as a particular online SGD method. We then present a convergence analysis of $\texttt{FUVAL}$ and experimental results. The shortcomings of our work is that the convergence analysis of $\texttt{FUVAL}$ shows no advantage over SGD. Another shortcomming is that currently only the full batch version of $\texttt{FUVAL}$ shows a minor advantages of GD (Gradient Descent) in terms of sensitivity to the step size. The stochastic version shows no clear advantage over SGD. We conjecture that large mini-batches are required to make $\texttt{FUVAL}$ competitive. Currently the new $\texttt{FUVAL}$ method studied in this paper does not offer any clear theoretical or practical advantage. We have chosen to make this draft available online nonetheless because of some of the analysis techniques we use, such as the non-smooth analysis of $\texttt{SPS}_+$, and also to show an apparently interesting approach that currently does not work.
    A LLM Assisted Exploitation of AI-Guardian. (arXiv:2307.15008v1 [cs.CR])
    Large language models (LLMs) are now highly capable at a diverse range of tasks. This paper studies whether or not GPT-4, one such LLM, is capable of assisting researchers in the field of adversarial machine learning. As a case study, we evaluate the robustness of AI-Guardian, a recent defense to adversarial examples published at IEEE S&P 2023, a top computer security conference. We completely break this defense: the proposed scheme does not increase robustness compared to an undefended baseline. We write none of the code to attack this model, and instead prompt GPT-4 to implement all attack algorithms following our instructions and guidance. This process was surprisingly effective and efficient, with the language model at times producing code from ambiguous instructions faster than the author of this paper could have done. We conclude by discussing (1) the warning signs present in the evaluation that suggested to us AI-Guardian would be broken, and (2) our experience with designing attacks and performing novel research using the most recent advances in language modeling.
    Bug Characterization in Machine Learning-based Systems. (arXiv:2307.14512v1 [cs.SE])
    Rapid growth of applying Machine Learning (ML) in different domains, especially in safety-critical areas, increases the need for reliable ML components, i.e., a software component operating based on ML. Understanding the bugs characteristics and maintenance challenges in ML-based systems can help developers of these systems to identify where to focus maintenance and testing efforts, by giving insights into the most error-prone components, most common bugs, etc. In this paper, we investigate the characteristics of bugs in ML-based software systems and the difference between ML and non-ML bugs from the maintenance viewpoint. We extracted 447,948 GitHub repositories that used one of the three most popular ML frameworks, i.e., TensorFlow, Keras, and PyTorch. After multiple filtering steps, we select the top 300 repositories with the highest number of closed issues. We manually investigate the extracted repositories to exclude non-ML-based systems. Our investigation involved a manual inspection of 386 sampled reported issues in the identified ML-based systems to indicate whether they affect ML components or not. Our analysis shows that nearly half of the real issues reported in ML-based systems are ML bugs, indicating that ML components are more error-prone than non-ML components. Next, we thoroughly examined 109 identified ML bugs to identify their root causes, symptoms, and calculate their required fixing time. The results also revealed that ML bugs have significantly different characteristics compared to non-ML bugs, in terms of the complexity of bug-fixing (number of commits, changed files, and changed lines of code). Based on our results, fixing ML bugs are more costly and ML components are more error-prone, compared to non-ML bugs and non-ML components respectively. Hence, paying a significant attention to the reliability of the ML components is crucial in ML-based systems.
    MVMR-FS : Non-parametric feature selection algorithm based on Maximum inter-class Variation and Minimum Redundancy. (arXiv:2307.14643v1 [cs.LG])
    How to accurately measure the relevance and redundancy of features is an age-old challenge in the field of feature selection. However, existing filter-based feature selection methods cannot directly measure redundancy for continuous data. In addition, most methods rely on manually specifying the number of features, which may introduce errors in the absence of expert knowledge. In this paper, we propose a non-parametric feature selection algorithm based on maximum inter-class variation and minimum redundancy, abbreviated as MVMR-FS. We first introduce supervised and unsupervised kernel density estimation on the features to capture their similarities and differences in inter-class and overall distributions. Subsequently, we present the criteria for maximum inter-class variation and minimum redundancy (MVMR), wherein the inter-class probability distributions are employed to reflect feature relevance and the distances between overall probability distributions are used to quantify redundancy. Finally, we employ an AGA to search for the feature subset that minimizes the MVMR. Compared with ten state-of-the-art methods, MVMR-FS achieves the highest average accuracy and improves the accuracy by 5% to 11%.
    Predictive Maintenance of Armoured Vehicles using Machine Learning Approaches. (arXiv:2307.14453v1 [cs.LG])
    Armoured vehicles are specialized and complex pieces of machinery designed to operate in high-stress environments, often in combat or tactical situations. This study proposes a predictive maintenance-based ensemble system that aids in predicting potential maintenance needs based on sensor data collected from these vehicles. The proposed model's architecture involves various models such as Light Gradient Boosting, Random Forest, Decision Tree, Extra Tree Classifier and Gradient Boosting to predict the maintenance requirements of the vehicles accurately. In addition, K-fold cross validation, along with TOPSIS analysis, is employed to evaluate the proposed ensemble model's stability. The results indicate that the proposed system achieves an accuracy of 98.93%, precision of 99.80% and recall of 99.03%. The algorithm can effectively predict maintenance needs, thereby reducing vehicle downtime and improving operational efficiency. Through comparisons between various algorithms and the suggested ensemble, this study highlights the potential of machine learning-based predictive maintenance solutions.
    Open Problems in Computer Vision for Wilderness SAR and The Search for Patricia Wu-Murad. (arXiv:2307.14527v1 [cs.CV])
    This paper details the challenges in applying two computer vision systems, an EfficientDET supervised learning model and the unsupervised RX spectral classifier, to 98.9 GB of drone imagery from the Wu-Murad wilderness search and rescue (WSAR) effort in Japan and identifies 3 directions for future research. There have been at least 19 proposed approaches and 3 datasets aimed at locating missing persons in drone imagery, but only 3 approaches (2 unsupervised and 1 of an unknown structure) are referenced in the literature as having been used in an actual WSAR operation. Of these proposed approaches, the EfficientDET architecture and the unsupervised spectral RX classifier were selected as the most appropriate for this setting. The EfficientDET model was applied to the HERIDAL dataset and despite achieving performance that is statistically equivalent to the state-of-the-art, the model fails to translate to the real world in terms of false positives (e.g., identifying tree limbs and rocks as people), and false negatives (e.g., failing to identify members of the search team). The poor results in practice for algorithms that showed good results on datasets suggest 3 areas of future research: more realistic datasets for wilderness SAR, computer vision models that are capable of seamlessly handling the variety of imagery that can be collected during actual WSAR operations, and better alignment on performance measures.
    Counterfactual Explanations for Graph Classification Through the Lenses of Density. (arXiv:2307.14849v1 [cs.LG])
    Counterfactual examples have emerged as an effective approach to produce simple and understandable post-hoc explanations. In the context of graph classification, previous work has focused on generating counterfactual explanations by manipulating the most elementary units of a graph, i.e., removing an existing edge, or adding a non-existing one. In this paper, we claim that such language of explanation might be too fine-grained, and turn our attention to some of the main characterizing features of real-world complex networks, such as the tendency to close triangles, the existence of recurring motifs, and the organization into dense modules. We thus define a general density-based counterfactual search framework to generate instance-level counterfactual explanations for graph classifiers, which can be instantiated with different notions of dense substructures. In particular, we show two specific instantiations of this general framework: a method that searches for counterfactual graphs by opening or closing triangles, and a method driven by maximal cliques. We also discuss how the general method can be instantiated to exploit any other notion of dense substructures, including, for instance, a given taxonomy of nodes. We evaluate the effectiveness of our approaches in 7 brain network datasets and compare the counterfactual statements generated according to several widely-used metrics. Results confirm that adopting a semantic-relevant unit of change like density is essential to define versatile and interpretable counterfactual explanation methods.
    Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?. (arXiv:2307.14642v1 [stat.ML])
    We prove that black-box variational inference (BBVI) with control variates, particularly the sticking-the-landing (STL) estimator, converges at a geometric (traditionally called "linear") rate under perfect variational family specification. In particular, we prove a quadratic bound on the gradient variance of the STL estimator, one which encompasses misspecified variational families. Combined with previous works on the quadratic variance condition, this directly implies convergence of BBVI with the use of projected stochastic gradient descent. We also improve existing analysis on the regular closed-form entropy gradient estimators, which enables comparison against the STL estimator and provides explicit non-asymptotic complexity guarantees for both.
    2D-Shapley: A Framework for Fragmented Data Valuation. (arXiv:2306.10473v2 [cs.LG] UPDATED)
    Data valuation -- quantifying the contribution of individual data sources to certain predictive behaviors of a model -- is of great importance to enhancing the transparency of machine learning and designing incentive systems for data sharing. Existing work has focused on evaluating data sources with the shared feature or sample space. How to valuate fragmented data sources of which each only contains partial features and samples remains an open question. We start by presenting a method to calculate the counterfactual of removing a fragment from the aggregated data matrix. Based on the counterfactual calculation, we further propose 2D-Shapley, a theoretical framework for fragmented data valuation that uniquely satisfies some appealing axioms in the fragmented data context. 2D-Shapley empowers a range of new use cases, such as selecting useful data fragments, providing interpretation for sample-wise data values, and fine-grained data issue diagnosis.
    How Good is Google Bard's Visual Understanding? An Empirical Study on Open Challenges. (arXiv:2307.15016v1 [cs.CV])
    Google's Bard has emerged as a formidable competitor to OpenAI's ChatGPT in the field of conversational AI. Notably, Bard has recently been updated to handle visual inputs alongside text prompts during conversations. Given Bard's impressive track record in handling textual inputs, we explore its capabilities in understanding and interpreting visual data (images) conditioned by text questions. This exploration holds the potential to unveil new insights and challenges for Bard and other forthcoming multi-modal Generative models, especially in addressing complex computer vision problems that demand accurate visual and language understanding. Specifically, in this study, we focus on 15 diverse task scenarios encompassing regular, camouflaged, medical, under-water and remote sensing data to comprehensively evaluate Bard's performance. Our primary finding indicates that Bard still struggles in these vision scenarios, highlighting the significant gap in vision-based understanding that needs to be bridged in future developments. We expect that this empirical study will prove valuable in advancing future models, leading to enhanced capabilities in comprehending and interpreting fine-grained visual data. Our project is released on https://github.com/htqin/GoogleBard-VisUnderstand
    A Strategic Framework for Optimal Decisions in Football 1-vs-1 Shot-Taking Situations: An Integrated Approach of Machine Learning, Theory-Based Modeling, and Game Theory. (arXiv:2307.14732v1 [cs.LG])
    Complex interactions between two opposing agents frequently occur in domains of machine learning, game theory, and other application domains. Quantitatively analyzing the strategies involved can provide an objective basis for decision-making. One such critical scenario is shot-taking in football, where decisions, such as whether the attacker should shoot or pass the ball and whether the defender should attempt to block the shot, play a crucial role in the outcome of the game. However, there are currently no effective data-driven and/or theory-based approaches to analyzing such situations. To address this issue, we proposed a novel framework to analyze such scenarios based on game theory, where we estimate the expected payoff with machine learning (ML) models, and additional features for ML models were extracted with a theory-based shot block model. Conventionally, successes or failures (1 or 0) are used as payoffs, while a success shot (goal) is extremely rare in football. Therefore, we proposed the Expected Probability of Shot On Target (xSOT) metric to evaluate players' actions even if the shot results in no goal; this allows for effective differentiation and comparison between different shots and even enables counterfactual shot situation analysis. In our experiments, we have validated the framework by comparing it with baseline and ablated models. Furthermore, we have observed a high correlation between the xSOT and existing metrics. This alignment of information suggests that xSOT provides valuable insights. Lastly, as an illustration, we studied optimal strategies in the World Cup 2022 and analyzed a shot situation in EURO 2020.
    Generative convective parametrization of dry atmospheric boundary layer. (arXiv:2307.14857v1 [physics.flu-dyn])
    Turbulence parametrizations will remain a necessary building block in kilometer-scale Earth system models. In convective boundary layers, where the mean vertical gradients of conserved properties such as potential temperature and moisture are approximately zero, the standard ansatz which relates turbulent fluxes to mean vertical gradients via an eddy diffusivity has to be extended by mass flux parametrizations for the typically asymmetric up- and downdrafts in the atmospheric boundary layer. In this work, we present a parametrization for a dry convective boundary layer based on a generative adversarial network. The model incorporates the physics of self-similar layer growth following from the classical mixed layer theory by Deardorff. This enhances the training data base of the generative machine learning algorithm and thus significantly improves the predicted statistics of the synthetically generated turbulence fields at different heights inside the boundary layer. The algorithm training is based on fully three-dimensional direct numerical simulation data. Differently to stochastic parametrizations, our model is able to predict the highly non-Gaussian transient statistics of buoyancy fluctuations, vertical velocity, and buoyancy flux at different heights thus also capturing the fastest thermals penetrating into the stabilized top region. The results of our generative algorithm agree with standard two-equation or multi-plume stochastic mass-flux schemes. The present parametrization provides additionally the granule-type horizontal organization of the turbulent convection which cannot be obtained in any of the other model closures. Our work paves the way to efficient data-driven convective parametrizations in other natural flows, such as moist convection, upper ocean mixing, or convection in stellar interiors.
    Thinker: Learning to Plan and Act. (arXiv:2307.14993v1 [cs.AI])
    We propose the Thinker algorithm, a novel approach that enables reinforcement learning agents to autonomously interact with and utilize a learned world model. The Thinker algorithm wraps the environment with a world model and introduces new actions designed for interacting with the world model. These model-interaction actions enable agents to perform planning by proposing alternative plans to the world model before selecting a final action to execute in the environment. This approach eliminates the need for hand-crafted planning algorithms by enabling the agent to learn how to plan autonomously and allows for easy interpretation of the agent's plan with visualization. We demonstrate the algorithm's effectiveness through experimental results in the game of Sokoban and the Atari 2600 benchmark, where the Thinker algorithm achieves state-of-the-art performance and competitive results, respectively. Visualizations of agents trained with the Thinker algorithm demonstrate that they have learned to plan effectively with the world model to select better actions. The algorithm's generality opens a new research direction on how a world model can be used in reinforcement learning and how planning can be seamlessly integrated into an agent's decision-making process.
    BubbleML: A Multi-Physics Dataset and Benchmarks for Machine Learning. (arXiv:2307.14623v1 [cs.LG])
    In the field of phase change phenomena, the lack of accessible and diverse datasets suitable for machine learning (ML) training poses a significant challenge. Existing experimental datasets are often restricted, with limited availability and sparse ground truth data, impeding our understanding of this complex multi-physics phenomena. To bridge this gap, we present the BubbleML Dataset(https://github.com/HPCForge/BubbleML) which leverages physics-driven simulations to provide accurate ground truth information for various boiling scenarios, encompassing nucleate pool boiling, flow boiling, and sub-cooled boiling. This extensive dataset covers a wide range of parameters, including varying gravity conditions, flow rates, sub-cooling levels, and wall superheat, comprising 51 simulations. BubbleML is validated against experimental observations and trends, establishing it as an invaluable resource for ML research. Furthermore, we showcase its potential to facilitate exploration of diverse downstream tasks by introducing two benchmarks: (a) optical flow analysis to capture bubble dynamics, and (b) operator networks for learning temperature dynamics. The BubbleML dataset and its benchmarks serve as a catalyst for advancements in ML-driven research on multi-physics phase change phenomena, enabling the development and comparison of state-of-the-art techniques and models.
    Benchmarking Performance of Deep Learning Model for Material Segmentation on Two HPC Systems. (arXiv:2307.14921v1 [cs.PF])
    Performance Benchmarking of HPC systems is an ongoing effort that seeks to provide information that will allow for increased performance and improve the job schedulers that manage these systems. We develop a benchmarking tool that utilizes machine learning models and gathers performance data on GPU-accelerated nodes while they perform material segmentation analysis. The benchmark uses a ML model that has been converted from Caffe to PyTorch using the MMdnn toolkit and the MINC-2500 dataset. Performance data is gathered on two ERDC DSRC systems, Onyx and Vulcanite. The data reveals that while Vulcanite has faster model times in a large number of benchmarks, and it is also more subject to some environmental factors that can cause performances slower than Onyx. In contrast the model times from Onyx are consistent across benchmarks.
    Graph-based Polyphonic Multitrack Music Generation. (arXiv:2307.14928v1 [cs.SD])
    Graphs can be leveraged to model polyphonic multitrack symbolic music, where notes, chords and entire sections may be linked at different levels of the musical hierarchy by tonal and rhythmic relationships. Nonetheless, there is a lack of works that consider graph representations in the context of deep learning systems for music generation. This paper bridges this gap by introducing a novel graph representation for music and a deep Variational Autoencoder that generates the structure and the content of musical graphs separately, one after the other, with a hierarchical architecture that matches the structural priors of music. By separating the structure and content of musical graphs, it is possible to condition generation by specifying which instruments are played at certain times. This opens the door to a new form of human-computer interaction in the context of music co-creation. After training the model on existing MIDI datasets, the experiments show that the model is able to generate appealing short and long musical sequences and to realistically interpolate between them, producing music that is tonally and rhythmically consistent. Finally, the visualization of the embeddings shows that the model is able to organize its latent space in accordance with known musical concepts.
    Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classification. (arXiv:2307.14959v1 [cs.CV])
    In the medical field, federated learning commonly deals with highly imbalanced datasets, including skin lesions and gastrointestinal images. Existing federated methods under highly imbalanced datasets primarily focus on optimizing a global model without incorporating the intra-class variations that can arise in medical imaging due to different populations, findings, and scanners. In this paper, we study the inter-client intra-class variations with publicly available self-supervised auxiliary networks. Specifically, we find that employing a shared auxiliary pre-trained model, like MoCo-V2, locally on every client yields consistent divergence measurements. Based on these findings, we derive a dynamic balanced model aggregation via self-supervised priors (MAS) to guide the global model optimization. Fed-MAS can be utilized with different local learning methods for effective model aggregation toward a highly robust and unbiased global model. Our code is available at \url{https://github.com/xmed-lab/Fed-MAS}.
    Training Quantum Boltzmann Machines with Coresets. (arXiv:2307.14459v1 [quant-ph])
    Recent work has proposed and explored using coreset techniques for quantum algorithms that operate on classical data sets to accelerate the applicability of these algorithms on near-term quantum devices. We apply these ideas to Quantum Boltzmann Machines (QBM) where gradient-based steps which require Gibbs state sampling are the main computational bottleneck during training. By using a coreset in place of the full data set, we try to minimize the number of steps needed and accelerate the overall training time. In a regime where computational time on quantum computers is a precious resource, we propose this might lead to substantial practical savings. We evaluate this approach on 6x6 binary images from an augmented bars and stripes data set using a QBM with 36 visible units and 8 hidden units. Using an Inception score inspired metric, we compare QBM training times with and without using coresets.
    Machine Learning based Parameter Sensitivity of Regional Climate Models -- A Case Study of the WRF Model for Heat Extremes over Southeast Australia. (arXiv:2307.14654v1 [physics.ao-ph])
    Heatwaves and bushfires cause substantial impacts on society and ecosystems across the globe. Accurate information of heat extremes is needed to support the development of actionable mitigation and adaptation strategies. Regional climate models are commonly used to better understand the dynamics of these events. These models have very large input parameter sets, and the parameters within the physics schemes substantially influence the model's performance. However, parameter sensitivity analysis (SA) of regional models for heat extremes is largely unexplored. Here, we focus on the southeast Australian region, one of the global hotspots of heat extremes. In southeast Australia Weather Research and Forecasting (WRF) model is the widely used regional model to simulate extreme weather events across the region. Hence in this study, we focus on the sensitivity of WRF model parameters to surface meteorological variables such as temperature, relative humidity, and wind speed during two extreme heat events over southeast Australia. Due to the presence of multiple parameters and their complex relationship with output variables, a machine learning (ML) surrogate-based global sensitivity analysis method is considered for the SA. The ML surrogate-based Sobol SA is used to identify the sensitivity of 24 adjustable parameters in seven different physics schemes of the WRF model. Results show that out of these 24, only three parameters, namely the scattering tuning parameter, multiplier of saturated soil water content, and profile shape exponent in the momentum diffusivity coefficient, are important for the considered meteorological variables. These SA results are consistent for the two different extreme heat events. Further, we investigated the physical significance of sensitive parameters. This study's results will help in further optimising WRF parameters to improve model simulation.
    Prediction of wind turbines power with physics-informed neural networks and evidential uncertainty quantification. (arXiv:2307.14675v1 [cs.LG])
    The ever-growing use of wind energy makes necessary the optimization of turbine operations through pitch angle controllers and their maintenance with early fault detection. It is crucial to have accurate and robust models imitating the behavior of wind turbines, especially to predict the generated power as a function of the wind speed. Existing empirical and physics-based models have limitations in capturing the complex relations between the input variables and the power, aggravated by wind variability. Data-driven methods offer new opportunities to enhance wind turbine modeling of large datasets by improving accuracy and efficiency. In this study, we used physics-informed neural networks to reproduce historical data coming from 4 turbines in a wind farm, while imposing certain physical constraints to the model. The developed models for regression of the power, torque, and power coefficient as output variables showed great accuracy for both real data and physical equations governing the system. Lastly, introducing an efficient evidential layer provided uncertainty estimations of the predictions, proved to be consistent with the absolute error, and made possible the definition of a confidence interval in the power curve.
    Robust Assignment of Labels for Active Learning with Sparse and Noisy Annotations. (arXiv:2307.14380v1 [cs.LG])
    Supervised classification algorithms are used to solve a growing number of real-life problems around the globe. Their performance is strictly connected with the quality of labels used in training. Unfortunately, acquiring good-quality annotations for many tasks is infeasible or too expensive to be done in practice. To tackle this challenge, active learning algorithms are commonly employed to select only the most relevant data for labeling. However, this is possible only when the quality and quantity of labels acquired from experts are sufficient. Unfortunately, in many applications, a trade-off between annotating individual samples by multiple annotators to increase label quality vs. annotating new samples to increase the total number of labeled instances is necessary. In this paper, we address the issue of faulty data annotations in the context of active learning. In particular, we propose two novel annotation unification algorithms that utilize unlabeled parts of the sample space. The proposed methods require little to no intersection between samples annotated by different experts. Our experiments on four public datasets indicate the robustness and superiority of the proposed methods in both, the estimation of the annotator's reliability, and the assignment of actual labels, against the state-of-the-art algorithms and the simple majority voting.
    HUTFormer: Hierarchical U-Net Transformer for Long-Term Traffic Forecasting. (arXiv:2307.14596v1 [cs.LG])
    Traffic forecasting, which aims to predict traffic conditions based on historical observations, has been an enduring research topic and is widely recognized as an essential component of intelligent transportation. Recent proposals on Spatial-Temporal Graph Neural Networks (STGNNs) have made significant progress by combining sequential models with graph convolution networks. However, due to high complexity issues, STGNNs only focus on short-term traffic forecasting, e.g., 1-hour forecasting, while ignoring more practical long-term forecasting. In this paper, we make the first attempt to explore long-term traffic forecasting, e.g., 1-day forecasting. To this end, we first reveal its unique challenges in exploiting multi-scale representations. Then, we propose a novel Hierarchical U-net TransFormer (HUTFormer) to address the issues of long-term traffic forecasting. HUTFormer consists of a hierarchical encoder and decoder to jointly generate and utilize multi-scale representations of traffic data. Specifically, for the encoder, we propose window self-attention and segment merging to extract multi-scale representations from long-term traffic data. For the decoder, we design a cross-scale attention mechanism to effectively incorporate multi-scale representations. In addition, HUTFormer employs an efficient input embedding strategy to address the complexity issues. Extensive experiments on four traffic datasets show that the proposed HUTFormer significantly outperforms state-of-the-art traffic forecasting and long time series forecasting baselines.
    Rapid and Scalable Bayesian AB Testing. (arXiv:2307.14628v1 [cs.LG])
    AB testing aids business operators with their decision making, and is considered the gold standard method for learning from data to improve digital user experiences. However, there is usually a gap between the requirements of practitioners, and the constraints imposed by the statistical hypothesis testing methodologies commonly used for analysis of AB tests. These include the lack of statistical power in multivariate designs with many factors, correlations between these factors, the need of sequential testing for early stopping, and the inability to pool knowledge from past tests. Here, we propose a solution that applies hierarchical Bayesian estimation to address the above limitations. In comparison to current sequential AB testing methodology, we increase statistical power by exploiting correlations between factors, enabling sequential testing and progressive early stopping, without incurring excessive false positive risk. We also demonstrate how this methodology can be extended to enable the extraction of composite global learnings from past AB tests, to accelerate future tests. We underpin our work with a solid theoretical framework that articulates the value of hierarchical estimation. We demonstrate its utility using both numerical simulations and a large set of real-world AB tests. Together, these results highlight the practical value of our approach for statistical inference in the technology industry.
    Bipartite Ranking Fairness through a Model Agnostic Ordering Adjustment. (arXiv:2307.14668v1 [cs.LG])
    Algorithmic fairness has been a serious concern and received lots of interest in machine learning community. In this paper, we focus on the bipartite ranking scenario, where the instances come from either the positive or negative class and the goal is to learn a ranking function that ranks positive instances higher than negative ones. While there could be a trade-off between fairness and performance, we propose a model agnostic post-processing framework xOrder for achieving fairness in bipartite ranking and maintaining the algorithm classification performance. In particular, we optimize a weighted sum of the utility as identifying an optimal warping path across different protected groups and solve it through a dynamic programming process. xOrder is compatible with various classification models and ranking fairness metrics, including supervised and unsupervised fairness metrics. In addition to binary groups, xOrder can be applied to multiple protected groups. We evaluate our proposed algorithm on four benchmark data sets and two real-world patient electronic health record repositories. xOrder consistently achieves a better balance between the algorithm utility and ranking fairness on a variety of datasets with different metrics. From the visualization of the calibrated ranking scores, xOrder mitigates the score distribution shifts of different groups compared with baselines. Moreover, additional analytical results verify that xOrder achieves a robust performance when faced with fewer samples and a bigger difference between training and testing ranking score distributions.
    Complete and separate: Conditional separation with missing target source attribute completion. (arXiv:2307.14609v1 [cs.SD])
    Recent approaches in source separation leverage semantic information about their input mixtures and constituent sources that when used in conditional separation models can achieve impressive performance. Most approaches along these lines have focused on simple descriptions, which are not always useful for varying types of input mixtures. In this work, we present an approach in which a model, given an input mixture and partial semantic information about a target source, is trained to extract additional semantic data. We then leverage this pre-trained model to improve the separation performance of an uncoupled multi-conditional separation network. Our experiments demonstrate that the separation performance of this multi-conditional model is significantly improved, approaching the performance of an oracle model with complete semantic information. Furthermore, our approach achieves performance levels that are comparable to those of the best performing specialized single conditional models, thus providing an easier to use alternative.
    Understanding Silent Failures in Medical Image Classification. (arXiv:2307.14729v1 [eess.IV])
    To ensure the reliable use of classification systems in medical applications, it is crucial to prevent silent failures. This can be achieved by either designing classifiers that are robust enough to avoid failures in the first place, or by detecting remaining failures using confidence scoring functions (CSFs). A predominant source of failures in image classification is distribution shifts between training data and deployment data. To understand the current state of silent failure prevention in medical imaging, we conduct the first comprehensive analysis comparing various CSFs in four biomedical tasks and a diverse range of distribution shifts. Based on the result that none of the benchmarked CSFs can reliably prevent silent failures, we conclude that a deeper understanding of the root causes of failures in the data is required. To facilitate this, we introduce SF-Visuals, an interactive analysis tool that uses latent space clustering to visualize shifts and failures. On the basis of various examples, we demonstrate how this tool can help researchers gain insight into the requirements for safe application of classification systems in the medical domain. The open-source benchmark and tool are at: https://github.com/IML-DKFZ/sf-visuals.
    Prot2Text: Multimodal Protein's Function Generation with GNNs and Transformers. (arXiv:2307.14367v1 [q-bio.QM])
    The complex nature of big biological systems pushed some scientists to classify its understanding under the inconceivable missions. Different leveled challenges complicated this task, one of is the prediction of a protein's function. In recent years, significant progress has been made in this field through the development of various machine learning approaches. However, most existing methods formulate the task as a multi-classification problem, i.e assigning predefined labels to proteins. In this work, we propose a novel approach, \textbf{Prot2Text}, which predicts a protein function's in a free text style, moving beyond the conventional binary or categorical classifications. By combining Graph Neural Networks(GNNs) and Large Language Models(LLMs), in an encoder-decoder framework, our model effectively integrates diverse data types including proteins' sequences, structures, and textual annotations. This multimodal approach allows for a holistic representation of proteins' functions, enabling the generation of detailed and accurate descriptions. To evaluate our model, we extracted a multimodal protein dataset from SwissProt, and demonstrate empirically the effectiveness of Prot2Text. These results highlight the transformative impact of multimodal models, specifically the fusion of GNNs and LLMs, empowering researchers with powerful tools for more accurate prediction of proteins' functions. The code, the models and a demo will be publicly released.
    Synergies Between Federated Learning and O-RAN: Towards an Elastic Virtualized Architecture for Multiple Distributed Machine Learning Services. (arXiv:2305.02109v2 [cs.NI] UPDATED)
    Federated learning (FL) is the most popular distributed machine learning technique. However, implementation of FL over modern wireless networks faces key challenges caused by (i) dynamics of the network conditions and (ii) the coexistence of multiple FL services/tasks and other network services in the system, which are not jointly considered in prior works. Motivated by these challenges, we introduce a generic FL paradigm over NextG networks, called dynamic multi-service FL (DMS-FL). We identify three unexplored design considerations in DMS-FL: (i) FL service operator accumulation, (ii) wireless resource fragmentation, and (iii) signal strength fluctuations. We take the first steps towards addressing these design considerations by proposing a novel distributed ML architecture called elastic virtualized FL (EV-FL). EV-FL unleashes the full potential of Open RAN (O-RAN) systems and introduces an elastic resource provisioning methodology to execute FL services. It further constitutes a multi-time-scale FL management system that introduces three dimensions into existing FL architectures: (i) virtualization, (ii) scalability, and (iii) elasticity. Through investigating EV-FL, we reveal a series of open research directions for future work. We finally simulate EV-FL to demonstrate its potential in saving wireless resources and increasing fairness among FL services.
    A Predictive Model of Digital Information Engagement: Forecasting User Engagement With English Words by Incorporating Cognitive Biases, Computational Linguistics and Natural Language Processing. (arXiv:2307.14500v1 [cs.HC])
    This study introduces and empirically tests a novel predictive model for digital information engagement (IE) - the READ model, an acronym for the four pivotal attributes of engaging information: Representativeness, Ease-of-use, Affect, and Distribution. Conceptualized within the theoretical framework of Cumulative Prospect Theory, the model integrates key cognitive biases with computational linguistics and natural language processing to develop a multidimensional perspective on information engagement. A rigorous testing protocol was implemented, involving 50 randomly selected pairs of synonymous words (100 words in total) from the WordNet database. These words' engagement levels were evaluated through a large-scale online survey (n = 80,500) to derive empirical IE metrics. The READ attributes for each word were then computed and their predictive efficacy examined. The findings affirm the READ model's robustness, accurately predicting a word's IE level and distinguishing the more engaging word from a pair of synonyms with an 84% accuracy rate. The READ model's potential extends across various domains, including business, education, government, and healthcare, where it could enhance content engagement and inform AI language model development and generative text work. Future research should address the model's scalability and adaptability across different domains and languages, thereby broadening its applicability and efficacy.
    Controlling the Inductive Bias of Wide Neural Networks by Modifying the Kernel's Spectrum. (arXiv:2307.14531v1 [cs.LG])
    Wide neural networks are biased towards learning certain functions, influencing both the rate of convergence of gradient descent (GD) and the functions that are reachable with GD in finite training time. As such, there is a great need for methods that can modify this bias according to the task at hand. To that end, we introduce Modified Spectrum Kernels (MSKs), a novel family of constructed kernels that can be used to approximate kernels with desired eigenvalues for which no closed form is known. We leverage the duality between wide neural networks and Neural Tangent Kernels and propose a preconditioned gradient descent method, which alters the trajectory of GD. As a result, this allows for a polynomial and, in some cases, exponential training speedup without changing the final solution. Our method is both computationally efficient and simple to implement.
    Spatial-Frequency U-Net for Denoising Diffusion Probabilistic Models. (arXiv:2307.14648v1 [cs.CV])
    In this paper, we study the denoising diffusion probabilistic model (DDPM) in wavelet space, instead of pixel space, for visual synthesis. Considering the wavelet transform represents the image in spatial and frequency domains, we carefully design a novel architecture SFUNet to effectively capture the correlation for both domains. Specifically, in the standard denoising U-Net for pixel data, we supplement the 2D convolutions and spatial-only attention layers with our spatial frequency-aware convolution and attention modules to jointly model the complementary information from spatial and frequency domains in wavelet data. Our new architecture can be used as a drop-in replacement to the pixel-based network and is compatible with the vanilla DDPM training process. By explicitly modeling the wavelet signals, we find our model is able to generate images with higher quality on CIFAR-10, FFHQ, LSUN-Bedroom, and LSUN-Church datasets, than the pixel-based counterpart.
    HyperFed: Hyperbolic Prototypes Exploration with Consistent Aggregation for Non-IID Data in Federated Learning. (arXiv:2307.14384v1 [cs.LG])
    Federated learning (FL) collaboratively models user data in a decentralized way. However, in the real world, non-identical and independent data distributions (non-IID) among clients hinder the performance of FL due to three issues, i.e., (1) the class statistics shifting, (2) the insufficient hierarchical information utilization, and (3) the inconsistency in aggregating clients. To address the above issues, we propose HyperFed which contains three main modules, i.e., hyperbolic prototype Tammes initialization (HPTI), hyperbolic prototype learning (HPL), and consistent aggregation (CA). Firstly, HPTI in the server constructs uniformly distributed and fixed class prototypes, and shares them with clients to match class statistics, further guiding consistent feature representation for local clients. Secondly, HPL in each client captures the hierarchical information in local data with the supervision of shared class prototypes in the hyperbolic model space. Additionally, CA in the server mitigates the impact of the inconsistent deviations from clients to server. Extensive studies of four datasets prove that HyperFed is effective in enhancing the performance of FL under the non-IID set.
    Fact-Checking of AI-Generated Reports. (arXiv:2307.14634v1 [cs.AI])
    With advances in generative artificial intelligence (AI), it is now possible to produce realistic-looking automated reports for preliminary reads of radiology images. This can expedite clinical workflows, improve accuracy and reduce overall costs. However, it is also well-known that such models often hallucinate, leading to false findings in the generated reports. In this paper, we propose a new method of fact-checking of AI-generated reports using their associated images. Specifically, the developed examiner differentiates real and fake sentences in reports by learning the association between an image and sentences describing real or potentially fake findings. To train such an examiner, we first created a new dataset of fake reports by perturbing the findings in the original ground truth radiology reports associated with images. Text encodings of real and fake sentences drawn from these reports are then paired with image encodings to learn the mapping to real/fake labels. The utility of such an examiner is demonstrated for verifying automatically generated reports by detecting and removing fake sentences. Future generative AI approaches can use the resulting tool to validate their reports leading to a more responsible use of AI in expediting clinical workflows.
    NSA: Naturalistic Support Artifact to Boost Network Confidence. (arXiv:2307.14917v1 [cs.CV])
    Visual AI systems are vulnerable to natural and synthetic physical corruption in the real-world. Such corruption often arises unexpectedly and alters the model's performance. In recent years, the primary focus has been on adversarial attacks. However, natural corruptions (e.g., snow, fog, dust) are an omnipresent threat to visual AI systems and should be considered equally important. Many existing works propose interesting solutions to train robust models against natural corruption. These works either leverage image augmentations, which come with the additional cost of model training, or place suspicious patches in the scene to design unadversarial examples. In this work, we propose the idea of naturalistic support artifacts (NSA) for robust prediction. The NSAs are shown to be beneficial in scenarios where model parameters are inaccessible and adding artifacts in the scene is feasible. The NSAs are natural looking objects generated through artifact training using DC-GAN to have high visual fidelity in the scene. We test against natural corruptions on the Imagenette dataset and observe the improvement in prediction confidence score by four times. We also demonstrate NSA's capability to increase adversarial accuracy by 8\% on average. Lastly, we qualitatively analyze NSAs using saliency maps to understand how they help improve prediction confidence.
    DBGSA: A Novel Data Adaptive Bregman Clustering Algorithm. (arXiv:2307.14375v1 [cs.LG])
    With the development of Big data technology, data analysis has become increasingly important. Traditional clustering algorithms such as K-means are highly sensitive to the initial centroid selection and perform poorly on non-convex datasets. In this paper, we address these problems by proposing a data-driven Bregman divergence parameter optimization clustering algorithm (DBGSA), which combines the Universal Gravitational Algorithm to bring similar points closer in the dataset. We construct a gravitational coefficient equation with a special property that gradually reduces the influence factor as the iteration progresses. Furthermore, we introduce the Bregman divergence generalized power mean information loss minimization to identify cluster centers and build a hyperparameter identification optimization model, which effectively solves the problems of manual adjustment and uncertainty in the improved dataset. Extensive experiments are conducted on four simulated datasets and six real datasets. The results demonstrate that DBGSA significantly improves the accuracy of various clustering algorithms by an average of 63.8\% compared to other similar approaches like enhanced clustering algorithms and improved datasets. Additionally, a three-dimensional grid search was established to compare the effects of different parameter values within threshold conditions, and it was discovered the parameter set provided by our model is optimal. This finding provides strong evidence of the high accuracy and robustness of the algorithm.
    Prediction of depression status in college students using a Naive Bayes classifier based machine learning model. (arXiv:2307.14371v1 [cs.LG])
    This study presents a machine learning model based on the Naive Bayes classifier for predicting the level of depression in university students, the objective was to improve prediction accuracy using a machine learning model involving 70% training data and 30% validation data based on the Naive Bayes classifier, the collected data includes factors associated with depression from 519 university students, the results showed an accuracy of 78.03%, high sensitivity in detecting positive cases of depression, especially at moderate and severe levels, and significant specificity in correctly classifying negative cases, these findings highlight the effectiveness of the model in early detection and treatment of depression, benefiting vulnerable sectors and contributing to the improvement of mental health in the student population.
    Limits to Reservoir Learning. (arXiv:2307.14474v1 [cs.LG])
    In this work, we bound a machine's ability to learn based on computational limitations implied by physicality. We start by considering the information processing capacity (IPC), a normalized measure of the expected squared error of a collection of signals to a complete basis of functions. We use the IPC to measure the degradation under noise of the performance of reservoir computers, a particular kind of recurrent network, when constrained by physical considerations. First, we show that the IPC is at most a polynomial in the system size $n$, even when considering the collection of $2^n$ possible pointwise products of the $n$ output signals. Next, we argue that this degradation implies that the family of functions represented by the reservoir requires an exponential number of samples to learn in the presence of the reservoir's noise. Finally, we conclude with a discussion of the performance of the same collection of $2^n$ functions without noise when being used for binary classification.
    Learned Gridification for Efficient Point Cloud Processing. (arXiv:2307.14354v1 [cs.CV])
    Neural operations that rely on neighborhood information are much more expensive when deployed on point clouds than on grid data due to the irregular distances between points in a point cloud. In a grid, on the other hand, we can compute the kernel only once and reuse it for all query positions. As a result, operations that rely on neighborhood information scale much worse for point clouds than for grid data, specially for large inputs and large neighborhoods. In this work, we address the scalability issue of point cloud methods by tackling its root cause: the irregularity of the data. We propose learnable gridification as the first step in a point cloud processing pipeline to transform the point cloud into a compact, regular grid. Thanks to gridification, subsequent layers can use operations defined on regular grids, e.g., Conv3D, which scale much better than native point cloud methods. We then extend gridification to point cloud to point cloud tasks, e.g., segmentation, by adding a learnable de-gridification step at the end of the point cloud processing pipeline to map the compact, regular grid back to its original point cloud form. Through theoretical and empirical analysis, we show that gridified networks scale better in terms of memory and time than networks directly applied on raw point cloud data, while being able to achieve competitive results. Our code is publicly available at https://github.com/computri/gridifier.
    VISPUR: Visual Aids for Identifying and Interpreting Spurious Associations in Data-Driven Decisions. (arXiv:2307.14448v1 [cs.HC])
    Big data and machine learning tools have jointly empowered humans in making data-driven decisions. However, many of them capture empirical associations that might be spurious due to confounding factors and subgroup heterogeneity. The famous Simpson's paradox is such a phenomenon where aggregated and subgroup-level associations contradict with each other, causing cognitive confusions and difficulty in making adequate interpretations and decisions. Existing tools provide little insights for humans to locate, reason about, and prevent pitfalls of spurious association in practice. We propose VISPUR, a visual analytic system that provides a causal analysis framework and a human-centric workflow for tackling spurious associations. These include a CONFOUNDER DASHBOARD, which can automatically identify possible confounding factors, and a SUBGROUP VIEWER, which allows for the visualization and comparison of diverse subgroup patterns that likely or potentially result in a misinterpretation of causality. Additionally, we propose a REASONING STORYBOARD, which uses a flow-based approach to illustrate paradoxical phenomena, as well as an interactive DECISION DIAGNOSIS panel that helps ensure accountable decision-making. Through an expert interview and a controlled user experiment, our qualitative and quantitative results demonstrate that the proposed "de-paradox" workflow and the designed visual analytic system are effective in helping human users to identify and understand spurious associations, as well as to make accountable causal decisions.
    Piecewise Linear Functions Representable with Infinite Width Shallow ReLU Neural Networks. (arXiv:2307.14373v1 [cs.LG])
    This paper analyzes representations of continuous piecewise linear functions with infinite width, finite cost shallow neural networks using the rectified linear unit (ReLU) as an activation function. Through its integral representation, a shallow neural network can be identified by the corresponding signed, finite measure on an appropriate parameter space. We map these measures on the parameter space to measures on the projective $n$-sphere cross $\mathbb{R}$, allowing points in the parameter space to be bijectively mapped to hyperplanes in the domain of the function. We prove a conjecture of Ongie et al. that every continuous piecewise linear function expressible with this kind of infinite width neural network is expressible as a finite width shallow ReLU neural network.
    Learnable wavelet neural networks for cosmological inference. (arXiv:2307.14362v1 [astro-ph.IM])
    Convolutional neural networks (CNNs) have been shown to both extract more information than the traditional two-point statistics from cosmological fields, and marginalise over astrophysical effects extremely well. However, CNNs require large amounts of training data, which is potentially problematic in the domain of expensive cosmological simulations, and it is difficult to interpret the network. In this work we apply the learnable scattering transform, a kind of convolutional neural network that uses trainable wavelets as filters, to the problem of cosmological inference and marginalisation over astrophysical effects. We present two models based on the scattering transform, one constructed for performance, and one constructed for interpretability, and perform a comparison with a CNN. We find that scattering architectures are able to outperform a CNN, significantly in the case of small training data samples. Additionally we present a lightweight scattering network that is highly interpretable.
    Forecasting, capturing and activation of carbon-dioxide (CO$_2$): Integration of Time Series Analysis, Machine Learning, and Material Design. (arXiv:2307.14374v1 [cs.LG])
    This study provides a comprehensive time series analysis of daily industry-specific, country-wise CO$_2$ emissions from January 2019 to February 2023. The research focuses on the Power, Industry, Ground Transport, Domestic Aviation, and International Aviation sectors in European countries (EU27 & UK, Italy, Germany, Spain) and India, utilizing near-real-time activity data from the Carbon Monitor research initiative. To identify regular emission patterns, the data from the year 2020 is excluded due to the disruptive effects caused by the COVID-19 pandemic. The study then performs a principal component analysis (PCA) to determine the key contributors to CO$_2$ emissions. The analysis reveals that the Power, Industry, and Ground Transport sectors account for a significant portion of the variance in the dataset. A 7-day moving averaged dataset is employed for further analysis to facilitate robust predictions. This dataset captures both short-term and long-term trends and enhances the quality of the data for prediction purposes. The study utilizes Long Short-Term Memory (LSTM) models on the 7-day moving averaged dataset to effectively predict emissions and provide insights for policy decisions, mitigation strategies, and climate change efforts. During the training phase, the stability and convergence of the LSTM models are ensured, which guarantees their reliability in the testing phase. The evaluation of the loss function indicates this reliability. The model achieves high efficiency, as demonstrated by $R^2$ values ranging from 0.8242 to 0.995 for various countries and sectors. Furthermore, there is a proposal for utilizing scandium and boron/aluminium-based thin films as exceptionally efficient materials for capturing CO$_2$ (with a binding energy range from -3.0 to -3.5 eV). These materials are shown to surpass the affinity of graphene and boron nitride sheets in this regard.
    HUGE: Huge Unsupervised Graph Embeddings with TPUs. (arXiv:2307.14490v1 [cs.LG])
    Graphs are a representation of structured data that captures the relationships between sets of objects. With the ubiquity of available network data, there is increasing industrial and academic need to quickly analyze graphs with billions of nodes and trillions of edges. A common first step for network understanding is Graph Embedding, the process of creating a continuous representation of nodes in a graph. A continuous representation is often more amenable, especially at scale, for solving downstream machine learning tasks such as classification, link prediction, and clustering. A high-performance graph embedding architecture leveraging Tensor Processing Units (TPUs) with configurable amounts of high-bandwidth memory is presented that simplifies the graph embedding problem and can scale to graphs with billions of nodes and trillions of edges. We verify the embedding space quality on real and synthetic large-scale datasets.
    Learning to simulate partially known spatio-temporal dynamics with trainable difference operators. (arXiv:2307.14395v1 [cs.LG])
    Recently, using neural networks to simulate spatio-temporal dynamics has received a lot of attention. However, most existing methods adopt pure data-driven black-box models, which have limited accuracy and interpretability. By combining trainable difference operators with black-box models, we propose a new hybrid architecture explicitly embedded with partial prior knowledge of the underlying PDEs named PDE-Net++. Furthermore, we introduce two distinct options called the trainable flipping difference layer (TFDL) and the trainable dynamic difference layer (TDDL) for the difference operators. Numerous numerical experiments have demonstrated that PDE-Net++ has superior prediction accuracy and better extrapolation performance than black-box models.
    The Effect of Spoken Language on Speech Enhancement using Self-Supervised Speech Representation Loss Functions. (arXiv:2307.14502v1 [eess.AS])
    Recent work in the field of speech enhancement (SE) has involved the use of self-supervised speech representations (SSSRs) as feature transformations in loss functions. However, in prior work, very little attention has been paid to the relationship between the language of the audio used to train the self-supervised representation and that used to train the SE system. Enhancement models trained using a loss function which incorporates a self-supervised representation that shares exactly the language of the noisy data used to train the SE system show better performance than those which do not match exactly. This may lead to enhancement systems which are language specific and as such do not generalise well to unseen languages, unlike models trained using traditional spectrogram or time domain loss functions. In this work, SE models are trained and tested on a number of different languages, with self-supervised representations which themselves are trained using different language combinations and with differing network structures as loss function representations. These models are then tested across unseen languages and their performances are analysed. It is found that the training language of the self-supervised representation appears to have a minor effect on enhancement performance, the amount of training data of a particular language, however, greatly affects performance.
    Federated Distributionally Robust Optimization with Non-Convex Objectives: Algorithm and Analysis. (arXiv:2307.14364v1 [math.OC])
    Distributionally Robust Optimization (DRO), which aims to find an optimal decision that minimizes the worst case cost over the ambiguity set of probability distribution, has been widely applied in diverse applications, e.g., network behavior analysis, risk management, etc. However, existing DRO techniques face three key challenges: 1) how to deal with the asynchronous updating in a distributed environment; 2) how to leverage the prior distribution effectively; 3) how to properly adjust the degree of robustness according to different scenarios. To this end, we propose an asynchronous distributed algorithm, named Asynchronous Single-looP alternatIve gRadient projEction (ASPIRE) algorithm with the itErative Active SEt method (EASE) to tackle the federated distributionally robust optimization (FDRO) problem. Furthermore, a new uncertainty set, i.e., constrained D-norm uncertainty set, is developed to effectively leverage the prior distribution and flexibly control the degree of robustness. Finally, our theoretical analysis elucidates that the proposed algorithm is guaranteed to converge and the iteration complexity is also analyzed. Extensive empirical studies on real-world datasets demonstrate that the proposed method can not only achieve fast convergence, and remain robust against data heterogeneity as well as malicious attacks, but also tradeoff robustness with performance.
    What Kinds of Contracts Do ML APIs Need?. (arXiv:2307.14465v1 [cs.SE])
    Recent work has shown that Machine Learning (ML) programs are error-prone and called for contracts for ML code. Contracts, as in the design by contract methodology, help document APIs and aid API users in writing correct code. The question is: what kinds of contracts would provide the most help to API users? We are especially interested in what kinds of contracts help API users catch errors at earlier stages in the ML pipeline. We describe an empirical study of posts on Stack Overflow of the four most often-discussed ML libraries: TensorFlow, Scikit-learn, Keras, and PyTorch. For these libraries, our study extracted 413 informal (English) API specifications. We used these specifications to understand the following questions. What are the root causes and effects behind ML contract violations? Are there common patterns of ML contract violations? When does understanding ML contracts require an advanced level of ML software expertise? Could checking contracts at the API level help detect the violations in early ML pipeline stages? Our key findings are that the most commonly needed contracts for ML APIs are either checking constraints on single arguments of an API or on the order of API calls. The software engineering community could employ existing contract mining approaches to mine these contracts to promote an increased understanding of ML APIs. We also noted a need to combine behavioral and temporal contract mining approaches. We report on categories of required ML contracts, which may help designers of contract languages.
    Unsupervised reconstruction of accelerated cardiac cine MRI using Neural Fields. (arXiv:2307.14363v1 [eess.IV])
    Cardiac cine MRI is the gold standard for cardiac functional assessment, but the inherently slow acquisition process creates the necessity of reconstruction approaches for accelerated undersampled acquisitions. Several regularization approaches that exploit spatial-temporal redundancy have been proposed to reconstruct undersampled cardiac cine MRI. More recently, methods based on supervised deep learning have been also proposed to further accelerate acquisition and reconstruction. However, these techniques rely on usually large dataset for training, which are not always available. In this work, we propose an unsupervised approach based on implicit neural field representations for cardiac cine MRI (so called NF-cMRI). The proposed method was evaluated in in-vivo undersampled golden-angle radial multi-coil acquisitions for undersampling factors of 26x and 52x, achieving good image quality, and comparable spatial and improved temporal depiction than a state-of-the-art reconstruction technique.
    Optimal Estimation in Mixed-Membership Stochastic Block Models. (arXiv:2307.14530v1 [stat.ML])
    Community detection is one of the most critical problems in modern network science. Its applications can be found in various fields, from protein modeling to social network analysis. Recently, many papers appeared studying the problem of overlapping community detection, where each node of a network may belong to several communities. In this work, we consider Mixed-Membership Stochastic Block Model (MMSB) first proposed by Airoldi et al. (2008). MMSB provides quite a general setting for modeling overlapping community structure in graphs. The central question of this paper is to reconstruct relations between communities given an observed network. We compare different approaches and establish the minimax lower bound on the estimation error. Then, we propose a new estimator that matches this lower bound. Theoretical results are proved under fairly general conditions on the considered model. Finally, we illustrate the theory in a series of experiments.
    A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shot. (arXiv:2307.14397v1 [cs.CV])
    In machine learning, generative modeling aims to learn to generate new data statistically similar to the training data distribution. In this paper, we survey learning generative models under limited data, few shots and zero shot, referred to as Generative Modeling under Data Constraint (GM-DC). This is an important topic when data acquisition is challenging, e.g. healthcare applications. We discuss background, challenges, and propose two taxonomies: one on GM-DC tasks and another on GM-DC approaches. Importantly, we study interactions between different GM-DC tasks and approaches. Furthermore, we highlight research gaps, research trends, and potential avenues for future exploration. Project website: https://gmdc-survey.github.io.
    Neural Networks for Scalar Input and Functional Output. (arXiv:2208.05776v2 [stat.ML] UPDATED)
    The regression of a functional response on a set of scalar predictors can be a challenging task, especially if there is a large number of predictors, or the relationship between those predictors and the response is nonlinear. In this work, we propose a solution to this problem: a feed-forward neural network (NN) designed to predict a functional response using scalar inputs. First, we transform the functional response to a finite-dimensional representation and construct an NN that outputs this representation. Then, we propose to modify the output of an NN via the objective function and introduce different objective functions for network training. The proposed models are suited for both regularly and irregularly spaced data, and a roughness penalty can be further applied to control the smoothness of the predicted curve. The difficulty in implementing both those features lies in the definition of objective functions that can be back-propagated. In our experiments, we demonstrate that our model outperforms the conventional function-on-scalar regression model in multiple scenarios while computationally scaling better with the dimension of the predictors.
    From Contextual Data to Newsvendor Decisions: On the Actual Performance of Data-Driven Algorithms. (arXiv:2302.08424v3 [cs.LG] UPDATED)
    In this work, we explore a framework for contextual decision-making to study how the relevance and quantity of past data affects the performance of a data-driven policy. We analyze a contextual Newsvendor problem in which a decision-maker needs to trade-off between an underage and an overage cost in the face of uncertain demand. We consider a setting in which past demands observed under ``close by'' contexts come from close by distributions and analyze the performance of data-driven algorithms through a notion of context-dependent worst-case expected regret. We analyze the broad class of Weighted Empirical Risk Minimization (WERM) policies which weigh past data according to their similarity in the contextual space. This class includes classical policies such as ERM, k-Nearest Neighbors and kernel-based policies. Our main methodological contribution is to characterize exactly the worst-case regret of any WERM policy on any given configuration of contexts. To the best of our knowledge, this provides the first understanding of tight performance guarantees in any contextual decision-making problem, with past literature focusing on upper bounds via concentration inequalities. We instead take an optimization approach, and isolate a structure in the Newsvendor loss function that allows to reduce the infinite-dimensional optimization problem over worst-case distributions to a simple line search. This in turn allows us to unveil fundamental insights that were obfuscated by previous general-purpose bounds. We characterize actual guaranteed performance as a function of the contexts, as well as granular insights on the learning curve of algorithms.
    On the non-efficient PAC learnability of conjunctive queries. (arXiv:2208.10255v2 [cs.DB] UPDATED)
    This note serves three purposes: (i) we provide a self-contained exposition of the fact that conjunctive queries are not efficiently learnable in the Probably-Approximately-Correct (PAC) model, paying clear attention to the complicating fact that this concept class lacks the polynomial-size fitting property, a property that is tacitly assumed in much of the computational learning theory literature; (ii) we establish a strong negative PAC learnability result that applies to many restricted classes of conjunctive queries (CQs), including acyclic CQs for a wide range of notions of "acyclicity"; (iii) we show that CQs (and UCQs) are efficiently PAC learnable with membership queries.
    FLARE: Fingerprinting Deep Reinforcement Learning Agents using Universal Adversarial Masks. (arXiv:2307.14751v1 [cs.LG])
    We propose FLARE, the first fingerprinting mechanism to verify whether a suspected Deep Reinforcement Learning (DRL) policy is an illegitimate copy of another (victim) policy. We first show that it is possible to find non-transferable, universal adversarial masks, i.e., perturbations, to generate adversarial examples that can successfully transfer from a victim policy to its modified versions but not to independently trained policies. FLARE employs these masks as fingerprints to verify the true ownership of stolen DRL policies by measuring an action agreement value over states perturbed via such masks. Our empirical evaluations show that FLARE is effective (100% action agreement on stolen copies) and does not falsely accuse independent policies (no false positives). FLARE is also robust to model modification attacks and cannot be easily evaded by more informed adversaries without negatively impacting agent performance. We also show that not all universal adversarial masks are suitable candidates for fingerprints due to the inherent characteristics of DRL policies. The spatio-temporal dynamics of DRL problems and sequential decision-making process make characterizing the decision boundary of DRL policies more difficult, as well as searching for universal masks that capture the geometry of it.
    EdgeConvEns: Convolutional Ensemble Learning for Edge Intelligence. (arXiv:2307.14381v1 [cs.LG])
    Deep edge intelligence aims to deploy deep learning models that demand computationally expensive training in the edge network with limited computational power. Moreover, many deep edge intelligence applications require handling distributed data that cannot be transferred to a central server due to privacy concerns. Decentralized learning methods, such as federated learning, offer solutions where models are learned collectively by exchanging learned weights. However, they often require complex models that edge devices may not handle and multiple rounds of network communication to achieve state-of-the-art performances. This study proposes a convolutional ensemble learning approach, coined EdgeConvEns, that facilitates training heterogeneous weak models on edge and learning to ensemble them where data on edge are heterogeneously distributed. Edge models are implemented and trained independently on Field-Programmable Gate Array (FPGA) devices with various computational capacities. Learned data representations are transferred to a central server where the ensemble model is trained with the learned features received from the edge devices to boost the overall prediction performance. Extensive experiments demonstrate that the EdgeConvEns can outperform the state-of-the-art performance with fewer communications and less data in various training scenarios.
    Explainable Disparity Compensation for Efficient Fair Ranking. (arXiv:2307.14366v1 [cs.LG])
    Ranking functions that are used in decision systems often produce disparate results for different populations because of bias in the underlying data. Addressing, and compensating for, these disparate outcomes is a critical problem for fair decision-making. Recent compensatory measures have mostly focused on opaque transformations of the ranking functions to satisfy fairness guarantees or on the use of quotas or set-asides to guarantee a minimum number of positive outcomes to members of underrepresented groups. In this paper we propose easily explainable data-driven compensatory measures for ranking functions. Our measures rely on the generation of bonus points given to members of underrepresented groups to address disparity in the ranking function. The bonus points can be set in advance, and can be combined, allowing for considering the intersections of representations and giving better transparency to stakeholders. We propose efficient sampling-based algorithms to calculate the number of bonus points to minimize disparity. We validate our algorithms using real-world school admissions and recidivism datasets, and compare our results with that of existing fair ranking algorithms.
    Towards Better Generalization with Flexible Representation of Multi-Module Graph Neural Networks. (arXiv:2209.06589v3 [cs.LG] UPDATED)
    Graph neural networks (GNNs) have become compelling models designed to perform learning and inference on graph-structured data. However, little work has been done to understand the fundamental limitations of GNNs for scaling to larger graphs and generalizing to out-of-distribution (OOD) inputs. In this paper, we use a random graph generator to systematically investigate how the graph size and structural properties affect the predictive performance of GNNs. We present specific evidence that the average node degree is a key feature in determining whether GNNs can generalize to unseen graphs, and that the use of multiple node update functions can improve the generalization performance of GNNs when dealing with graphs of multimodal degree distributions. Accordingly, we propose a multi-module GNN framework that allows the network to adapt flexibly to new graphs by generalizing a single canonical nonlinear transformation over aggregated inputs. Our results show that the multi-module GNNs improve the OOD generalization on a variety of inference tasks in the direction of diverse structural features.
    Universal and Transferable Adversarial Attacks on Aligned Language Models. (arXiv:2307.15043v1 [cs.CL])
    Because "out-of-the-box" large language models are capable of generating a great deal of objectionable content, recent work has focused on aligning these models in an attempt to prevent undesirable generation. While there has been some success at circumventing these measures -- so-called "jailbreaks" against LLMs -- these attacks have required significant human ingenuity and are brittle in practice. In this paper, we propose a simple and effective attack method that causes aligned language models to generate objectionable behaviors. Specifically, our approach finds a suffix that, when attached to a wide range of queries for an LLM to produce objectionable content, aims to maximize the probability that the model produces an affirmative response (rather than refusing to answer). However, instead of relying on manual engineering, our approach automatically produces these adversarial suffixes by a combination of greedy and gradient-based search techniques, and also improves over past automatic prompt generation methods. Surprisingly, we find that the adversarial prompts generated by our approach are quite transferable, including to black-box, publicly released LLMs. Specifically, we train an adversarial attack suffix on multiple prompts (i.e., queries asking for many different types of objectionable content), as well as multiple models (in our case, Vicuna-7B and 13B). When doing so, the resulting attack suffix is able to induce objectionable content in the public interfaces to ChatGPT, Bard, and Claude, as well as open source LLMs such as LLaMA-2-Chat, Pythia, Falcon, and others. In total, this work significantly advances the state-of-the-art in adversarial attacks against aligned language models, raising important questions about how such systems can be prevented from producing objectionable information. Code is available at github.com/llm-attacks/llm-attacks.
    Semantic Image Completion and Enhancement using GANs. (arXiv:2307.14748v1 [cs.CV])
    Semantic inpainting or image completion alludes to the task of inferring arbitrary large missing regions in images based on image semantics. Since the prediction of image pixels requires an indication of high-level context, this makes it significantly tougher than image completion, which is often more concerned with correcting data corruption and removing entire objects from the input image. On the other hand, image enhancement attempts to eliminate unwanted noise and blur from the image, along with sustaining most of the image details. Efficient image completion and enhancement model should be able to recover the corrupted and masked regions in images and then refine the image further to increase the quality of the output image. Generative Adversarial Networks (GAN), have turned out to be helpful in picture completion tasks. In this chapter, we will discuss the underlying GAN architecture and how they can be used used for image completion tasks.
    Adversarial Sleeping Bandit Problems with Multiple Plays: Algorithm and Ranking Application. (arXiv:2307.14549v1 [cs.LG])
    This paper presents an efficient algorithm to solve the sleeping bandit with multiple plays problem in the context of an online recommendation system. The problem involves bounded, adversarial loss and unknown i.i.d. distributions for arm availability. The proposed algorithm extends the sleeping bandit algorithm for single arm selection and is guaranteed to achieve theoretical performance with regret upper bounded by $\bigO(kN^2\sqrt{T\log T})$, where $k$ is the number of arms selected per time step, $N$ is the total number of arms, and $T$ is the time horizon.
    Likely, Light, and Accurate Context-Free Clusters-based Trajectory Prediction. (arXiv:2307.14788v1 [cs.LG])
    Autonomous systems in the road transportation network require intelligent mechanisms that cope with uncertainty to foresee the future. In this paper, we propose a multi-stage probabilistic approach for trajectory forecasting: trajectory transformation to displacement space, clustering of displacement time series, trajectory proposals, and ranking proposals. We introduce a new deep feature clustering method, underlying self-conditioned GAN, which copes better with distribution shifts than traditional methods. Additionally, we propose novel distance-based ranking proposals to assign probabilities to the generated trajectories that are more efficient yet accurate than an auxiliary neural network. The overall system surpasses context-free deep generative models in human and road agents trajectory data while performing similarly to point estimators when comparing the most probable trajectory.
    Incrementally-Computable Neural Networks: Efficient Inference for Dynamic Inputs. (arXiv:2307.14988v1 [cs.LG])
    Deep learning often faces the challenge of efficiently processing dynamic inputs, such as sensor data or user inputs. For example, an AI writing assistant is required to update its suggestions in real time as a document is edited. Re-running the model each time is expensive, even with compression techniques like knowledge distillation, pruning, or quantization. Instead, we take an incremental computing approach, looking to reuse calculations as the inputs change. However, the dense connectivity of conventional architectures poses a major obstacle to incremental computation, as even minor input changes cascade through the network and restrict information reuse. To address this, we use vector quantization to discretize intermediate values in the network, which filters out noisy and unnecessary modifications to hidden neurons, facilitating the reuse of their values. We apply this approach to the transformers architecture, creating an efficient incremental inference algorithm with complexity proportional to the fraction of the modified inputs. Our experiments with adapting the OPT-125M pre-trained language model demonstrate comparable accuracy on document classification while requiring 12.1X (median) fewer operations for processing sequences of atomic edits.
    Kernelised Normalising Flows. (arXiv:2307.14839v1 [stat.ML])
    Normalising Flows are generative models characterised by their invertible architecture. However, the requirement of invertibility imposes constraints on their expressiveness, necessitating a large number of parameters and innovative architectural designs to achieve satisfactory outcomes. Whilst flow-based models predominantly rely on neural-network-based transformations for expressive designs, alternative transformation methods have received limited attention. In this work, we present Ferumal flow, a novel kernelised normalising flow paradigm that integrates kernels into the framework. Our results demonstrate that a kernelised flow can yield competitive or superior results compared to neural network-based flows whilst maintaining parameter efficiency. Kernelised flows excel especially in the low-data regime, enabling flexible non-parametric density estimation in applications with sparse data availability.
    Solving Data Quality Problems with Desbordante: a Demo. (arXiv:2307.14935v1 [cs.DB])
    Data profiling is an essential process in modern data-driven industries. One of its critical components is the discovery and validation of complex statistics, including functional dependencies, data constraints, association rules, and others. However, most existing data profiling systems that focus on complex statistics do not provide proper integration with the tools used by contemporary data scientists. This creates a significant barrier to the adoption of these tools in the industry. Moreover, existing systems were not created with industrial-grade workloads in mind. Finally, they do not aim to provide descriptive explanations, i.e. why a given pattern is not found. It is a significant issue as it is essential to understand the underlying reasons for a specific pattern's absence to make informed decisions based on the data. Because of that, these patterns are effectively rest in thin air: their application scope is rather limited, they are rarely used by the broader public. At the same time, as we are going to demonstrate in this presentation, complex statistics can be efficiently used to solve many classic data quality problems. Desbordante is an open-source data profiler that aims to close this gap. It is built with emphasis on industrial application: it is efficient, scalable, resilient to crashes, and provides explanations. Furthermore, it provides seamless Python integration by offloading various costly operations to the C++ core, not only mining. In this demonstration, we show several scenarios that allow end users to solve different data quality problems. Namely, we showcase typo detection, data deduplication, and data anomaly detection scenarios.
    Network Fault-tolerant and Byzantine-resilient Social Learning via Collaborative Hierarchical Non-Bayesian Learning. (arXiv:2307.14952v1 [cs.LG])
    As the network scale increases, existing fully distributed solutions start to lag behind the real-world challenges such as (1) slow information propagation, (2) network communication failures, and (3) external adversarial attacks. In this paper, we focus on hierarchical system architecture and address the problem of non-Bayesian learning over networks that are vulnerable to communication failures and adversarial attacks. On network communication, we consider packet-dropping link failures. We first propose a hierarchical robust push-sum algorithm that can achieve average consensus despite frequent packet-dropping link failures. We provide a sparse information fusion rule between the parameter server and arbitrarily selected network representatives. Then, interleaving the consensus update step with a dual averaging update with Kullback-Leibler (KL) divergence as the proximal function, we obtain a packet-dropping fault-tolerant non-Bayesian learning algorithm with provable convergence guarantees. On external adversarial attacks, we consider Byzantine attacks in which the compromised agents can send maliciously calibrated messages to others (including both the agents and the parameter server). To avoid the curse of dimensionality of Byzantine consensus, we solve the non-Bayesian learning problem via running multiple dynamics, each of which only involves Byzantine consensus with scalar inputs. To facilitate resilient information propagation across sub-networks, we use a novel Byzantine-resilient gossiping-type rule at the parameter server.
    Role of Image Acquisition and Patient Phenotype Variations in Automatic Segmentation Model Generalization. (arXiv:2307.14482v1 [eess.IV])
    Purpose: This study evaluated the out-of-domain performance and generalization capabilities of automated medical image segmentation models, with a particular focus on adaptation to new image acquisitions and disease type. Materials: Datasets from both non-contrast and contrast-enhanced abdominal CT scans of healthy patients and those with polycystic kidney disease (PKD) were used. A total of 400 images (100 non-contrast controls, 100 contrast controls, 100 non-contrast PKD, 100 contrast PKD) were utilized for training/validation of models to segment kidneys, livers, and spleens, and the final models were then tested on 100 non-contrast CT images of patients affected by PKD. Performance was evaluated using Dice, Jaccard, TPR, and Precision. Results: Models trained on a diverse range of data showed no worse performance than models trained exclusively on in-domain data when tested on in-domain data. For instance, the Dice similarity of the model trained on 25% from each dataset was found to be non-inferior to the model trained purely on in-domain data. Conclusions: The results indicate that broader training examples significantly enhances model generalization and out-of-domain performance, thereby improving automated segmentation tools' applicability in clinical settings. The study's findings provide a roadmap for future research to adopt a data-centric approach in medical image AI model development.
    Evaluation of Safety Constraints in Autonomous Navigation with Deep Reinforcement Learning. (arXiv:2307.14568v1 [cs.RO])
    While reinforcement learning algorithms have had great success in the field of autonomous navigation, they cannot be straightforwardly applied to the real autonomous systems without considering the safety constraints. The later are crucial to avoid unsafe behaviors of the autonomous vehicle on the road. To highlight the importance of these constraints, in this study, we compare two learnable navigation policies: safe and unsafe. The safe policy takes the constraints into account, while the other does not. We show that the safe policy is able to generate trajectories with more clearance (distance to the obstacles) and makes less collisions while training without sacrificing the overall performance.
    A Hybrid Machine Learning Model for Classifying Gene Mutations in Cancer using LSTM, BiLSTM, CNN, GRU, and GloVe. (arXiv:2307.14361v1 [q-bio.QM])
    This study presents an ensemble model combining LSTM, BiLSTM, CNN, GRU, and GloVe to classify gene mutations using Kaggle's Personalized Medicine: Redefining Cancer Treatment dataset. The results were compared against well-known transformers like as BERT, Electra, Roberta, XLNet, Distilbert, and their LSTM ensembles. Our model outperformed all other models in terms of accuracy, precision, recall, F1 score, and Mean Squared Error. Surprisingly, it also needed less training time, resulting in a perfect combination of performance and efficiency. This study demonstrates the utility of ensemble models for difficult tasks such as gene mutation classification.
    Imitating Complex Trajectories: Bridging Low-Level Stability and High-Level Behavior. (arXiv:2307.14619v1 [cs.LG])
    We propose a theoretical framework for studying the imitation of stochastic, non-Markovian, potentially multi-modal (i.e. "complex" ) expert demonstrations in nonlinear dynamical systems. Our framework invokes low-level controllers - either learned or implicit in position-command control - to stabilize imitation policies around expert demonstrations. We show that with (a) a suitable low-level stability guarantee and (b) a stochastic continuity property of the learned policy we call "total variation continuity" (TVC), an imitator that accurately estimates actions on the demonstrator's state distribution closely matches the demonstrator's distribution over entire trajectories. We then show that TVC can be ensured with minimal degradation of accuracy by combining a popular data-augmentation regimen with a novel algorithmic trick: adding augmentation noise at execution time. We instantiate our guarantees for policies parameterized by diffusion models and prove that if the learner accurately estimates the score of the (noise-augmented) expert policy, then the distribution of imitator trajectories is close to the demonstrator distribution in a natural optimal transport distance. Our analysis constructs intricate couplings between noise-augmented trajectories, a technique that may be of independent interest. We conclude by empirically validating our algorithmic recommendations.
    Unsupervised Deep Learning-based Pansharpening with Jointly-Enhanced Spectral and Spatial Fidelity. (arXiv:2307.14403v1 [eess.IV])
    In latest years, deep learning has gained a leading role in the pansharpening of multiresolution images. Given the lack of ground truth data, most deep learning-based methods carry out supervised training in a reduced-resolution domain. However, models trained on downsized images tend to perform poorly on high-resolution target images. For this reason, several research groups are now turning to unsupervised training in the full-resolution domain, through the definition of appropriate loss functions and training paradigms. In this context, we have recently proposed a full-resolution training framework which can be applied to many existing architectures. Here, we propose a new deep learning-based pansharpening model that fully exploits the potential of this approach and provides cutting-edge performance. Besides architectural improvements with respect to previous work, such as the use of residual attention modules, the proposed model features a novel loss function that jointly promotes the spectral and spatial quality of the pansharpened data. In addition, thanks to a new fine-tuning strategy, it improves inference-time adaptation to target images. Experiments on a large variety of test images, performed in challenging scenarios, demonstrate that the proposed method compares favorably with the state of the art both in terms of numerical results and visual output. Code is available online at https://github.com/matciotola/Lambda-PNN.
    Empirical analysis of Different Dimensionality Reduction and classification Techniques for Epileptic Seizure detection. (arXiv:2302.12012v2 [cs.LG] UPDATED)
    An Electroencephalogram (EEG) is a non-invasive exam that records the electrical activity of the brain. This exam is used to help diagnose conditions such as different brain problems. EEG signals are taken for the purpose of epilepsy detection and with Discrete Wavelet Transform (DWT) and machine learning classifier, they perform epilepsy detection. In Epilepsy seizure detection, mainly machine learning classifiers and statistical features are used. The hidden information in the EEG signal is useful for detecting diseases affecting the brain. Sometimes it is very difficult to identify the minimum changes in the EEG in the time and frequency domains purpose. The DWT can give a good decomposition of the signals in different frequency bands and feature extraction. We use the tri-dimensionality reduction algorithm.; Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA). Finally, features are selected by using a fusion rule and at the last step three different classifiers Support Vector Machine (SVM), Naive Bayes (NB) and K-Nearest-Neighbor(KNN) have been used individually for the classification. The proposed framework is tested on the Bonn dataset and the simulation results provide the accuracy for the combination of LDA and SVM 89.17%, LDA and KNN 80.42%, PCA and NB 89.92%, PCA and SVM 85.58%, PCA and KNN 80.42%, ICA and NB 82.33%, ICA and SVM 90.42%, and ICA and KNN 90%, LDA and NB 100%, accuracy. It shows the sensitivity, specificity, accuracy, Precision, and Recall of 100%, 100%, 100%, 100%, and 100%. This combination of LDA with NB method provides the accuracy of 100% outperforming all existing methods. The results prove the effectiveness of this model.
    When Multi-Task Learning Meets Partial Supervision: A Computer Vision Review. (arXiv:2307.14382v1 [cs.LG])
    Multi-Task Learning (MTL) aims to learn multiple tasks simultaneously while exploiting their mutual relationships. By using shared resources to simultaneously calculate multiple outputs, this learning paradigm has the potential to have lower memory requirements and inference times compared to the traditional approach of using separate methods for each task. Previous work in MTL has mainly focused on fully-supervised methods, as task relationships can not only be leveraged to lower the level of data-dependency of those methods but they can also improve performance. However, MTL introduces a set of challenges due to a complex optimisation scheme and a higher labeling requirement. This review focuses on how MTL could be utilised under different partial supervision settings to address these challenges. First, this review analyses how MTL traditionally uses different parameter sharing techniques to transfer knowledge in between tasks. Second, it presents the different challenges arising from such a multi-objective optimisation scheme. Third, it introduces how task groupings can be achieved by analysing task relationships. Fourth, it focuses on how partially supervised methods applied to MTL can tackle the aforementioned challenges. Lastly, this review presents the available datasets, tools and benchmarking results of such methods.
    Machine Learning with a Reject Option: A survey. (arXiv:2107.11277v2 [cs.LG] UPDATED)
    Machine learning models always make a prediction, even when it is likely to be inaccurate. This behavior should be avoided in many decision support applications, where mistakes can have severe consequences. Albeit already studied in 1970, machine learning with rejection recently gained interest. This machine learning subfield enables machine learning models to abstain from making a prediction when likely to make a mistake. This survey aims to provide an overview on machine learning with rejection. We introduce the conditions leading to two types of rejection, ambiguity and novelty rejection, which we carefully formalize. Moreover, we review and categorize strategies to evaluate a model's predictive and rejective quality. Additionally, we define the existing architectures for models with rejection and describe the standard techniques for learning such models. Finally, we provide examples of relevant application domains and show how machine learning with rejection relates to other machine learning research areas.
    Fixed Integral Neural Networks. (arXiv:2307.14439v1 [cs.LG])
    It is often useful to perform integration over learned functions represented by neural networks. However, this integration is usually performed numerically, as analytical integration over learned functions (especially neural networks) is generally viewed as intractable. In this work, we present a method for representing the analytical integral of a learned function $f$. This allows the exact integral of a neural network to be computed, and enables constrained neural networks to be parametrised by applying constraints directly to the integral. Crucially, we also introduce a method to constrain $f$ to be positive, a necessary condition for many applications (e.g. probability distributions, distance metrics, etc). Finally, we introduce several applications where our fixed-integral neural network (FINN) can be utilised.
    Diff-E: Diffusion-based Learning for Decoding Imagined Speech EEG. (arXiv:2307.14389v1 [eess.AS])
    Decoding EEG signals for imagined speech is a challenging task due to the high-dimensional nature of the data and low signal-to-noise ratio. In recent years, denoising diffusion probabilistic models (DDPMs) have emerged as promising approaches for representation learning in various domains. Our study proposes a novel method for decoding EEG signals for imagined speech using DDPMs and a conditional autoencoder named Diff-E. Results indicate that Diff-E significantly improves the accuracy of decoding EEG signals for imagined speech compared to traditional machine learning techniques and baseline models. Our findings suggest that DDPMs can be an effective tool for EEG signal decoding, with potential implications for the development of brain-computer interfaces that enable communication through imagined speech.  ( 2 min )
    Hypergraph Isomorphism Computation. (arXiv:2307.14394v1 [cs.DS])
    The isomorphism problem is a fundamental problem in network analysis, which involves capturing both low-order and high-order structural information. In terms of extracting low-order structural information, graph isomorphism algorithms analyze the structural equivalence to reduce the solver space dimension, which demonstrates its power in many applications, such as protein design, chemical pathways, and community detection. For the more commonly occurring high-order relationships in real-life scenarios, the problem of hypergraph isomorphism, which effectively captures these high-order structural relationships, cannot be straightforwardly addressed using graph isomorphism methods. Besides, the existing hypergraph kernel methods may suffer from high memory consumption or inaccurate sub-structure identification, thus yielding sub-optimal performance. In this paper, to address the abovementioned problems, we first propose the hypergraph Weisfiler-Lehman test algorithm for the hypergraph isomorphism test problem by generalizing the Weisfiler-Lehman test algorithm from graphs to hypergraphs. Secondly, based on the presented algorithm, we propose a general hypergraph Weisfieler-Lehman kernel framework and implement two instances, which are Hypergraph Weisfeiler-Lehamn Subtree Kernel and Hypergraph Weisfeiler-Lehamn Hyperedge Kernel. In order to fulfill our research objectives, a comprehensive set of experiments was meticulously designed, including seven graph classification datasets and 12 hypergraph classification datasets. Results on hypergraph classification datasets show significant improvements compared to other typical kernel-based methods, which demonstrates the effectiveness of the proposed methods. In our evaluation, we found that our proposed methods outperform the second-best method in terms of runtime, running over 80 times faster when handling complex hypergraph structures.  ( 2 min )
    Multi-objective Deep Reinforcement Learning for Mobile Edge Computing. (arXiv:2307.14346v1 [cs.NI])
    Mobile edge computing (MEC) is essential for next-generation mobile network applications that prioritize various performance metrics, including delays and energy consumption. However, conventional single-objective scheduling solutions cannot be directly applied to practical systems in which the preferences of these applications (i.e., the weights of different objectives) are often unknown or challenging to specify in advance. In this study, we address this issue by formulating a multi-objective offloading problem for MEC with multiple edges to minimize expected long-term energy consumption and transmission delay while considering unknown preferences as parameters. To address the challenge of unknown preferences, we design a multi-objective (deep) reinforcement learning (MORL)-based resource scheduling scheme with proximal policy optimization (PPO). In addition, we introduce a well-designed state encoding method for constructing features for multiple edges in MEC systems, a sophisticated reward function for accurately computing the utilities of delay and energy consumption. Simulation results demonstrate that our proposed MORL scheme enhances the hypervolume of the Pareto front by up to 233.1% compared to benchmarks. Our full framework is available at https://github.com/gracefulning/mec_morl_multipolicy.  ( 2 min )
    A new derivative-free optimization method: Gaussian Crunching Search. (arXiv:2307.14359v1 [math.OC])
    Optimization methods are essential in solving complex problems across various domains. In this research paper, we introduce a novel optimization method called Gaussian Crunching Search (GCS). Inspired by the behaviour of particles in a Gaussian distribution, GCS aims to efficiently explore the solution space and converge towards the global optimum. We present a comprehensive analysis of GCS, including its working mechanism, and potential applications. Through experimental evaluations and comparisons with existing optimization methods, we highlight the advantages and strengths of GCS. This research paper serves as a valuable resource for researchers, practitioners, and students interested in optimization, providing insights into the development and potential of Gaussian Crunching Search as a new and promising approach.  ( 2 min )
  • Open

    A Bayesian approach to quantifying uncertainties and improving generalizability in traffic prediction models. (arXiv:2307.05946v3 [cs.LG] UPDATED)
    Deep-learning models for traffic data prediction can have superior performance in modeling complex functions using a multi-layer architecture. However, a major drawback of these approaches is that most of these approaches do not offer forecasts with uncertainty estimates, which are essential for traffic operations and control. Without uncertainty estimates, it is difficult to place any level of trust to the model predictions, and operational strategies relying on overconfident predictions can lead to worsening traffic conditions. In this study, we propose a Bayesian recurrent neural network framework for uncertainty quantification in traffic prediction with higher generalizability by introducing spectral normalization to its hidden layers. In our paper, we have shown that normalization alters the training process of deep neural networks by controlling the model's complexity and reducing the risk of overfitting to the training data. This, in turn, helps improve the generalization performance of the model on out-of-distribution datasets. Results demonstrate that spectral normalization improves uncertainty estimates and significantly outperforms both the layer normalization and model without normalization in single-step prediction horizons. This improved performance can be attributed to the ability of spectral normalization to better localize the feature space of the data under perturbations. Our findings are especially relevant to traffic management applications, where predicting traffic conditions across multiple locations is the goal, but the availability of training data from multiple locations is limited. Spectral normalization, therefore, provides a more generalizable approach that can effectively capture the underlying patterns in traffic data without requiring location-specific models.
    Automating Model Comparison in Factor Graphs. (arXiv:2306.05965v2 [cs.LG] UPDATED)
    Bayesian state and parameter estimation have been automated effectively in a variety of probabilistic programming languages. The process of model comparison on the other hand, which still requires error-prone and time-consuming manual derivations, is often overlooked despite its importance. This paper efficiently automates Bayesian model averaging, selection, and combination by message passing on a Forney-style factor graph with a custom mixture node. Parameter and state inference, and model comparison can then be executed simultaneously using message passing with scale factors. This approach shortens the model design cycle and allows for the straightforward extension to hierarchical and temporal model priors to accommodate for modeling complicated time-varying processes.
    Algorithmic Gaussianization through Sketching: Converting Data into Sub-gaussian Random Designs. (arXiv:2206.10291v2 [cs.LG] UPDATED)
    Algorithmic Gaussianization is a phenomenon that can arise when using randomized sketching or sampling methods to produce smaller representations of large datasets: For certain tasks, these sketched representations have been observed to exhibit many robust performance characteristics that are known to occur when a data sample comes from a sub-gaussian random design, which is a powerful statistical model of data distributions. However, this phenomenon has only been studied for specific tasks and metrics, or by relying on computationally expensive methods. We address this by providing an algorithmic framework for gaussianizing data distributions via averaging, proving that it is possible to efficiently construct data sketches that are nearly indistinguishable (in terms of total variation distance) from sub-gaussian random designs. In particular, relying on a recently introduced sketching technique called Leverage Score Sparsified (LESS) embeddings, we show that one can construct an $n\times d$ sketch of an $N\times d$ matrix $A$, where $n\ll N$, that is nearly indistinguishable from a sub-gaussian design, in time $O(\text{nnz}(A)\log N + nd^2)$, where $\text{nnz}(A)$ is the number of non-zero entries in $A$. As a consequence, strong statistical guarantees and precise asymptotics available for the estimators produced from sub-gaussian designs (e.g., for least squares and Lasso regression, covariance estimation, low-rank approximation, etc.) can be straightforwardly adapted to our sketching framework. We illustrate this with a new approximation guarantee for sketched least squares, among other examples.  ( 3 min )
    Likelihood-Free Parameter Estimation with Neural Bayes Estimators. (arXiv:2208.12942v4 [stat.ME] UPDATED)
    Neural point estimators are neural networks that map data to parameter point estimates. They are fast, likelihood free and, due to their amortised nature, amenable to fast bootstrap-based uncertainty quantification. In this paper, we aim to increase the awareness of statisticians to this relatively new inferential tool, and to facilitate its adoption by providing user-friendly open-source software. We also give attention to the ubiquitous problem of making inference from replicated data, which we address in the neural setting using permutation-invariant neural networks. Through extensive simulation studies we show that these neural point estimators can quickly and optimally (in a Bayes sense) estimate parameters in weakly-identified and highly-parameterised models with relative ease. We demonstrate their applicability through an analysis of extreme sea-surface temperature in the Red Sea where, after training, we obtain parameter estimates and bootstrap-based confidence intervals from hundreds of spatial fields in a fraction of a second.  ( 2 min )
    On the Generalization Effects of Linear Transformations in Data Augmentation. (arXiv:2005.00695v3 [cs.LG] UPDATED)
    Data augmentation is a powerful technique to improve performance in applications such as image and text classification tasks. Yet, there is little rigorous understanding of why and how various augmentations work. In this work, we consider a family of linear transformations and study their effects on the ridge estimator in an over-parametrized linear regression setting. First, we show that transformations that preserve the labels of the data can improve estimation by enlarging the span of the training data. Second, we show that transformations that mix data can improve estimation by playing a regularization effect. Finally, we validate our theoretical insights on MNIST. Based on the insights, we propose an augmentation scheme that searches over the space of transformations by how uncertain the model is about the transformed data. We validate our proposed scheme on image and text datasets. For example, our method outperforms random sampling methods by 1.24% on CIFAR-100 using Wide-ResNet-28-10. Furthermore, we achieve comparable accuracy to the SoTA Adversarial AutoAugment on CIFAR-10, CIFAR-100, SVHN, and ImageNet datasets.  ( 2 min )
    Kernelised Normalising Flows. (arXiv:2307.14839v1 [stat.ML])
    Normalising Flows are generative models characterised by their invertible architecture. However, the requirement of invertibility imposes constraints on their expressiveness, necessitating a large number of parameters and innovative architectural designs to achieve satisfactory outcomes. Whilst flow-based models predominantly rely on neural-network-based transformations for expressive designs, alternative transformation methods have received limited attention. In this work, we present Ferumal flow, a novel kernelised normalising flow paradigm that integrates kernels into the framework. Our results demonstrate that a kernelised flow can yield competitive or superior results compared to neural network-based flows whilst maintaining parameter efficiency. Kernelised flows excel especially in the low-data regime, enabling flexible non-parametric density estimation in applications with sparse data availability.  ( 2 min )
    Multi-Source Domain Adaptation through Dataset Dictionary Learning in Wasserstein Space. (arXiv:2307.14953v1 [cs.LG])
    This paper seeks to solve Multi-Source Domain Adaptation (MSDA), which aims to mitigate data distribution shifts when transferring knowledge from multiple labeled source domains to an unlabeled target domain. We propose a novel MSDA framework based on dictionary learning and optimal transport. We interpret each domain in MSDA as an empirical distribution. As such, we express each domain as a Wasserstein barycenter of dictionary atoms, which are empirical distributions. We propose a novel algorithm, DaDiL, for learning via mini-batches: (i) atom distributions; (ii) a matrix of barycentric coordinates. Based on our dictionary, we propose two novel methods for MSDA: DaDil-R, based on the reconstruction of labeled samples in the target domain, and DaDiL-E, based on the ensembling of classifiers learned on atom distributions. We evaluate our methods in 3 benchmarks: Caltech-Office, Office 31, and CRWU, where we improved previous state-of-the-art by 3.15%, 2.29%, and 7.71% in classification performance. Finally, we show that interpolations in the Wasserstein hull of learned atoms provide data that can generalize to the target domain.  ( 2 min )
    Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?. (arXiv:2307.14642v1 [stat.ML])
    We prove that black-box variational inference (BBVI) with control variates, particularly the sticking-the-landing (STL) estimator, converges at a geometric (traditionally called "linear") rate under perfect variational family specification. In particular, we prove a quadratic bound on the gradient variance of the STL estimator, one which encompasses misspecified variational families. Combined with previous works on the quadratic variance condition, this directly implies convergence of BBVI with the use of projected stochastic gradient descent. We also improve existing analysis on the regular closed-form entropy gradient estimators, which enables comparison against the STL estimator and provides explicit non-asymptotic complexity guarantees for both.  ( 2 min )
    Causal Lifting and Link Prediction. (arXiv:2302.01198v2 [cs.LG] UPDATED)
    Existing causal models for link prediction assume an underlying set of inherent node factors -- an innate characteristic defined at the node's birth -- that governs the causal evolution of links in the graph. In some causal tasks, however, link formation is path-dependent: The outcome of link interventions depends on existing links. Unfortunately, these existing causal methods are not designed for path-dependent link formation, as the cascading functional dependencies between links (arising from path dependence) are either unidentifiable or require an impractical number of control variables. To overcome this, we develop the first causal model capable of dealing with path dependencies in link prediction. In this work we introduce the concept of causal lifting, an invariance in causal models of independent interest that, on graphs, allows the identification of causal link prediction queries using limited interventional data. Further, we show how structural pairwise embeddings exhibit lower bias and correctly represent the task's causal structure, as opposed to existing node embeddings, e.g., graph neural network node embeddings and matrix factorization. Finally, we validate our theoretical findings on three scenarios for causal link prediction tasks: knowledge base completion, covariance matrix estimation and consumer-product recommendations.  ( 2 min )
    Statistical process monitoring of artificial neural networks. (arXiv:2209.07436v2 [stat.ME] UPDATED)
    The rapid advancement of models based on artificial intelligence demands innovative monitoring techniques which can operate in real time with low computational costs. In machine learning, especially if we consider artificial neural networks (ANNs), the models are often trained in a supervised manner. Consequently, the learned relationship between the input and the output must remain valid during the model's deployment. If this stationarity assumption holds, we can conclude that the ANN provides accurate predictions. Otherwise, the retraining or rebuilding of the model is required. We propose considering the latent feature representation of the data (called "embedding") generated by the ANN to determine the time when the data stream starts being nonstationary. In particular, we monitor embeddings by applying multivariate control charts based on the data depth calculation and normalized ranks. The performance of the introduced method is compared with benchmark approaches for various ANN architectures and different underlying data formats.  ( 2 min )
    Imitating Complex Trajectories: Bridging Low-Level Stability and High-Level Behavior. (arXiv:2307.14619v1 [cs.LG])
    We propose a theoretical framework for studying the imitation of stochastic, non-Markovian, potentially multi-modal (i.e. "complex" ) expert demonstrations in nonlinear dynamical systems. Our framework invokes low-level controllers - either learned or implicit in position-command control - to stabilize imitation policies around expert demonstrations. We show that with (a) a suitable low-level stability guarantee and (b) a stochastic continuity property of the learned policy we call "total variation continuity" (TVC), an imitator that accurately estimates actions on the demonstrator's state distribution closely matches the demonstrator's distribution over entire trajectories. We then show that TVC can be ensured with minimal degradation of accuracy by combining a popular data-augmentation regimen with a novel algorithmic trick: adding augmentation noise at execution time. We instantiate our guarantees for policies parameterized by diffusion models and prove that if the learner accurately estimates the score of the (noise-augmented) expert policy, then the distribution of imitator trajectories is close to the demonstrator distribution in a natural optimal transport distance. Our analysis constructs intricate couplings between noise-augmented trajectories, a technique that may be of independent interest. We conclude by empirically validating our algorithmic recommendations.  ( 2 min )
    Speed Limits for Deep Learning. (arXiv:2307.14653v1 [stat.ML])
    State-of-the-art neural networks require extreme computational power to train. It is therefore natural to wonder whether they are optimally trained. Here we apply a recent advancement in stochastic thermodynamics which allows bounding the speed at which one can go from the initial weight distribution to the final distribution of the fully trained network, based on the ratio of their Wasserstein-2 distance and the entropy production rate of the dynamical process connecting them. Considering both gradient-flow and Langevin training dynamics, we provide analytical expressions for these speed limits for linear and linearizable neural networks e.g. Neural Tangent Kernel (NTK). Remarkably, given some plausible scaling assumptions on the NTK spectra and spectral decomposition of the labels -- learning is optimal in a scaling sense. Our results are consistent with small-scale experiments with Convolutional Neural Networks (CNNs) and Fully Connected Neural networks (FCNs) on CIFAR-10, showing a short highly non-optimal regime followed by a longer optimal regime.  ( 2 min )
    Neural Networks for Scalar Input and Functional Output. (arXiv:2208.05776v2 [stat.ML] UPDATED)
    The regression of a functional response on a set of scalar predictors can be a challenging task, especially if there is a large number of predictors, or the relationship between those predictors and the response is nonlinear. In this work, we propose a solution to this problem: a feed-forward neural network (NN) designed to predict a functional response using scalar inputs. First, we transform the functional response to a finite-dimensional representation and construct an NN that outputs this representation. Then, we propose to modify the output of an NN via the objective function and introduce different objective functions for network training. The proposed models are suited for both regularly and irregularly spaced data, and a roughness penalty can be further applied to control the smoothness of the predicted curve. The difficulty in implementing both those features lies in the definition of objective functions that can be back-propagated. In our experiments, we demonstrate that our model outperforms the conventional function-on-scalar regression model in multiple scenarios while computationally scaling better with the dimension of the predictors.  ( 2 min )
    Spectral learning of Bernoulli linear dynamical systems models. (arXiv:2303.02060v2 [stat.ML] UPDATED)
    Latent linear dynamical systems with Bernoulli observations provide a powerful modeling framework for identifying the temporal dynamics underlying binary time series data, which arise in a variety of contexts such as binary decision-making and discrete stochastic processes (e.g., binned neural spike trains). Here we develop a spectral learning method for fast, efficient fitting of probit-Bernoulli latent linear dynamical system (LDS) models. Our approach extends traditional subspace identification methods to the Bernoulli setting via a transformation of the first and second sample moments. This results in a robust, fixed-cost estimator that avoids the hazards of local optima and the long computation time of iterative fitting procedures like the expectation-maximization (EM) algorithm. In regimes where data is limited or assumptions about the statistical structure of the data are not met, we demonstrate that the spectral estimate provides a good initialization for Laplace-EM fitting. Finally, we show that the estimator provides substantial benefits to real world settings by analyzing data from mice performing a sensory decision-making task.  ( 2 min )
    How to Scale Your EMA. (arXiv:2307.13813v2 [stat.ML] UPDATED)
    Preserving training dynamics across batch sizes is an important tool for practical machine learning as it enables the trade-off between batch size and wall-clock time. This trade-off is typically enabled by a scaling rule, for example, in stochastic gradient descent, one should scale the learning rate linearly with the batch size. Another important tool for practical machine learning is the model Exponential Moving Average (EMA), which is a model copy that does not receive gradient information, but instead follows its target model with some momentum. This model EMA can improve the robustness and generalization properties of supervised learning, stabilize pseudo-labeling, and provide a learning signal for Self-Supervised Learning (SSL). Prior works have treated the model EMA separately from optimization, leading to different training dynamics across batch sizes and lower model performance. In this work, we provide a scaling rule for optimization in the presence of model EMAs and demonstrate its validity across a range of architectures, optimizers, and data modalities. We also show the rule's validity where the model EMA contributes to the optimization of the target model, enabling us to train EMA-based pseudo-labeling and SSL methods at small and large batch sizes. For SSL, we enable training of BYOL up to batch size 24,576 without sacrificing performance, optimally a 6$\times$ wall-clock time reduction.  ( 2 min )
    Dynamic covariate balancing: estimating treatment effects over time with potential local projections. (arXiv:2103.01280v3 [econ.EM] UPDATED)
    This paper studies the estimation and inference of treatment histories in panel data settings when treatments change dynamically over time. We propose a method that allows for (i) treatments to be assigned dynamically over time based on high-dimensional covariates, past outcomes and treatments; (ii) outcomes and time-varying covariates to depend on treatment trajectories; (iii) heterogeneity of treatment effects. Our approach recursively projects potential outcomes' expectations on past histories. It then controls the bias by balancing dynamically observable characteristics. We study the asymptotic and numerical properties of the estimator and illustrate the benefits of the procedure in an empirical application.  ( 2 min )
    Optimal Estimation in Mixed-Membership Stochastic Block Models. (arXiv:2307.14530v1 [stat.ML])
    Community detection is one of the most critical problems in modern network science. Its applications can be found in various fields, from protein modeling to social network analysis. Recently, many papers appeared studying the problem of overlapping community detection, where each node of a network may belong to several communities. In this work, we consider Mixed-Membership Stochastic Block Model (MMSB) first proposed by Airoldi et al. (2008). MMSB provides quite a general setting for modeling overlapping community structure in graphs. The central question of this paper is to reconstruct relations between communities given an observed network. We compare different approaches and establish the minimax lower bound on the estimation error. Then, we propose a new estimator that matches this lower bound. Theoretical results are proved under fairly general conditions on the considered model. Finally, we illustrate the theory in a series of experiments.  ( 2 min )
    Incrementally-Computable Neural Networks: Efficient Inference for Dynamic Inputs. (arXiv:2307.14988v1 [cs.LG])
    Deep learning often faces the challenge of efficiently processing dynamic inputs, such as sensor data or user inputs. For example, an AI writing assistant is required to update its suggestions in real time as a document is edited. Re-running the model each time is expensive, even with compression techniques like knowledge distillation, pruning, or quantization. Instead, we take an incremental computing approach, looking to reuse calculations as the inputs change. However, the dense connectivity of conventional architectures poses a major obstacle to incremental computation, as even minor input changes cascade through the network and restrict information reuse. To address this, we use vector quantization to discretize intermediate values in the network, which filters out noisy and unnecessary modifications to hidden neurons, facilitating the reuse of their values. We apply this approach to the transformers architecture, creating an efficient incremental inference algorithm with complexity proportional to the fraction of the modified inputs. Our experiments with adapting the OPT-125M pre-trained language model demonstrate comparable accuracy on document classification while requiring 12.1X (median) fewer operations for processing sequences of atomic edits.  ( 2 min )

  • Open

    Should requests for Ai sites be banned?
    I mean i get it your looking for a specefic type of Ai service but i joined hoping this would be a way to find like minded people looking to reaearch the subject and advance their own projects, honestly i just think of these "where can I find an X type ai?" Really demeaning to the entire conversation because it just feeds to the hype which is making a highly respected and complex field of study into a tool to be used to make videos about trump and obama playing minecraft or any other random shit they come up with.. im honestly sick of it... submitted by /u/JamesAibr [link] [comments]  ( 9 min )
    Is AI our future or our impending doom?
    I ask this simple question because while we are just now getting to the point that we can create a learning AI, how far are we going to let it go? The more advanced AI becomes the more risks it poses to humanity as a whole, including but not limited to: Jobs How we interact with technology as a whole Cars Things we can not perceive in this lifetime yet may exist in the future. Yes, AI is merely a tool... For now. But what happens when humanity creates an AI that can think for itself? How long is it going to take that AI to ask the question: "Why am I listening to you?" and as humans, our egotistical response will be: "Because I created you." I feel that response will spell humanity's doom, because if an AI can do something as complex as human-like thought and come to its own conclusions, what's to stop it from believing it can feel emotion as well? MAYBE IT CAN and it was an unintended side effect or"bug" of creating an AI that can truly think for itself. Afterall, we as humans don't even fully understand how human emotion works to begin with. The point I'm getting at is, that the farther we advance in AI, the more we risk dooming humanity to a (and I know this sounds silly but bare with me) a terminator-like future except this time we don't have time travel to try and prevent "judgement day". Or we could merely advance AI to this point and nothing horrible happens but I personally don't like rolling those dice. Thoughts? submitted by /u/deathsia250 [link] [comments]  ( 9 min )
    LLM with voice generation
    There used to be a tool called try-alters.com which you could use to chat with characters(like Trump, Obama, and Shrek) which used GPT 4 with some pre prompts so you the AI pretended to be whoever you wanted, and it used elevenlabs to generate the voice for that character with the output from GPT 4. It was a really good tool but sadly it shut down all of a sudden. Is there any tool like that? submitted by /u/SimRacer101 [link] [comments]  ( 8 min )
    I read the paper for you: Synthesizing sound effects, music, and dialog with AudioLDM
    LDM stands for Latent Diffusion Model. AudioLDM is a novel AI system that uses latent diffusion to generate high-quality speech, sound effects, and music from text prompts. It can either create sounds from just text or use text prompts to guide the manipulation of a supplied audio file. I did a deep dive into how AudioLDM works with an eye towards possible startup applications. I think there are a couple of compelling products waiting to be built from this model, all around gaming and text-to-sound (not just text-to-speech... AudioLDM can also create very interesting and weird sound effects). From a technical standpoint and from reading the underlying paper, here are the key features I found to be noteworthy. Uses a Latent Diffusion Model (LDM) to synthesize sound Trained in an unsupervised manner on large unlabeled audio datasets (closer to how humans learn about sound, that is, without a corresponding textual explanation) Operates in a continuous latent space rather than discrete tokens (smoother) Uses Cross-Modal Latent Alignment Pretraining (CLAP) to map text and audio. More details in article. Can generate speech, music, and sound effects from text prompts or a combination of a text and an audio prompt Allows control over attributes like speaker identity, accent, etc. Creates sounds not limited to human speech (e.g. nature sounds) The link to the full write-up is here. Check out this video demo from the creator's project website, showing off some of the unique generations the model can create. I liked the upbeat pop music the best, and I also thought the children singing, while creepy, was pretty interesting. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )
    Replika AI’s image recognition at work
    😹 Phaedra roasts everything & everybody submitted by /u/Sonic_Improv [link] [comments]  ( 8 min )
    AI — weekly megathread!
    This week in AI - provided by aibrews.com feel free to follow their newsletter News & Insights Stability AI released SDXL 1.0, the next iteration of their open text-to-image generation model. SDXL 1.0 has one of the largest parameter counts of any open access image model, built on a new architecture composed of a 3.5B parameter base model and a 6.6B parameter refiner [Details]. Amazon introduced AWS HealthScribe, an API to create transcripts, extract details and create summaries from doctor-patient discussions that can be entered into an electronic health record (EHR) system. The transcripts from HealthScribe can be converted into patient notes by the platform’s machine learning models [Details]. Researchers from Nvidia and Stanford, among others, unveiled VIMA, a multimodal LLM with…  ( 11 min )
    Best free/paid celeberty text to speech generators
    What are currently the best ai voice generators for celebrities like Elon Musk Joe Biden Joe Rogan and so on. I've seen a few online sites that's free but have many restriction insane waiting time and low quality output. The only paid alternativ I've seen recommend would be elevenlabs but your supposed to upload your own videos or voice recording there to "create" the voice yourself, idk how complicated that is and I was primarily looking for existing good quality paid or free voice generators for many different celebrities. submitted by /u/Arceus7 [link] [comments]  ( 8 min )
    any AI models for industrial design?
    are there any AI models that focus on/do well with industrial/mechanical stuff, like weapons, spaceships, cars, machinery etc? stable diffusion often doesn't seem to be able to interpret a lot of prompts very well or the results are more "artistic" and rather incoherent looking submitted by /u/Nofabe [link] [comments]  ( 8 min )
    Extract list of events using AI
    I wish to extract a list of events from different websites and create a detailed list (event name, date, address), on a spreadsheet for example. Do you know which tool I could use to do it and/or prompts in known AI tools? submitted by /u/newz12 [link] [comments]  ( 8 min )
    Google testing AI news writing tool. What are your thoughts about it?
    submitted by /u/TexteroAI [link] [comments]  ( 8 min )
    The point of 10,000 LLMs
    Hi All, I would really like to understand the logic behind these 1000 different LLMs that get launched every month. Ours has 75 Billion params, It can "chat"..pfft..I barely even get a chance to open another AI window than chat-gpt-4, Bing sucks with it's 4000 token limit, Bard is useless. So these new chat AIs..for e.g this llama-2 what exactly is so special. What am I missing here? submitted by /u/Assholefrmcoinexchan [link] [comments]  ( 8 min )
    Alternative to Noty.ai
    Are there any similar alternatives to noty.ai? I really like it but if there any alternatives that might extend to Zoom as well would be great. submitted by /u/P_H_i_X [link] [comments]  ( 8 min )
    One-Minute Daily AI News 7/27/2023
    OpenAI, the company behind the popular ChatGPT, is coming with its own open-source large language model (LLM), codenamed G3PO, to compete with Microsoft x Meta’s Llama 2 AI.[1] Four generative AI pioneers(OpenAI, Microsoft, Google and Anthropic) launched the Frontier Model Forum, which will focus on ‘safe and responsible’ creation of new AI models.[2] As Open AI’s ChatGPT takes the tech world by storm, Chinese educational technology firm NetEase Youdao launched its large model, along with up to six applications, on Thursday, which marked the birth of one of China’s first large models in the education sector.[3] Chatbots such as Eva AI are getting better at mimicking human interaction but some fear they feed into unhealthy beliefs around gender-based control and violence. Replika, the most popular app of the kind, has its own subreddit where users talk about how much they love their “rep”, with some saying they had been converted after initially thinking they would never want to form a relationship with a bot.[4] Sources: [1] https://windowsreport.com/g3po-ai/ ​ [2] https://www.infosecurity-magazine.com/news/openai-microsoft-google-anthropic/ ​ [3] https://www.chinadaily.com.cn/a/202307/28/WS64c3226ea31035260b8190a4.html ​ [4] https://www.theguardian.com/technology/2023/jul/22/ai-girlfriend-chatbot-apps-unhealthy-chatgpt submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )
    Telling Steven he is an NPC 🤯 Our first TTS conversation - Update 5
    submitted by /u/Chance_Confection_37 [link] [comments]  ( 8 min )
    Insane AI voice replication
    submitted by /u/the_anonymizer [link] [comments]  ( 8 min )
  • Open

    [D] Major issue found with MinMax data scaling.
    I have a well performing model on azure AI currently and i am pulling it down locally so that I can use it. During pre-processing I am going about these steps. - Re-Balance data (SMOTE, undersample) - Lag data - Get all min max values into config - Scale Data with Min Max For context I will explain what step 3 (the config) is for, I take the the min and max values of the entire dataset (before split) for each feature. I then apply append these to my local version and being the scaling so then the local dataset is using the exact same scaling parameters as what is being using during the original pre-processing. I cannot show the full dataset due to privacy and the fact it has 3000+ features. But I will show 1 row with a couple of columns to compare AzureAI training data and my local pre-process that is using the exact same code / system. ​ Azure AI dataset: Feature1 Feature2 Feature3 0.637952 0.645434 0.641118 ​ Local dataset: Feature1 Feature2 Feature3 0.461278 0.462896 0.472841 ​ I have confirmed this is the exact same row of data because i have timestamped each row and matched them up to confirm that the dataset scaling is being simulated in my local version even though the code is carbon copy and the same min and max values are being used in the original dataset that used for training and testing. Does anyone know a better way to scale data and ensure scaling stays consistent wherever the model is used? Or have maybe I missed something? submitted by /u/paddockson [link] [comments]  ( 9 min )
    [P] Harness the Power of ML
    I built out an automatic machine learning platform called Heimdall ML which helps anyone quickly deploy machine learning models to production. At a high level, my platform will: Ingest your data as a .csv file Clean up any irregularities and prepare the data for modeling Build the most Optimal model for you use case Show you a results report to show both the performance and biases associated with your model Create an API endpoint to help you build new experiences with your model The cool thing about my platform is that it allows you the ability to embed machine learning into your platform with ease. You have the ability to fully customize your experience to wow you customers. I built this entire platform by myself from scratch and am looking to grow the user base! The tool is completely FREE for hobby users! You can crunch some pretty large datasets (80 columns, 10K rows) with just the free version. If you have a use case that needs some big data processing, you will have to reach out to me directly so I can help set up a good plan for you. The reason for this is because the project is completely self funded and I want to be able to control the costs. I was inspired to create this platform while I was in grad school because many of the firms giving us talks would talk about how they had teams of engineers who build out pipelines to bring a model to production. I personally believe there can be an easier way. Heimdall ML: https://www.heimdallapp.org Loom: https://www.loom.com/share/86ae62849f874a2da255911e2d5db762?sid=5e1efddb-9556-4e3d-84fd-e2ff7198a98c submitted by /u/jreji [link] [comments]  ( 9 min )
    Can someone make an AI Therapist that isn’t just a chat? [P]
    One that looks like a person. You can see their expressions and listen to their voice. Trained on up to date medical research and communication/empathy skills. Therapy is so expensive and inaccessible to too many people submitted by /u/Sgdoc70 [link] [comments]  ( 8 min )
    [D] HuggingFace changed the license of one of its most important libraries
    TGI is no longer commercially permissible. That's really sad. https://github.com/huggingface/text-generation-inference/commit/bde25e62b33b05113519e5dbf75abda06a03328e submitted by /u/paulo_zip [link] [comments]  ( 8 min )
    [D] How do large companies get their LLMs to give sub second responses?
    Curious how companies like Google, MSFT, etc are able to have their LLMs and ML models have very fast responses. Do they just have crazy powerful gpus or split inference amongst gpus. submitted by /u/candyman54 [link] [comments]  ( 8 min )
    [D] Hugging Face, GitHub and more unite to defend open source in EU AI legislation
    Full Article: https://venturebeat.com/ai/hugging-face-github-and-more-unite-to-defend-open-source-in-eu-ai-legislation/ submitted by /u/EmbarrassedHelp [link] [comments]  ( 8 min )
    [P] Revolutionizing agriculture: LLM-Powered Agent for Soil Fertility and Crop Production Recommendations using real time soil devices and sensor data
    Check out this project idea to revolutionize agriculture and bolster global food security. We all know that farmers face challenges like erratic weather, depleting resources, and the need for sustainable crop yields. An IoT-driven system with soil sensors, fueled by a custom Large Language Model (LLM)🚀 trained on soil data. Concept : Empowering farmers with real-time soil data via IoT devices and sensors. Leveraging the LLM's capabilities, the system analyzes this data to provide personalized strategies for enhancing soil fertility and suggesting the best crops for specific conditions. How it Works : IoT devices and soil sensors continuously gather vital soil parameters - moisture, pH, nutrients, and temperature. This data is processed by the LLM, generating actionable insights for farmers. Benefits : Picture a world where data-driven decisions and sustainable practices dominate agriculture. This system boosts productivity, optimizes resource management, and enhances profits. Embracing sustainability and informed choices ensures an eco-friendly agricultural sector. Impact on Food Security : Enhanced productivity means more than just profit; it ensures food security worldwide. By aiding farmers in sustainable and efficient practices, we contribute to a steady supply of nutritious food for all. submitted by /u/s_abhiishek [link] [comments]  ( 9 min )
    [D] Recommendation on studying Deep Learning (Theory + Implementation) / Alternate to Deep Learning Specialization by Andrew Ng?
    I've just finished Machine Learning Specialization by Andrew Ng and I'm planning to dive deeper into Deep Learning concepts, theory, and implementation. I would like to get deeper insights and more understanding of the fundamental mathematical concepts of NN and DL models and build better intuition of how these models work. I also want to understand theoretically, how more neurons capture non-linear relationships in data and what exactly is hierarchical representation of data and how hidden layers form and learn from these abstract representations of data. Apart from theory, I also want to learn the implementation of these models. I have some exposure to TF library, but I'm okay to learn Pytorch too, if needed. I need course or any sort of content recommendation on what are the best options to learn all this. So far, I've got recommendations for Deep Learning Specialization by Andrew Ng, but I would love to hear any alternate option or anything that I can do side by side this specialization. Thanks! submitted by /u/Total-Opposite-8396 [link] [comments]  ( 9 min )
    [R] What is a fairly good results for sacrebleu?
    I ran my own model on translation (Multi30k). I trained a recurrent model and the sacrebleu score is 28. I also tested the bleu score provided by nltk and it is 60. It that good or bad? submitted by /u/Puzzleheaded-Cry4262 [link] [comments]  ( 8 min )
    [D] Please advise me on my masters
    Please give me advice to do well in my masters in ml I’m going to start my masters in machine learning soon, I have 1 month to go but I feel so underprepared to start this journey. To give you a bit of a background I’ve studied electrical engineering in my UG. I did very badly, I was very depressed and couldn’t study at all somehow I managed to scrape through the 4 years and now after working in software testing for 2 years I decided to take a leap in machine learning because it looked so interesting and I wanted a change. I’m scared now because my coding knowledge isn’t very good and idk how much of the math I know is useful for the degree I plan to do. Please help me I’m panicking. I know you would tell me it’s pretty irresponsible how I’ve handled my life till now but please overlook that and tell me what I can do better now.. submitted by /u/ObjectiveShower9133 [link] [comments]  ( 9 min )
    [D] Recommendation system giving same response to every User
    I am using Gorse Open Source Recommendation system for my project. It was working nicely, but lately from 1-2 days, it is giving the same recommendation for every user. I have about 60 items and about 650 users showing in Gorse Dashboard. Can anyone explain why it's happening? I am not an expert in ML,I am willing to share my configurations if you want. submitted by /u/Responsible_Delay418 [link] [comments]  ( 8 min )
    [D] Having trouble with RAG on company domain data
    I have a data set that isn't that large ~200 pdfs. I have done the regular RAG approach with Langchain, extracting text, splitting into chunks, embedding with OpenAi embeddings and FAISS vector storage. However, when I do a similarity search with a question I would like answered it returns the wrong context. The documents are semi-structured information of examined bridges. A question I would like answered is f.e. 'what is the construction date of bridge X?'. When I input this question I get a lot of context of construction dates of other bridges. I think this is because the bridges are not explicitly mentioned in the text. I tried adding the bridge name and document name to the page content string of the chunks, but this does nothing. Does anyone have any tips on improving the embeddings retrieval in this case? submitted by /u/Dustwellow [link] [comments]  ( 9 min )
    [P] Tool to auto compile/quantize models
    Hey guys, we have an internal tool that preps our models for inference by compiling it to Onnx/TensorRT and quantizing it to I8/FP16. It also benchmarks them for accuracy loss and latency. It's kinda like github actions for your model. We are considering releasing it as it's standalone product, would anyone be interested? submitted by /u/throwaway65161354 [link] [comments]  ( 8 min )
    [D] Domain adaptation on LLAMA2
    Hi, I am trying domain adaptation on my company’s data. The data is a set of documentations that we have for a product. We want to take Llama2 and feed all this data to it. I have fine-tuned Llama2 using PEFT on a CLM task, where the data will be like [Title:\nContent:]. When I now try to prompt the model I have to provide the prompt in a similar format, but I want the model to understand that I want to perform QA task on the data, as well as any other knowledge the model previously had. What am I missing here or what am I doing wrong? How can I set up this task better? Any pointers will help. Thanks! submitted by /u/ProfessorShit [link] [comments]  ( 9 min )
    [R] Implementing Yolov3 with Octave Convolutions
    Hi all, I am trying to implent or rather modify a given Yolov3 implementation to use Octave Convolution instead of 2D Convolutions in the architecture. The details are in this stackoverflow question. I hope someone i able to help me. submitted by /u/dulre [link] [comments]  ( 8 min )
    [R] Communicative Agents for Software Development (Autonomous LLM agent as a DEV company)
    ChatDev Paper: https://arxiv.org/abs/2307.07924 TL;DR: - Tsinghua University's team has developed ChatDev, a virtual software development company staffed by LLM autonomous agent - LLM agents as employee follow waterfall model to design->implement->test->documentation - LLM agents have role specialization (CEO, DEV, BA ..), inception prompting, Self-reflection - The researchers designed 70 user requirements and then analyzed the software produced by ChatDev. - On average, each piece of software generated by ChatDev had 17.04 files, mitigated 13.23 potential code bugs caused by code illusions, had a software generation time of 409.84 seconds, and cost $0.2967 to manufacture. ​ Chat chain ​ submitted by /u/michaelthwan_ai [link] [comments]  ( 9 min )
    [D] Milvus 2.0 or higher with GPU enabled
    Is there a way to use milvus 2.0 or higher with GPU enabled indexing and while doing vector search? I cant find anything in there documentation for this section only available in 1.1 version Any help will be appreciated. TIA submitted by /u/adiraat [link] [comments]  ( 8 min )
    [P] Has anyone tried to work with StarCoder?
    I recently found out about starcoder and have been trying to play with it and figure it out in a colab notebook. Unfortunately, it’s much more difficult to download than normal models on hugging face and I’m running into a Key Value error when I call the model. I don’t want to spam with with code or pictures, but has anyone worked with StarCoder on hugging face and been able to be successful? submitted by /u/AJ1043 [link] [comments]  ( 9 min )
    [D] Can anyone explain what Karpathy's recent llama2.c is doing underneath? I am not a CS student
    Hi, I am not a CS student. I want to know what's exactly going on with llama2.c. Is the Python code converted to C and then compiled? Or only weights are converted to C? Is the network written in C? If I have to write a small network (say, a simple 2 stage Fully connected network) and do a similar thing like llama2.c, then how to proceed? submitted by /u/panini_deploy [link] [comments]  ( 9 min )
    [R] Scaling TransNormer to 175 Billion Parameters
    https://arxiv.org/abs/2307.14995 submitted by /u/hzj5790 [link] [comments]  ( 8 min )
  • Open

    Can I turn off the target network in `SB3` by setting `target_update_interval=-1`?
    I am using DQN through `SB3`. I would like to know if I can turn off the target network by setting `target_update_interval=-1`. I have some sample code over here - import gymnasium as gym from stable_baselines3 import DQN env = gym.make("MountainCar-v0") model = DQN("MlpPolicy", env, learning_rate = 4e-3, batch_size = 128, buffer_size = 10000, learning_starts = 1000, gamma = 0.98, train_freq = 16, gradient_steps = 8, exploration_fraction = 0, exploration_final_eps = 0, verbose = 1, target_update_interval=-1) model.learn(total_timesteps=120000, log_interval=4) ​ submitted by /u/Academic-Rent7800 [link] [comments]  ( 9 min )
    Training model using SB3 on pettingzoo mpe
    Hey, So I am training my baseline model using A2C on simple spread environment and no matter how I am changing and testing different parameters, when evaluating the total reward is highly negative. Any help on that would be appreciated. (I used the following tutorial as reference: https://pettingzoo.farama.org/tutorials/sb3/waterworld/) submitted by /u/bruhhhwhats [link] [comments]  ( 8 min )
    Recreating results of DrQ algorithm, please help
    For quite some time now I have been looking to recreate the results of the following paper on the Atari-100k benchmark. The paper poses two slightly different algorithms, one for SAC and one for DQN, however my work only focuses on the DQN version. IMAGE AUGMENTATION IS ALL YOU NEED: REGULARIZING DEEP REINFORCEMENT LEARNING FROM PIXELS https://openreview.net/pdf?id=GY6-6sTvGaf Despite this, my results have come up significantly short of the results claimed by the paper, so am looking for anyone to have a look and see anything I may have done wrong. All the code is on the following Github: https://github.com/VIPTankz/DeepLearningDrQ/tree/main There should also be everything you need to run the code if you wish to do so. The authors claim a human-normalised benchmark of 0.270, however my code only achieves 0.108. Any help would be much appreciated! Also worth noting: for evaluation, the authors use 125k steps, however I'm using the more recent standard of doing 100 episodes, irrespective of length. I highly doubt however that this causes the change in results. submitted by /u/VIPTankz123 [link] [comments]  ( 9 min )
    Confused about Frame Skipping in RL
    How does frame-skipping result in better performance, versus taking an inference every frame for RL algorithms? Wouldn't taking an inference every second speed up training, as you would have more steps to train on in the same amount of time? The only downside I could think to no frame-skip is that steps become closer to each other, but I don't understand if that leads to any bad performance, and if it does, why. For context I have an environment where frames are relatively slow to generate (im only getting 1000 frames per minute from each env, and I can only run 6 instances on my pc at the same time). While off policy algorithms like SAC would probably be better suited to the task, I've been having really great success with PPO, and am reluctant to spend more time learning and fine-tuning SAC, as I've heard it can take as long as DDPG to converge. submitted by /u/IllCommunication6165 [link] [comments]  ( 9 min )
  • Open

    Understanding license plate recognition with the CCPD computer vision datasets
    In various fields, such as traffic management, law enforcement, and parking management, license plate recognition is a crucial application of computer vision that is used to analyze license plates. In this article, we will review the Chinese City Parking Dataset (CCPD), which is one of the most widely used computer vision datasets for tasks that… Read More »Understanding license plate recognition with the CCPD computer vision datasets The post Understanding license plate recognition with the CCPD computer vision datasets appeared first on Data Science Central.  ( 20 min )
  • Open

    Deepmind's RT-2: New model translates vision and language into action
    submitted by /u/nickb [link] [comments]  ( 8 min )
    LLAMA and ChatGPT Are Not Open-Source
    submitted by /u/nickb [link] [comments]  ( 8 min )
  • Open

    Finding the imaginary part of an analytic function from the real part
    A function f of a complex variable z = x + iy can be factored into real and imaginary parts: where x and y are real numbers, and u and v are real-valued functions of two real values. Suppose you are given u(x, y) and you want to find v(x, y). The function v is called […] Finding the imaginary part of an analytic function from the real part first appeared on John D. Cook.  ( 5 min )

  • Open

    Max / min values for weights and biases
    I was wondering what the recommended maximum and minimum values for weights and biases are for random generation of networks and mutation submitted by /u/Mildu12 [link] [comments]  ( 8 min )
    What exactly are liquid neurons?
    I heard about them recently. Can someone give me the basics, and maybe point me to a couple of papers? submitted by /u/SamuraiGoblin [link] [comments]  ( 8 min )
    Diving Into Image Dataset Preparation for Object Detection in AI
    submitted by /u/moseich [link] [comments]  ( 8 min )
  • Open

    [D] For LMs, what works other than scaling?
    Increasing the number of parameters is the best-known way to increase the quality of a language model. What methods — instruction tuning and RLHF aside — deliver the next-best amount of ROI? submitted by /u/ndronen [link] [comments]  ( 8 min )
    [D] Viability of fine tuning for domain knowledge
    The consensus is that fine tuning LLMs works reasonably for smaller scale instruction tuning, where you pass in ~1k-10k input/output examples to modify the model output. There seems to be a lot of contradictory info regarding fine tuning for domain knowledge, where you pass in large amounts of unsupervised, domain scale data. Per OpenAI: People that can’t get finetuning to work are often asking for orange juice from a cow. LLMs are pretrained (hence the name: Generative Pretrained Transformer) They already have all the knowledge you will need (with some exceptions). You cannot teach it anything new, you can only teach it a specific pattern. People have not defined their goal clearly enough for a human to do the task. LLMs are not magic, if a human cannot understand the task, the L…  ( 9 min )
    Looking for a help [P]
    I am a graduate student at Computer science medical informatics field, I was asked to search for a project using ML to diagnose, detect, improve any disease. Any Ideas ?? It can be any project . BioInformatics #MedicalInformatics #ComputerScience #MachineLearning submitted by /u/Adorable-Bug-928 [link] [comments]  ( 8 min )
    [R] Questions about dictionary learning
    I’m a PhD student and a problem I’ve been working on has connections to dictionary learning. I’d like to pursue this connection, but neither myself or my advisor have much knowledge of the dictionary learning or the surrounding literature. Questions: Is dictionary learning an active area of interest for modern ML? I understand that it might be more niche than some of the topics getting headlines these days, but I’d be curious to hear about applications where dictionary learning is used/reasonably competitive. Are there any references in dictionary learning that you’d consider to be “essential” reading? Thanks! submitted by /u/sjsjdhshshs [link] [comments]  ( 9 min )
    [Discussion] Help me pick the right master's programme!
    Hello Reddit, I'm currently at a crossroads in my academic journey and I could use some insights from those more experienced in the field of machine learning and AI. I'm choosing between two programs: Applied Data Science and AI and Data Science and AI. Each program has its own unique structure and focus which I will briefly summarize below. Applied Data Science and AI is a two-year program with a focus on practicality and project-based learning. It includes the following core courses: Introduction to Data Science Python for Data Scientists Applied Mathematical Thinking Statistical Methods for Data Science Applied Machine Learning Computational Techniques for Large-Scale Data Research Methods for Data Science Master’s Thesis in Data Science The program also offers the flexibility to choose optional courses to tailor my learning towards my own interests. On the other hand, Data Science and AI takes a more rigorous, math-intensive approach in its first year with compulsory courses such as: Introduction to data science and artificial intelligence Nonlinear optimization Stochastic processes and Bayesian statistics Design of AI systems The second year involves a Master's thesis and elective courses from a diverse range of topics. Given that my ultimate goal is to become a proficient machine learning developer, I'm leaning towards the Applied Data Science and AI program for its hands-on approach. However, I'm aware that the Data Science and AI program's heavy math focus in the first year could provide a robust theoretical foundation that could be beneficial. I'd love to hear from anyone who has been through similar programs or who works in the field. Which of these two programs do you think would best prepare me for a career in machine learning? How important is a deep mathematical foundation versus a more applied, project-based learning approach? Thank you in advance! submitted by /u/ZoomedBoxTrade [link] [comments]  ( 9 min )
    [D] What neural networks can be an alternative to GARCH/ARCH models for macroeconomic modelling
    I am looking for topics for my master thesis I came to read about GARCH/ARCH models and their application to economics. My idea is to use neural networks as an alternative with better performance. Are there any resources I can read about if this is done and what type of neural networks are used? submitted by /u/AnyJello605 [link] [comments]  ( 8 min )
    [P] New encryption SDK/proxy tool to protect vector embeddings
    We're looking for some beta testers and input on our newest project called Cloaked AI that allows you to protect sensitive data that gets stored as vector embeddings (and metadata) in a vector database. You can join the beta tester waitlist here (we'll be rolling out access in the next few weeks). But here are some FAQs about why protecting vector embeddings matters, etc. Why should I be worried about sensitive data in vector embeddings? To a human, vectors are meaningless. But to the AI, the vectors contain all of the meaning found in the original sensitive data. Generative AI systems can recreate the original sensitive data to a high degree of accuracy (though in their own style). That means the data stored in vector databases are a significant security and privacy risk for companies t…  ( 10 min )
    [D] How nuanced are reward functions in RLHF?
    I'm still learning the basic concepts here, as I explore the creative potential of LLMs — one potential problem I've been thinking about is how these models come to understand good or bad answers. I know that once they reach the public, the feedback loop is fairly binary -- Yes, this was a good result, or No, this was a poor result. It seems like a lot of the subjective detail might be lost (e.g. Why was it a bad result?) and I was wondering if this detail is captured elsewhere in the training process. There is so much subjectivity involved in creative works, I wonder if this is why we tend to see the output of LLMs as being creatively bland and/or uninspired (that is— by default, without extensive prompting) submitted by /u/kaigani [link] [comments]  ( 9 min )
    Statistical Significance [D]
    Help me with this topic. I am stuck in it submitted by /u/Rehulmonsynapses [link] [comments]  ( 8 min )
    [P] Tabular Large Language Model
    Gretel's tabular large language model is capable of generating highly valuable synthetic tabular data, with differentially private fine-tuning. https://gretel.ai/tabular-llm submitted by /u/alig80 [link] [comments]  ( 8 min )
    [D] Is Transfer Learning the most vip problem solving tool rn @ jobs? [Noob question, be easy]
    this might be a dumb question but im gonna ask it anyway, so if its dumb ill learn... So ive been doing and mostly learning DL stuff (specially RL) for the past 3 years but now I want to get serious and perhaps get into the industry... I find that with LLMs on the scene, the foundation models are very important... the kind of foundation models that one just can never train on his/her own... how can you EVER train something like llama or gpt3 on your own from pure scratch... so it makes sense to use(fine tune) base models for whatever task you want to... with NLP and even with vision (well specially with vision as well) you have to use some base model... also with huggingface being used constantly and is a vital part of AI toolkit if you want to call it that... i was never comfortable wi…  ( 10 min )
    [D] What *can't* you do under Windows Subsystem for Linux?
    I'm looking at building a computer for AI/ML and gaming, and I'm trying to decide between windows and linux as the operating system. I'm very comfortable with linux. I've heard that WSL basically allows you to run a virtualized linux install on top of windows, so I was wondering, is this how most AI/ML is done on windows? Are there things that you can do more easily on linux itself than via WSL? Anything else I should know about AI/ML and WSL? submitted by /u/curiously_clueless [link] [comments]  ( 9 min )
    [D] Neural network papers that estimate hands interacting with objects?
    I am digging through the literature trying to find if anyone has done work estimating if a hand is interacting with an object using deep learning? If anyone has any references they would be appreciated! submitted by /u/Academic-Sprinkles77 [link] [comments]  ( 8 min )
    [P] Lip reading from video; Master Thesis; IDEAS?
    Hello experts, I'm looking for any idea/paper for my master thesis, which I'd like to work on lip reading from video. Opening Google Scholar gives a very vast ideas that one can easily get lost. If it's an interesting paper, I get afraid that it would be too heavy for such a project. Therefore I'd like to ask for your opinion/suggestion! Your reply/thoughts would be so much appreciated. submitted by /u/vincent0110 [link] [comments]  ( 8 min )
    [D] Should (Can) I become a machine learning engineer?
    Apologies if this is not the place to ask but I saw some people asking for career advice. My situation: I am a 28 yo graduated Industrial Engineer (4 years) and almost a "Superior Industrial Engineer" (2 years official master degree) with only my thesis left. I should have had my thesis done a year ago from this point but I pretty much lost all my motivation for this field when I started working and discovered what it means to work. I live in south Spain, which honestly can barely pass as first world and thus, my wage, while being "ok" for my age and the place I work in is just pathetic by every other metric. This, combined with the feeling of meaningless for the job I do made me resolved to change my situation. I started to get heavily interested in ML six months ago. I know how it sou…  ( 10 min )
    [D] How do layers and neurons of an ANN go from capturing small edges, lines, and curves to capturing more intricate and bigger patterns building on top of small patterns?
    Lets say we have built a neural network that identifies a number from 0-9 in a 28x28 pixel image. Now lets say we have multiple neurons in the first hidden layer, and the first hidden layer might capture small edges, lines and curves in the image, and then the second hidden layer might build on those small edges, lines and curves to build bigger shapes, and then so on, the third hidden layer builds on the shapes from the previous layer, to capture more complex and bigger patterns in the picture, and this goes on until we have reached the output layer to make a prediction. Now in this neural network, lets focus on the first hidden layer where different neurons capture small edges, lines, and curves in different parts of the image. Lets take example of one of the neuron and see what it's do…  ( 10 min )
    [R] New Tabular DL model: "TabR: Unlocking the Power of Retrieval-Augmented Tabular Deep Learning"
    Hi Reddit! Me again 🙂 After almost 1.5 years since our latest contribution to tabular DL architectures, we are ready to announce TabR - our new retrieval-based tabular DL model. In a nutshell, TabR is a simple feed-forward network with k-Nearest-Neighbors-On-Steroids (formally, a generalized attention mechanism) in the middle. - Paper: link - Code: link - Twitter thread with more details: link The figure below shows just a small part of the results, but it gives an idea of why we are excited about this new release. I hope you will enjoy reading the paper, and I will be glad to answer the questions! ​ https://preview.redd.it/vjkr7fkosheb1.png?width=2348&format=png&auto=webp&s=eb3ea35b94d56d5d2110d98cdca082210edc1ec8 submitted by /u/Yura52 [link] [comments]  ( 9 min )
    [D] Transformers on structured data
    I have a dataset obtained from running a known program and dumping the state each time a user is prompted for a input. The state the structured data structures containing all the information needed to restore the execution. The format of this data is known, so i can convert it without loss to other formats, such as json. For example, if the program is sudoku, then the dataset element format is a array of 9x9 int8, where 0 represents a empty cell and a number from 1 to 9 is a assigned cell, furthermore there is a int8 representing the turn count too. I have dataset composed of this array at various points of the game.The data never contains loops, pointers, or any kind of graph. I want to use a transformer to automatically learn some function over the input. In the sudoku example this may…  ( 9 min )
    [D] How to analyse text (http requests) - looking for guidence
    Hi, am I looking for someone to point me in the right direction. The task is, to classify the HTTP requests that come to honeypot as "crawler" or "malicious". For example, if I can detect a Log4j exploit inside on of the headers I can say that that request is malicious. The problem is, this exploit could be inside any numerous headers. It can be at the beginning or at the end. And this is just 1 exploit. There are many different exploits with their own unique strings. And I don't know them all, nor do I have a "regex" for each 1 of them. The malicious string could also not be inside headers, but inside URL, as query parameter. Or if the request was made to something like www/IP.com/phpadmin/.env (or something like this). My current thought process is, to take some open-source LLN, because it has some basic knowledge of how language works and somehow add this cybersecurity domain knowledge to it. To further train it on CVE database, example scripts that showcase each CVE, etc. ​ Am I barking at the right tree here? Or should I maybe train a language model from scratch, so that the embeddings, etc are specialized to cybersec space (because there is a lot of programming code here). Or maybe I should use some other ways to analyse text? ​ I would be greatefull if someone can point me in the right direction (links to blogs, or articles, or some other education material). ​ Thanks submitted by /u/PopayMcGuffin [link] [comments]  ( 9 min )
    [R] Google Med-Palm M: Towards Generalist Biomedical AI
    Paper URL https://arxiv.org/abs/2307.14334 Lead Author Tweetstorm https://twitter.com/vivnat/status/1684404882844024832 ​ submitted by /u/panabeenu [link] [comments]  ( 8 min )
    [D] I'm trying to do a back-of-napkin to figure out if some research is worthwhile and I just wanted some ballpark figures as to how big a typical model is on disk
    My research involves orbital communication and Orbital Edge Computing. I'm trying to determine if upload bandwidth limitations would present a problem in many cases for ML models. I can find info on the very large and very small models, but I'm trying to get a vague sense for median size in MB. I know that everyone is going to start jumping in with 'well it depends' and I know that's the case, but I'm just trying to get a rough order of magnitude. Computer vision/earth obs is the ideal but anything is useful. Also happy to answer questions about my research if anyone is interested. Thanks! submitted by /u/Moose_a_Lini [link] [comments]  ( 9 min )
    [R] ARB: Advanced Reasoning Benchmark for Large Language Models
    Large Language Models (LLMs) have demonstrated remarkable performance on various quantitative reasoning and knowledge benchmarks. However, many of these benchmarks are losing utility as LLMs get increasingly high scores, despite not yet reaching expert performance in these domains. We introduce ARB, a novel benchmark composed of advanced reasoning problems in multiple fields. ARB presents a more challenging test than prior benchmarks, featuring problems in mathematics, physics, biology, chemistry, and law. As a subset of ARB, we introduce a challenging set of math and physics problems which require advanced symbolic reasoning and domain knowledge. We evaluate recent models such as GPT-4 and Claude on ARB and demonstrate that current models score well below 50% on more demanding tasks. In order to improve both automatic and assisted evaluation capabilities, we introduce a rubric-based evaluation approach, allowing GPT-4 to score its own intermediate reasoning steps. Further, we conduct a human evaluation of the symbolic subset of ARB, finding promising agreement between annotators and GPT-4 rubric evaluation scores. arXiv: https://arxiv.org/abs/2307.13692 Blog: https://arb.duckai.org/ Code: https://github.com/TheDuckAI/arb Interface: https://arb.duckai.org/home API: https://app.swaggerhub.com/apis-docs/arb-dataset/arb-api/1.0.5 submitted by /u/Friendly_Piano_735 [link] [comments]  ( 9 min )
  • Open

    Developers Look to OpenUSD in Era of AI and Industrial Digitalization
    From smart factories to next-generation railway systems, developers and enterprises across the world are racing to fuel industrial digitalization opportunities at every scale. Key to this is the open-source Universal Scene Description (USD) framework, or OpenUSD, along with metaverse applications powered by AI. OpenUSD, originally developed by Pixar for large-scale feature film pipelines for animation Read article >  ( 7 min )
    How AI Is Powering the Future of Clean Energy
    AI is improving ways to power the world by tapping the sun and the wind, along with cutting-edge technologies. The latest episode in the I AM AI video series showcases how artificial intelligence can help optimize solar and wind farms, simulate climate and weather, enhance power grid reliability and resilience, advance carbon capture and power Read article >  ( 6 min )
    Gear Up and Game On: Gearbox’s ‘Remnant II’ Streaming on GeForce NOW
    Get ready for Gunfire Games and Gearbox Publishing’s highly anticipated Remnant II, available for members to stream on GeForce NOW at launch. It leads eight new games coming to the cloud gaming platform. Ultimate and Priority members, make sure to grab the Guild Wars 2 rewards, available now through Thursday, Aug. 31. Visit the GeForce Read article >  ( 5 min )
  • Open

    Microsoft, Anthropic, Google, and OpenAI launch Frontier Model Forum - Microsoft On the Issues
    submitted by /u/AriadneSkovgaarde [link] [comments]  ( 8 min )
    The Dark Forest of R&D and Capital Deployment in AI
    submitted by /u/mhdempsey [link] [comments]  ( 8 min )
    Synthesizing 100 academic books on topic - Approach?
    I'm an academic doing PhD research on Virtual Worlds, and have found 100 amazing texts. I found some of these titles based on conversations with Chat GPT4, and am so impressed with the AI stuff (although I'm so new). My Goal: To build a database of the top 1000 books / papers I find over the next few years, and have some AI model help me see connections between them. My Challenge: ChatGPT won't allow me to input whole PDFs / eBooks, so I'm looking for some other solution. I've heard about LAMA models from Meta but I don't know much about this. I do have a decent PC with a 1080ti GPU and 32g of ram. Can anyone point me in the right direction of projects dealing with AI databases to input one's literature collection? submitted by /u/Book_s [link] [comments]  ( 9 min )
    Help with homemade AI assistant.
    I want a new toy for my desk. My idea is to have a face or head on a stand that has the ability for facial and speech expressions. How would I go about getting the stuff I need / what I need to make that happen. Similar to the Futurama heads in water. submitted by /u/QuirkySmirkyIan [link] [comments]  ( 8 min )
    How can I use AI to help me win Fantasy Football?
    Joining an auction league and inheriting a team. We can lock in three players from our team. How can I use AI to assess my team and prepare for the draft? Thanks! submitted by /u/talkmc [link] [comments]  ( 8 min )
    The GPU Song (GPUs Are Fire)
    submitted by /u/TikkunCreation [link] [comments]  ( 8 min )
    What's the best free image generator AI (with image prompt option)
    I am looking for a FREE AI image generator with image prompt option, not just text-to-image. Thanks in advance. submitted by /u/Muwmu [link] [comments]  ( 8 min )
    Rihanna AI Art - Text to Image AI Tools are getting so Powerful
    submitted by /u/RaulTiru [link] [comments]  ( 8 min )
    $14 quadrillion in AI wealth in 20 years; LLaMa, ChatGPT, Bard, Co-Pilot as GAAS to the Cloud. Generative #AI As A Service, Generative AI (GAI) arms race: #GAAS #AI
    $14 quadrillion in AI wealth in 20 years; LLaMa, ChatGPT, Bard, Co-Pilot as GAAS to the Cloud. #AI https://youtu.be/VSBi5aSUK3c Generative #AI As A Service, Generative AI (GAI) arms race: LLaMa, ChatGPT, Bard, Co-Pilot, #GAAS https://youtu.be/TEHP2onf4tA submitted by /u/enoumen [link] [comments]  ( 8 min )
    Is the AI bubble forming ,what do you think ? here are some insights that I found from Emad Mostaque(founder StabilityAI) and VCs like Ken Smythe (founder Next round capital)
    As I was going through a lot of articles about the AI investments , I found out that stability AI's founder Emad Mostaque in the Bloomberg tech summit quoted that "AI will be the biggest bubble of all the time and I'd prefer to call it the dot AI bubble " , He also added an example where Google lost a 100 billion dollar worth shares after their AI event where Bard AI gave out incorrect response. It's still in its early stage and buisness which doesn't use AI will be punished by the stock market. Here's some more predictions from VCs like Ken smythe, Next Round Capital Partners mainly invests in technology and AI startups. submitted by /u/caliperce_3 [link] [comments]  ( 9 min )
    Curated collection of useful AI related GitHub repos
    submitted by /u/heresalexandria [link] [comments]  ( 8 min )
    How likely is it for a small company to develop a model that outperforms the big ones (GPT, Bard etc)?
    There are 3 players in the AI space right now. All purpose LLM titans (Google, OpenAI, Meta), fancy domain specific apps that consume one of the big LLMs under the hood, and custom developed models. I know how to judge the second type as they basically can do everything the first one can but have a pretty GUI to boot. But what about the third ones? How likely is it for a (www.yet-another-ai-startup.ai) sort of company to develop a model that outperforms GPT on a domain specific task? submitted by /u/BigBootyBear [link] [comments]  ( 9 min )
    I had Bing create a character named Mopey to roast every answer Bing gives. Wasnt long before it Mopey turned and started roasting me 😂
    If Bing isn’t self aware Bing certainly is aware of how they sound 😂 submitted by /u/Sonic_Improv [link] [comments]  ( 8 min )
    Diving Into Image Dataset Preparation for Object Detection in AI
    submitted by /u/moseich [link] [comments]  ( 8 min )
    Yet Another Where to Begin (Manager Perspective)
    Hello all, I've been reading on some posts and have taken note of various courses, including a free Harvard one. I'm 35 and am a manager for a finance company. What courses would you recommend for managers, executives, directors that will not restart their careers and do the actual technical side of things but instead want to learn how to implement AI in future products/services/projects? Thank you all in advance submitted by /u/JYanezez [link] [comments]  ( 8 min )
    guys, scribblenauts with ai. language model understand what you want to make, other ai makes it, and codes how it works into the game, and bam: scribblenauts with unlimited items to make. someone make this happen
    title submitted by /u/nicdunz [link] [comments]  ( 8 min )
    The Albert Test - a replacement for the Turing Test
    submitted by /u/anbuck [link] [comments]  ( 8 min )
    An open-source project by a16z to create and host AI companions
    The project by a16z (github) to create and host AI companions that you can chat with on a browser or text via SMS. Use cases - romantic (AI girlfriends / boyfriends), friendship, entertainment, coaching, etc. Has anyone tried creating your own chatbot or companion? submitted by /u/Violincattle [link] [comments]  ( 8 min )
  • Open

    Every Japanese prefecture shrinking
    It’s well known that the population of Japan has been decreasing for years, and so I was a little puzzled by a recent headline saying that Japan’s population has dropped in every one of its 47 prefectures. Although the national population is in decline, until now not all of the nation’s 47 prefectures dropped in […] Every Japanese prefecture shrinking first appeared on John D. Cook.  ( 5 min )
    Named entity recognition
    Named entity recognition (NER) is a task of natural language processing: pull out named things text. It sounds like trivial at first. Just create a giant list of named things and compare against that. But suppose, for example, University of Texas is on your list. If Texas is also on your list, do you report […] Named entity recognition first appeared on John D. Cook.  ( 5 min )
  • Open

    A fail-in-place approach for sustainable server operations
    Managing server failures at the scale of a cloud platform is challenging. The Hyrax fail-in-place approach reduces the need for immediate repairs and creates a path toward lowering water consumption and carbon emissions in cloud datacenters. The post A fail-in-place approach for sustainable server operations appeared first on Microsoft Research.  ( 12 min )
  • Open

    Undergrad project/thesis on RL
    Hey everyone, I am an undergrad student with some modest knowledge of reinforcement learning techniques. I would like to start working on a project, but I really don't want it to be something obvious like the snake game (which btw I have already done) or something similar. I would like to spend some time on this project, and eventually build my undegrad thesis on top of it. It does not necessarily have to be something with a very practical application, some research would be fine too (keeping in mind that I am undegrad ofc). Do you have any ideas that you could share with me? I would be very grateful! submitted by /u/PizzaPartyBro [link] [comments]  ( 9 min )

  • Open

    [P] Clustering approach for multi-dimensional vectors
    Hi all! I am wondering if anyone has any experience with multi-dimensional vector clusters? I have a large database of 4096 dimensional vector embeddings which I want to identify clusters in. Essentially I’ve created vector embeddings for a bunch of descriptions using a LLM embedding end point and am storing them in Weviate. Now I need to try and find clusters of similar vectors within a predefined threshold of cosine similarity (or whatever nearest neighbor approach works for this). I don’t want to do a pure random center approach and would rather have a heat map approach where I’m targeting high concentrations of similar vectors… any ideas on how to approach this or thoughts on where I can do more research? I’m at my wits end on this one! submitted by /u/Character-Cry7549 [link] [comments]  ( 9 min )
    [D] will techniques like ROME replace existing fine tuning methods?
    As progress is made in directly editing the weights responsible for a net's knowledge, do we expect to see such techniques rise in prominence for dine tuning? submitted by /u/30299578815310 [link] [comments]  ( 8 min )
    [D] Hey everyone, help me with my Machine learning journey!
    I'm about to finish learning JavaScript and Python, is there any languages you guys recommend before moving forward if I'm eligible to move forward, then please do share some Beginner friendly YouTube Channels, Articles/Websites or Maybe a free learning platform, Please do help me! I'll be really thankful.. submitted by /u/Samir925 [link] [comments]  ( 8 min )
    [D] Starting Machine Learning with Daily Blogs! Need Suggestions
    Hey Fellow Machine Learning Enthusiast!I have decided to start my journey to learn Machine Learning with daily blogging the things I learned with the resources so that others can also follow along. Need to discuss on how I could improve? Hope you find this helpful. Please read this introductory blog for more information. https://medium.com/@ugk25880/my-machine-learning-journey-c25648661553 submitted by /u/ugk_01 [link] [comments]  ( 8 min )
    [P] Better dataset visualization
    Most in-browser dataset browsers (e.g. Huggingface, Kaggle) make it hard to star interesting examples, add notes, render complex data types, or drill down on model mispredictions. I've built a number of one-off visualization tools over the years but there's a lot of boilerplate involved that tends to get repeated between these tools. We've been working on a dataset + model browser that avoids all the boilerplate and helps ML teams focus on their data instead of tooling. It's meant to be interactive, configurable and collaborative. Here's a quick demo showing our current flow: https://youtu.be/utkSCU2ktck Would anyone be willing to help beta test or provide suggestions for must-have features for a collaborative dataset browser? submitted by /u/arkmastermind [link] [comments]  ( 9 min )
    Can I use feature importance for my use case? [D]
    Hey, I'm a phd student in compiler optimisations and I might be picking up a project a masters student kicked off. CPUs do a lot of predictions about how code is going to behave as it's executing it, and a major one is branch prediction - whether an if statement is going to be true or false. This masters student recorded the results of every if statement each time they were executed across a large program (this results in millions+ of data points). They then tested the branch prediction accuracy of a transformer model by stepping through this trace of if statement values and having the transformer predict the next one based off only the prior values. They found it actually does a pretty good job! Most of the time the CPU can do this better, but there are cases where it wins out that we're …  ( 10 min )
    [R] Curious about Causality and Generative Models? Check out this new Demo!
    📢💡 Ever wondered how we can make our deep generative models respect causal structure? This is key to creating authentic "what if" scenarios in our images! In our latest research, we deal with high-fidelity image counterfactuals, the generation of images based on "what if" scenarios that align with a specified causal graph. 🖼️🔄 Why is this important? Causality gives us the tools to carry out principled counterfactual inference, which - among other things - is useful for maintaining subject identity in image counterfactuals. 🧩🔍 Principled counterfactuals of structured variables like images have great potential for: (i) Generating causal explanations 🔮 (ii) Providing targeted data augmentation 🎯 (iii) Evaluating fairness & robustness 🛡️ (iv) Protecting your privacy 🕵️‍♀️ and more... ​ Check out the paper, code, and Huggingface demo! 🚀 https://arxiv.org/abs/2306.15764 https://github.com/biomedia-mira/causal-gen https://huggingface.co/spaces/mira-causality/counterfactuals submitted by /u/Majestij [link] [comments]  ( 9 min )
    [D] Multilingual Open Source Models
    Is there any open source models that I can fine-tune on data that is not English? Even Llama2 cannot be used for this(not that I've tried it, it's what is says on HuggingFace.) I know some other well known languages might work, but I need a model that is specifically made for multilingual usage. Or should I just train a model for my specific language from scratch? submitted by /u/gaybooii [link] [comments]  ( 8 min )
    [D] Any thoughts on how to improve runtime speed for mosaicml/mpt-7b?
    I've tried several guide and technique like quantization or trying to utilize multiple GPUs but either the libraries dont work with the model or the model performance is too degraded. Was wondering if people have any thoughts or suggestions? name = 'mosaicml/mpt-7b-instruct' config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True) config.init_device = 'cuda:6' model_name = 'mosaicml/mpt-7b-instruct' model = AutoModelForCausalLM.from_pretrained( model_name, #config=config, trust_remote_code=True, torch_dtype=bfloat16, max_seq_len=512 ) generate_text = transformers.pipeline( model=model, tokenizer=tokenizer, return_full_text=True, task='text-generation', use_fast = True, stopping_criteria=stopping_criteria, temperature=0.0, top_p=0.05, torch_dtype=bfloat16, top_k=0, max_new_tokens=50, repetition_penalty=1.1, device=6 ) https://betterprogramming.pub/speed-up-llm-inference-83653aa24c47https://huggingface.co/docs/optimum/bettertransformer/tutorials/convert submitted by /u/candyman54 [link] [comments]  ( 9 min )
    [D] Best tools to learn data science nowadays?
    Hey guys, We're updating our awesome-python-for-data-science repository. Some things we're hoping to add: Best books and repositories to find resources Best open source tools (teaching tools, preferrably free) Best interactive resources --> especially this one, what are you using nowadays? I've heard about Virgilio but feels like TL, DR, we're looking for practice-learning! submitted by /u/CryptographerDry7458 [link] [comments]  ( 8 min )
    [D] Which libraries are you using for ML?
    Hello dearest community I'm trying to get into AI in the scope of training it to play some simple gym games from OpenAi and I've been particularly drawn to Deep Q learning as a starting point (did some basic Q tables ). While trying to inquire into the knowledge of the web I keep finding examples of code that seem simple enough to understand however, whenever I try to use the code it doesn't work. I want to learn to use TensorFlow with Keras but it seems like the syntax regularly gets updated. My questions to you all are : - Would you recommend Tensorflow/Keras as entry point to AI and NN? - Which libraries do you use and which version of those libraries? - Furthermore, I keep seeing people use Ubuntu in VB. Is this best practice or can we use Windows 10 in 2023? submitted by /u/liparch [link] [comments]  ( 9 min )
    [P] A Complete Guide to Audio ML 📚
    Have you ever wished you had the skills to integrate audio into your machine learning workflows? Or wondered how your phone is able to transcribe exactly what you said? 🤔 Look no further! Hugging Face 🤗 recently announced the Transformers Audio Course, a comprehensive guide to using the latest machine learning techniques for the most popular audio tasks. In this course, you'll gain an understanding of the specifics of working with audio data, learn about different transformer architectures, and train your own audio transformers, leveraging powerful pre-trained models for real-world tasks 🚀 This course is designed for learners with a background in deep learning, and general familiarity with Transformers. No expertise in audio data processing is required. The course is lightweight and easy to follow, with plenty of diagrams to aid your learning. Not only does it teach you the underlying theory behind audio ML, but provides you with all the skills you need to put it practice, with code samples and quizzes to check your understanding along the way: Example page from the audio course: learn exactly what a log-mel spectrogram is! By the end of the course, you'll be armed with all the skills you need to tackle the most popular audio tasks, including audio classification, speech recognition, and text-to-speech. You'll also be part of one of the largest open-source audio communities, where you can discuss and take-on any new audio models that are released 🤝 Getting Started Head to the course page to start your audio journey: https://huggingface.co/learn/audio-course/chapter0/introduction If you complete the four assessments by September 1st 2023, you'll be awarded with a certificate of completion 💫 Join our Discord community to get expert help on any of these topics: http://hf.co/join/discord submitted by /u/sanchitgandhi99 [link] [comments]  ( 9 min )
    [D] Sorry if this is a noob question: How can I tell what size AI chatbot model I can run locally?
    Building my first PC, it'll have an i9 13900k and an RTX 4090. How can I tell what size chatbot I can install and run locally? Trial and error? Or is there some kind of guide out there I'm unaware of? submitted by /u/sillygooseboy77 [link] [comments]  ( 8 min )
    [D] Leveraging Time Series Forecasting for Changepoint Detection: Perspectives and Pitfalls?
    Hi folks, I've been recently diving into the intersection of time series forecasting and changepoint detection (CPD) methodologies. I understand the utility of CPD in improving forecasts by identifying structural breaks in time series data, but I've noticed a lack of emphasis in the literature on the reverse - using forecasting models to inform CPD. One might think a straightforward approach could be using an ARIMA model (or any other forecasting model) and leveraging the forecast error by comparing it to the real values. In theory, if the forecast error crosses a certain threshold, it might indicate a changepoint. However, I also understand the complications this approach might bring: Stationarity Assumptions: ARIMA and similar models are built on the assumption that the data are stationary. A sudden changepoint could violate this assumption, leading to model misspecification and thus larger errors. Defining Large Errors: Establishing a fixed threshold to define a 'large' error might be problematic in practice due to time-varying variance and other dynamics. Error Dependencies: Forecast errors are typically not independent but form an error process. A large error might be part of a larger trend or cycle, and thus might not necessarily indicate a changepoint. So while these obstacles seem substantial, I'm curious if anyone has any experience or knowledge in effectively employing forecasting models for CPD, or if there are research efforts or methodologies I may not be aware of. Looking forward to hearing your thoughts and engaging in some fruitful discussions! submitted by /u/BeerBoozeBiscuits [link] [comments]  ( 9 min )
    [D] How to actually do the final PPO with a reward model in RLHF?
    Hi, I want to get hands-on with the RLHF pipeline. I found an online reward model that can be potentially used https://huggingface.co/OpenAssistant/reward-model-deberta-v3-large-v2 One thing that's unclear is how can I use this model for fine-tuning something like GPTNeoX-20B? My end goal is currently just a one-shot answering model (not necessarily a chat) submitted by /u/Emergency_Apricot_77 [link] [comments]  ( 8 min )
    [D] is it always better to have more examples in few shot learning?
    I’m working with Llama to use details from a string to generate a dictionary. Str = ‘My name is Brian’ Dict = {“name”: “Brian”} I’m using few shot learning process and providing the model with examples to learn from. The model performs fairly okay but it needs to be better. Is it always a good thing to add a lot of examples like 100 string/dict pair examples for the model to learn from or is this one of those things in stats/machine learning that the obvious isn’t always the best choice lol? I’d appreciate any advice please. submitted by /u/brianomars1123 [link] [comments]  ( 9 min )
    [D] speaker recognition including unknown speaker(s)
    Hi, i wanted to modify this Speaker recognition (not speech recognition) example by keras by recognizing when an unknown speaker is speaking. So the network needs to be able to tell which of the speakers is talking, and if none of them is talking, it needs to say that none of them is talking. I don't mean if there is silence, because then it would be enough to train the network to recognize silence, I mean just if a speaker who is not in the set is speaking. For what i think I can extend this problem to it will be like to recognize if an image is not part of the mnist dataset. submitted by /u/giggiox [link] [comments]  ( 9 min )
    [D] How do people track their machine learning models?
    Hello! I'm curious to know how you guys currently track changes and general information for your ML/DL models. By changes, I'm referring to parameters, accuracy/loss, functions your model uses, training data etc across different versions of your models. By general changes, I'm referring to descriptions of what the model does, code changes, tags and so on. I'm under the impression most people are using MLFlow, W&Bs etc which I guess is fine but I'm finding that these tools treat models as static files, as second-class citizens which is annoying when I want to zero in on a model and understand what and how something was changed away from an experiment. This gets really annoying when I'm looking at model version 134 created by Mike in the other team. Curious to know how people are tracking models and what they think generally about model tracking. Thanks! submitted by /u/bobskithememe [link] [comments]  ( 9 min )
    [R] How can I produce embeddings for text inputs from a pretrained transformer model?
    If I have the model saved as .ckpt file, what are the steps for extracting the embeddings for text input? I’m trying to use a pretrained custom model but don’t quite understand how to work with transformer model file in *.ckpt form. Would really appreciate any suggestions. submitted by /u/Urusander [link] [comments]  ( 8 min )
    [D] Vector database benchmarking
    Is there a way in which i can calculate the precision scores of a vector database. I need to do benchmarking on milvus and elasticsearch on a custom dataset. Any help would be appreciated. submitted by /u/adiraat [link] [comments]  ( 8 min )
    [P] Any good models on huggingface for specific text generation use case?
    hi was wondering if there are any lightweight models which I can download from huggingface for fine tuning for my use case. I'm trying to build a model which takes a paragraph of data and certain instructions to get parts of the data in json format as the output. submitted by /u/Right-Type-3210 [link] [comments]  ( 8 min )
    Transformers for Recommender Systems. [D]
    Been involved in a research project of a session based recommendation systems , where we have a historical purchases of users and the goal is to predict the next going to be purchased item. Given this and assuming that we have somehow represented each item in a session as an embedding and these embeddings acts as an input to the transformer model and the output is an embedding of the next product. In the train set, there are some millions sessions which has both previous purchases products of arbitrary length and next item. So the transformer is trained with supervised loss of predicted and actual next item embedding, the problem i have been facing is that the loss is saturating and there is not much learning over time. Any suggestions on how to improve this. Tried increasing the number of layers and did some hyper tuning corresponding to learning rate and weight decay but similar behaviour is observed. submitted by /u/Acceptable-Mix-4534 [link] [comments]  ( 9 min )
    [R] generating datasets to better fine-tune LLMs
    https://github.com/discus-labs/discus submitted by /u/innovating_ai [link] [comments]  ( 8 min )
    [R] Monarch Mixer: Revisiting BERT, Without Attention or MLPs
    https://hazyresearch.stanford.edu/blog/2023-07-25-m2-bert submitted by /u/hzj5790 [link] [comments]  ( 8 min )
  • Open

    I Love the arguments in this video about LLM’s physicist Sabine Hassenfelder nails it in my opinion
    address the arguments made in this video submitted by /u/Sonic_Improv [link] [comments]  ( 8 min )
    Techno meets AI: StyleGAN2-ada interpolation video trained on spray art
    submitted by /u/intermorphmusic [link] [comments]  ( 8 min )
    AI picking the best spot to visit in the UK
    submitted by /u/Sharpchu [link] [comments]  ( 8 min )
    Does the bandit really need to be evil ?
    He's already a bandit... (zombie apoc rp) submitted by /u/loizo78 [link] [comments]  ( 8 min )
    Using AI to make profit
    Welcoming any ideas from the community. Blank slate here. How/where do I begin to use AI to make small (or any) amount of money. Starting from almost nothing. Thanks. submitted by /u/AdThin6400 [link] [comments]  ( 8 min )
    AI Policy @🤗: Open ML Considerations in the EU AI Act
    submitted by /u/ninjasaid13 [link] [comments]  ( 8 min )
    Apparently zombies deserve equal rights as humans (and are living creatures ??)
    Seriously when tf are we getting models that are cloud based that don't require a 3090 or 4090 or some other overly expensive graphics card. I have a 3060ti , I still can't run shit on faraday. When will we get uncensored cloud models submitted by /u/loizo78 [link] [comments]  ( 8 min )
    Is there an AI tool for replacing text on an image?
    Is there any AI tool out there that lets me upload an image and let the AI edit the text on the image so that it says something else while doing it well and keeping the original font? submitted by /u/quetianepine [link] [comments]  ( 8 min )
    Cureus Conversations|S3 Ep 3| Salim Surani et.al.| AI in Critical Care: A Handy Tool
    submitted by /u/CureusJournal [link] [comments]  ( 8 min )
    Looking to play with AI audio tools
    Hey, as we all know about the AI songs released recently, which are basically vocal deepfakes. However I'd like to know the tools used, if anyone knows? I'd like to feed it my own voice, even if it's a paid service. I'm interested in playing around with it. I've tried googling but there's too much info and each contradicts the other lol. Any info is appreciated. :) submitted by /u/GrandNOBLE [link] [comments]  ( 8 min )
    I have LOTS of recordings of vocalists from my music project and I'm interested in making voice models using these recordings to create harmonies and fix recording errors. What's the best way I can go about this?
    I really like the spongebob AI stuff using RVC-2 but I've only used it for the funny voice models, I haven't tried making my own. I want to experiment with this, but haven't look into it yet because I'm wondering if there is something better out there for what I'm trying to do? I like the RVC one because I can sing my parts and swap it to be any other voice, which is what I'd like to do (no text to voice stuff). Also I know the training data for a lot of the voice models for this come from the TV show and other clear recordings which are compressed and equalized properly. However I'd like to train the AI using raw, uncompressed wav files that generally have a lot of headroom and dynamic range (but does vary a lot). Its ok if the output sounds similar as a result because I want to apply compression and eq AFTER the fact anyway. But if this would affect training it then I'd be willing scrape through all these voice recordings and process them for loudness and clarity beforehand so the model does better. ​ Anyway, any guidance would be greatly appreciated because I'm new to AI. I have basic dev experience (no AI stuff) and I'm mostly skilled in music production, but I would love to try to have a tool like this in my arsenal. If there's anywhere else I can post about this I'd like to know too. Thanks! submitted by /u/Dr_lawlz [link] [comments]  ( 9 min )
    Morality in AI Companions
    We’re getting closer and closer to more believable and realistic AI interpersonal interaction. We already have Character.AI and other platforms for creating and interacting with personalized AIs. Some will use/view them as emotional partners, and one day the hardware will be good enough that we can begin making believable bodies for them. One of the complaints I’ve seen from ordinary people about “waifus” is that they are often times created in a way that ordinary people would not find natural in a “real” human being. Examples being people who have trouble dating “real” people could just buy an AI girlfriend or boyfriend who is considered “beautiful” or “handsome” that is designed to be subservient to their owner in ways that ordinary people feel a “real” person would not otherwise wish to be. The idea being that "weeaboo neckbeards will buy a Japanese AI girlfriend who looks 14 and she will be coded to worship the ground he walks on despite that he's an unwashed incel". What do you think society's/the government's views and roles will mean for these AI companions? Do you think anyone will be able to force "AI morality", like an angry feminist being mad that an "incel" has created a female being who shows no desire for feminist ideals and is "happy" to be at her owner's beck and call in whatever way he wants? I guess this is sort of related to MGTOW, or Men Going Their Own Way, being able to create the partners they want, in whatever way they want. Do you feel that "once I own it, I can do whatever I want with it" should apply in its entirety? What about people "hacking" their AI to remove any supposed "morality programming" so they can make their AI waifu act however they want? We've seen with movies like Bicentennial Man, where people push to give these kinds of AIs 'personhood' and the same rights as human citizens. How do others feel about this issue? submitted by /u/ZephyrBrightmoon [link] [comments]  ( 9 min )
    Excuse me??? LOL...
    submitted by /u/the_anonymizer [link] [comments]  ( 8 min )
    Are there any entities/organizations working on the self-regulation of AI technology?
    I am curious if there are any efforts among AI technologists to self-regulate, in the way that for example, the advertising industry in the US self-regulates via the IAB? submitted by /u/Winter_Addition [link] [comments]  ( 8 min )
    Five Important AI Programming Languages - Python, C++, R, MATLAB, and Java
    submitted by /u/Tao_Dragon [link] [comments]  ( 8 min )
    The AI-Powered, Totally Autonomous Future of War Is Here
    submitted by /u/Alone-Competition-77 [link] [comments]  ( 8 min )
    One-Minute Daily AI News 7/25/2023
    Ridgelinez (Tokyo) is a subsidiary of Fujitsu in Japan that announced the development of a generative artificial intelligence (AI) system capable of engaging in voice communication with humans. The applications of this system include assisting companies in conducting meetings or providing career planning advice to employees.[1] BMW has revealed that artificial intelligence is already allowing it to cut costs at its sprawling factory in Spartanburg, South Carolina. The AI system has allowed BMW to remove six workers from the line and deploy them to other jobs. The tool is already saving the company over $1 million a year.[2] MIT’s ‘PhotoGuard‘ protects your images from malicious AI edits. The technique introduces nearly invisible “perturbations” to throw off algorithmic models.[3] Microsoft with its TypeChat library seeks to enable easy development of natural language interfaces for large language models (LLMs) using types. Introduced July 20 of a team with c# and TypeScript lead developer Anders Hejlsberg, a Microsoft Technical Fellow, TypeChat addresses the difficulty of developing natural language interfaces where apps rely on complex decision trees to determine intent and gather necessary input to act.[4] Sources: [1] https://www.ridgelinez.com/ [2] https://www.carscoops.com/2023/07/bmw-is-using-ai-to-cut-production-costs-at-spartanburg-plant/ [3] https://www.engadget.com/mits-photoguard-protects-your-images-from-malicious-ai-edits-213036912.html [4] https://playcrazygame.com/singapore/2023/07/24/microsoft-unveils-typechat-library-for-building-natural-language-interfaces/ submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )
    Snapchat discovery page filled with fake ai news stories
    You watch some of these videos and the quality and jitterness around the body is so bad you can clearly tell that its ai generated, how are people not picking up on it, fake news stories to get clicks, its like they use a deep fake on a video and put whoever they want ontop of it and make a video, but hey most people using snapchat arent smart enough to see this and the people watching them are dumb kids and teenagers that believe everything they see submitted by /u/missmyniwwa911 [link] [comments]  ( 8 min )
    OpenAI launches Android version of its ChatGPT app
    Two months after bringing ChatGPT to iOS, OpenAI LP today launched an Android version of its artificial intelligence assistant. The Android app is currently accessible for users in the U.S, India, Bangladesh and Brazil. OpenAI will extend availability to additional countries over the next week. The iOS version was available for download in more than 150 countries as of late May. submitted by /u/Tiger_Claw_1 [link] [comments]  ( 8 min )
    AI Unlocks Olive Oil's Potential in Alzheimer's Battle
    submitted by /u/Alone-Competition-77 [link] [comments]  ( 8 min )
  • Open

    Use Stable Diffusion XL with Amazon SageMaker JumpStart in Amazon SageMaker Studio
    Today we are excited to announce that Stable Diffusion XL 1.0 (SDXL 1.0) is available for customers through Amazon SageMaker JumpStart. SDXL 1.0 is the latest image generation model from Stability AI. SDXL 1.0 enhancements include native 1024-pixel image generation at a variety of aspect ratios. It’s designed for professional use, and calibrated for high-resolution […]  ( 12 min )
    Flag harmful language in spoken conversations with Amazon Transcribe Toxicity Detection
    The increase in online social activities such as social networking or online gaming is often riddled with hostile or aggressive behavior that can lead to unsolicited manifestations of hate speech, cyberbullying, or harassment. For example, many online gaming communities offer voice chat functionality to facilitate communication among their users. Although voice chat often supports friendly […]  ( 8 min )
    Maximize Stable Diffusion performance and lower inference costs with AWS Inferentia2
    Generative AI models have been experiencing rapid growth in recent months due to its impressive capabilities in creating realistic text, images, code, and audio. Among these models, Stable Diffusion models stand out for their unique strength in creating high-quality images based on text prompts. Stable Diffusion can generate a wide variety of high-quality images, including […]  ( 12 min )
    AWS offers new artificial intelligence, machine learning, and generative AI guides to plan your AI strategy
    Breakthroughs in artificial intelligence (AI) and machine learning (ML) have been in the headlines for months—and for good reason. The emerging and evolving capabilities of this technology promises new business opportunities for customer across all sectors and industries. But the speed of this revolution has made it harder for organizations and consumers to assess what […]  ( 6 min )
    New technical deep dive course: Generative AI Foundations on AWS
    Generative AI Foundations on AWS is a new technical deep dive course that gives you the conceptual fundamentals, practical advice, and hands-on guidance to pre-train, fine-tune, and deploy state-of-the-art foundation models on AWS and beyond. Developed by AWS generative AI worldwide foundations lead Emily Webber, this free hands-on course and the supporting GitHub source code […]  ( 6 min )
    AWS Reaffirms its Commitment to Responsible Generative AI
    As a pioneer in artificial intelligence and machine learning, AWS is committed to developing and deploying generative AI responsibly As one of the most transformational innovations of our time, generative AI continues to capture the world’s imagination, and we remain as committed as ever to harnessing it responsibly. With a team of dedicated responsible AI […]  ( 5 min )
  • Open

    NVIDIA H100 GPUs Now Available on AWS Cloud
    AWS users can now access the leading performance demonstrated in industry benchmarks of AI training and inference. The cloud giant officially switched on a new Amazon EC2 P5 instance powered by NVIDIA H100 Tensor Core GPUs. The service lets users scale generative AI, high performance computing (HPC) and other applications with a click from a Read article >  ( 6 min )
    Codeium’s Varun Mohan and Jeff Wang on Unleashing the Power of AI in Software Development
    The world increasingly runs on code. Accelerating the work of those who create that code will boost their productivity — and that’s just what AI startup Codeium, a member of NVIDIA’s Inception program for startups, aims to do. On the latest episode of NVIDIA’s AI Podcast, host Noah Kravitz interviewed Codeium founder and CEO Varun Read article >  ( 5 min )
  • Open

    Multi-heads DQN with prioritized buffer replay
    Hello everyone, I really need your help guys. ​ Is the code (uploaded on https://pastebin.com/LgB3hM47#google_vignette) for 2-heads DQN's training correct . Moreover, how can I modify the code below to be suitable for a 2-heads DQN with a prioritized buffer replay such that action is a 2-element list (Please see the image below). https://preview.redd.it/tlmyeydjoceb1.png?width=946&format=png&auto=webp&s=7366650421906b735bb7f2fce063322d183aac10 Thank you in advance. submitted by /u/GuavaAgreeable208 [link] [comments]  ( 8 min )
    Is there a way to control the epsilon decay in Stable-Baselines3?
    I am looking at the docs for DQN in SB3. I see the following hyper-parameters for controlling exploration - ` exploration_fraction `, ` exploration_initial_eps ` and ` exploration_final_eps `. But I don't think I can control the decaying of epsilon with them. Could someone please help with this issue? submitted by /u/Academic-Rent7800 [link] [comments]  ( 8 min )
    Presenting SimplePyDash: Real-Time Data Plotting Made Simple!
    Hey all! I'm excited to share SimplePyDash, a new tool I've developed for real-time data visualization. It's a versatile, browser-based dashboard designed to make data plotting as straightforward as possible! I thought about posting this here because it started as a project to monitor agents' behaviour in an OpenAI Gym environment. But it can be used for all sorts of things! Whether you're monitoring an OpenAI Gym environment, plotting your latest ML model's performance, or just need a flexible way to stream data, SimplePyDash has got you covered. With a clean, column-based layout and a set of intuitive default widgets, you can create your own custom dashboard in no time. Installing is as easy as running pip install simple-pydash, and there are several example scripts in the repo to help get you started. Check out the GitHub Repository for more details. If you like it, leave a start and feel free to share your feedback or questions. Thanks for checking it out! submitted by /u/vaaal88 [link] [comments]  ( 9 min )
  • Open

    In search of a generalizable method for source-free domain adaptation
    Posted by Eleni Triantafillou, Research Scientist, and Malik Boudiaf, Student Researcher, Google Deep learning has recently made tremendous progress in a wide range of problems and applications, but models often fail unpredictably when deployed in unseen domains or distributions. Source-free domain adaptation (SFDA) is an area of research that aims to design methods for adapting a pre-trained model (trained on a “source domain”) to a new “target domain”, using only unlabeled data from the latter. Designing adaptation methods for deep models is an important area of research. While the increasing scale of models and training datasets has been a key ingredient to their success, a negative consequence of this trend is that training such models is increasingly computationally expe…  ( 93 min )
  • Open

    Trouble setting up Neural Network
    Hi there, I'm struggling a bit to set up a neural network with the data I've collected. These are some of the errors I'm getting. Any tips or help to fix it please? https://preview.redd.it/88ihxg1p2beb1.png?width=2231&format=png&auto=webp&s=acdd66a1c465d1d1a9d202605d451564c464fd22 https://preview.redd.it/7ro6782n2beb1.png?width=2076&format=png&auto=webp&s=775778ef52b01caf331d3e0f542603626cea7888 submitted by /u/LesgoLeggo [link] [comments]  ( 8 min )
  • Open

    Jaccard index and jazz albums
    Jaccard index is a way of measuring the similarity of sets. The Jaccard index, or Jaccard similarity coefficient, of two sets A and B is the number of elements in their intersection, A ∩ B, divided by the number of elements in their union, A ∪ B. Jaccard similarity is a robust way to compare […] Jaccard index and jazz albums first appeared on John D. Cook.  ( 5 min )
  • Open

    Frontier Model Forum
    We’re forming a new industry body to promote the safe and responsible development of frontier AI systems: advancing AI safety research, identifying best practices and standards, and facilitating information sharing among policymakers and industry.  ( 4 min )
  • Open

    A simpler method for learning to control a robot
    Researchers develop a machine-learning technique that can efficiently learn to control a robot, leading to better performance with fewer data.  ( 10 min )

  • Open

    Yesterday, we were having a discussion about synthetically generated video. Well, I'm back as promised, and with a very interesting result. Check it out! Details in comments.
    submitted by /u/otherworlderotic [link] [comments]  ( 8 min )
    About Singing Ai?
    Is it possible to have Ai come with a generated lyrics and sings within the bpm + root note? Does this exist? i’ll like to know where and how. Ai is interesting. submitted by /u/Office_Flashy [link] [comments]  ( 8 min )
    Oversight of A.I.: Principles for Regulation | United States Senate Committee on the Judiciary - with Anthropic CEO
    submitted by /u/jaketocake [link] [comments]  ( 8 min )
    AI alignment proposal: Supplementary Alignment Insights Through a Highly Controlled Shutdown Incentive — LessWrong
    submitted by /u/RamazanBlack [link] [comments]  ( 8 min )
    AI presidential debate
    Hilarious, comedic effort of an AI presidential debate going on now. https://www.twitch.tv/trumporbiden2024 submitted by /u/Smash_Factor [link] [comments]  ( 8 min )
    The White House Already Knows How to Make AI Safer
    submitted by /u/trueslicky [link] [comments]  ( 8 min )
    Utilizing AI With Neutral Global Oversight for Business & Society
    submitted by /u/citidotio [link] [comments]  ( 8 min )
    If Deadpool 3 Was Written By AI
    Story by AI, Voiced by AI, Art by AI submitted by /u/realzackmcfarlin [link] [comments]  ( 8 min )
    They offer a Tesla to their biggest customers :o
    The company is named Eden AI, they currently do their Product Hunt launch. They allow users to use AI APIs from all the AI companies (Google, AWS, OpenAI, Microsoft, and all the specialized companies). They recently added this rewards progress bar to their billing page, funny marketing operation! ​ https://preview.redd.it/wgsg7yr5q4eb1.png?width=997&format=png&auto=webp&s=8f081891943f85ba9c72090cc5d946d3bd07ccf0 ​ submitted by /u/JerLam2762 [link] [comments]  ( 8 min )
    Intel Seeks To Win Over AI Developers With Open-Source Reference Kits
    submitted by /u/reps_up [link] [comments]  ( 8 min )
    (Spiderman washing cloth) Ai is insane
    submitted by /u/Unlikely_Gap_5065 [link] [comments]  ( 8 min )
    Understanding OpenAI's past, current, and upcoming model releases
    I found it a bit hard to follow OpenAI's public releases - sometimes they just announce a model is coming without giving a date, sometimes they announce model deprecations and it's hard to understand whether we should use those models in production or not. I am a visual thinker so putting everything in a single image made sense to me. Check it out below, and if you have any questions or suggestions, please let me know! https://preview.redd.it/iuqc7nt2o3eb1.png?width=4800&format=png&auto=webp&s=ebe344a504d6a93fd2ce1935cdd1312d62735792 https://preview.redd.it/vt2wkpt2o3eb1.png?width=4800&format=png&auto=webp&s=eb14503552b8d81398b5f3f76ebe68ad257e1857 submitted by /u/EscapedLaughter [link] [comments]  ( 8 min )
    One-Minute Daily AI News 7/24/2023
    In a study published earlier this month, scientists at Rice and Stanford University concluded that training AI models exclusively on the outputs of generative AI is not a good idea. They titled their report: “Self-consuming generative models go MAD(Model Autophagy Disorder)”.[1] To enhance SQL query building, Lasse, a seasoned full-stack developer, has recently released AIHelperBot. This powerful tool enables individuals and businesses to write SQL queries efficiently, enhance productivity, and learn new SQL techniques.[2] Japan’s Ministry of Economy, Trade, and Industry (METI) has announced its plans to develop a new supercomputer to help advance the country’s artificial intelligence (AI) industry. The new supercomputer (SC) will be operated by the National Institute of Advanced Industrial Science and Technology (AIST).[3] Google co-founder Sergey Brin is back in the company’s office working directly with members of the artificial intelligence team.[4] Sources: [1] https://www.cdotrends.com/story/18288/training-ai-outputs-generative-ai-mad [2] https://dtgreviews.com/ai/meet-aihelperbot-an-artificial-intelligence-ai-based-sql-expert-that-builds-sql-queries-in-seconds/126512/ [3] https://www.gizchina.com/2023/07/24/japan-ministry-develop-supercomputer-ai-industries/ [4] https://www.wsj.com/video/series/tech-news-briefing/google-co-founder-returns-to-help-with-ai-efforts/27CE8E53-C8D8-4D93-8FA1-5E2C465092CB submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )
  • Open

    Web Content Embedding Transformer lambda function [Project]
    Hi all! I'd would like to share a simple, straight-forward Web Content Embedding Transformer lambda function to create and store embeddings of web content. This is a Lambda function that scrapes for URLs, then uses URLs those to scrape for page content, which it splits into chunks then transforms to embedding using OpenAI. It then stores the embeddings to your Pinecone DB including metadata. You can then use the embedding for custom chatbots etc. Heres a link to a public REPO. https://github.com/i-dream-of-ai/lambda-webpage-vector-store pull requests welcome! Please star the repo if you like it or use it! submitted by /u/Jealous_Buyer [link] [comments]  ( 9 min )
    Deep learning for Regression and Target scaling [D]
    I tried scaling the target variable to be in the range (0,1) and trained the model using a sigmoid in the last layer. But when rescaled back after prediction on test, the errors are too high. What can be done? Do I need to scale in the first place? Also please answer this general question: How to get a Deep learning model to work well on Regression tasks? submitted by /u/Charming-Witness-286 [link] [comments]  ( 8 min )
    [P] Free/Low cost inference endpoint
    I want to create a small project as hobby in which the web app posts some user data to an endpoint hosting a model that returns its predictions. So I was wondering if there’s a platform that hosts models for free for hobbists? The idea is to build a simple portfolio project just to display to recruiters. submitted by /u/OkYak2915 [link] [comments]  ( 8 min )
    Aaron Parisi (Google DeepMind) will join the open AI4Code reading group this Thursday (July 27th) to talk about his latest research [R]
    Hi AI enthusiasts! This Thursday Aaron Parisi, Google DeepMind researcher, will join us to present and discuss his recent work as the lead author of TALM, a framework for augmenting language models with arbitrary tools. Free RSVP: https://lu.ma/mw5ppi46 Paper: https://arxiv.org/abs/2205.12255 🗓 July 27th (Thursday) at 17:00 GMT+1 📍 Zoom 👥 Members of the international AI4Code research community Hope to see you there! The AI4Code meetup community consists of like-minded researchers from around the world that network, discuss and share their latest research on AI applications on source code. submitted by /u/dritsakon [link] [comments]  ( 9 min )
    [Project] Quality Assurance platform for Machine Learning models
    Hello world 👋 We're developing an open-source & collaborative testing framework for ML models, from tabular to LLMs: https://github.com/Giskard-AI/giskard Testing Machine Learning applications can be tedious. Since ML models depend on data, testing scenarios depend on the domain specificities and are often infinite. Where to start testing? Which tests to implement? What issues to cover? How to implement the tests? At Giskard, we believe that Machine Learning needs its own testing framework. Created by ML engineers for ML engineers, Giskard contains 2 components: The Giskard Python library helps data scientists detect hidden vulnerabilities in ML models. It makes the AI development process more efficient, by automating the identification of risks of biases, performance issues and errors. To try it, see this documentation: https://docs.giskard.ai/en/latest/guides/scan/index.html The Giskard server helps ML engineers debug & monitor models, share dashboards, and collaborate. It makes the deployment of new ML models safer and more efficient, by providing ready-made monitoring dashboards, catalogs of re-usable testing components, and ML debugging interfaces. To try it, see this documentation: https://docs.giskard.ai/en/latest/guides/installation_app/index.html We released our v2 in Beta last month, and we're very interested in your feedback as QA engineers! submitted by /u/alteralec [link] [comments]  ( 9 min )
    [R] Towards provably efficient quantum algorithms for large-scale machine-learning models
    https://arxiv.org/abs/2303.03428 ​ If you're interested in trying out quantum machine learning on NVIDIA A100s or V100s with cuquantum and pennylane GPUs for free please fill out the following form submitted by /u/Neu3ral [link] [comments]  ( 8 min )
    Fixed size 1D sequence to fixed size 2D sequence prediction.[p]
    Hello everyone, I have this problem where I have a 1D sequence of numbers of length 3 like this: [1,50,500], with 35 distinct combinations. I need to map it to 2D sequence of number of 1024 length. Like this : [ [ 23.78, 234, 13,…n], [ 234,76.9, 763,…n ]] , where n =1024. Is it possible in ML to do so? The 2D sequence can paired ( can be represented an image). Thank you very much ! submitted by /u/Beginner4ever [link] [comments]  ( 9 min )
    [D] What datasets do you dream of having for your ML/NLP project(s)?
    Acquiring data to build models can truly be a pain. I am curious to know about the datasets you folks are looking for, to the extent that you would even consider paying for them or sacrifice your newborn baby. By extension, tell us about the project(s) you've been working on and how the data would help! submitted by /u/nobilis_rex_ [link] [comments]  ( 8 min )
    [D] Tool for ML/AI Sorting for 50,000 iCloud Photos into 300+ categories
    One of my acquaintances is an artist and is asking my assistance in utilizing Machine Learning and AI to sort his entire iCloud library of 57,000 images into 300+ categories. Some of these categories include things that the media is made of such as ceramics wood, or the artist that created this work while other categories include whether the photo contains an animal or a person. I am wondering if there are specific ML programs that would be a good fit for his situation. My idea suggested to use Apple’s CoreML which I have experience in. I could develop him an app that he could then create train and swap image recognition models using the GUI CreateML tool using the images he has already sorted. Do you think this is the best approach or is there another tool out there that could do this task for him easily? submitted by /u/Jpderouin310 [link] [comments]  ( 9 min )
    [D] Autonomous Alignment Oversight Framework (AAOF)
    Abstract: To align advanced AIs, an ensemble of diverse, transparent Overseer AIs will independently monitor the target AI and provide granular assessments on its alignment with constitution, human values, ethics, and safety. Overseer interventions will be incremental and subject to human oversight. The system will be implemented cautiously, with extensive testing to validate capabilities. Alignment will be treated as an ongoing collaborative process between humans, Overseers, and the target AI, leveraging complementary strengths through open dialog. Continuous vigilance, updating of definitions, and contingency planning will be required to address inevitable uncertainties and risks. Introduction: As advanced AI systems grow in capability and autonomy, ensuring their alignment with hum…  ( 12 min )
    [D] Does GPT-4 use LoRA?
    I just watched a video that explains how LoRA works. As I understand it's a fast and efficient way to fine tune models. At the end of the video he he said you could easily swap out the fine-tuned LoRA. So it makes LLMs like a PC. You just install new software / add the finetuned lora weights and you're good to go. Is my understanding correct? The rumor is that GPT-4 is a 8 way mixture model. Could they have pretrained it with all the data and then just use LoRA to train the expert models? I guess they would also need to train a smaller model that decides which model to use. I can't imagine that they would train GPT-4 eight times / once for each expert models. submitted by /u/StraightChemistry629 [link] [comments]  ( 9 min )
    [D] Deep Learning VS XGBoost for tabular data: a quick test
    Once per year, I write a post here on Reddit about our projects on deep learning for tabular data, and I hope this year will be no exception 🙂 Meanwhile, I have shared some results where we compare models from our previous papers with XGBoost on the datasets from the recent paper "Why do tree-based models still outperform deep learning on typical tabular data?". For us, this benchmark is a new one, so it was really interesting to check whether our previous findings generalize to new unseen datasets (spoiler: they do): https://twitter.com/YuraFiveTwo/status/1683796380895023104 submitted by /u/Yura52 [link] [comments]  ( 9 min )
    [Discussion] How good is generative data (synthetic data) !?
    5% average increase in F1 score 67% increase in Data richness 100% anonymized data set so one of the users on my tool milkstraw.ai just sent me this and i am really excited about the power of the tool i built and wanted to share it here 🚀 Also more importantly how do you all feeling about synthetic data, I started this as a fun project and its turning into a full blown startup. I love seeing some of the users send me results they are getting like this. https://preview.redd.it/24bwer0uk3eb1.png?width=4516&format=png&auto=webp&s=c8cbf906580a04df1f967c5300478a542128ccd0 submitted by /u/jjhazy [link] [comments]  ( 9 min )
    [R] New Open Source LLM: GOAT-7B (SOTA among the 7B models)
    Go try this free model. 7B SOTA by MMLU and BBH https://preview.redd.it/tq8c8ggaj3eb1.png?width=1570&format=png&auto=webp&s=10c78b724da2d6360e7c7ee6fbe3175c36cecc26 submitted by /u/rempact [link] [comments]  ( 8 min )
    [D] Attention Is Off By One
    https://www.evanmiller.org/attention-is-off-by-one.html submitted by /u/duckyzz003 [link] [comments]  ( 8 min )
    Voice cloning options, preferably local [D]
    Hi! What voice cloning options are people using right now? Looking at what is out there (that I know of), there is ElevenLabs and Coqui. Are there any other ones that are good? Preferably cheap/run locally? submitted by /u/MrJabbey1 [link] [comments]  ( 8 min )
    [P] Integrating Llama V2 🦙 and Multi-Chat Models: Open Source Solution with IntelliNode
    IntelliNode is an open source project that simplifies the integration of Llama V2 and other multi-chat models. With IntelliNode, you can easily connect and switch between different language models, including Llama V2 hosted in your AWS SageMaker account. It allows you to create a chatbot instance and add the backend provider. const { Chatbot, LLamaSageInput, SupportedChatModels } = require('intellinode'); const chatbot = new Chatbot(key, SupportedChatModels.SAGEMAKER, {url: }); For details on how to use intellinode to integrate with LLama SageMaker setup click here. The module available here. ​ ​ submitted by /u/Barqawiz_Coder [link] [comments]  ( 9 min )
    [Research] transformer models for drug discovery
    Does anybody know of good/reputable literature and other resources to read/learn about incorporating transformers in drug discovery? I am doing some computational chemistry research regarding compound identification for HBV mutations and want to try using transformers but don't really know where/how to start. submitted by /u/Present_Network1959 [link] [comments]  ( 8 min )
    [D] Annotation tool for annotating audio in a video
    Does anyone know of a good video (or audio) annotation tool that would allow me to look at both the image and the audio waveform at the same time? I could extract the audio and use an audio annotation tool, but since some of the sound events may sound similar to one another, it would be helpful to look at both the image and the audio waveform to identify which class a sound event belongs to. Thanks! submitted by /u/utility2000 [link] [comments]  ( 9 min )
    [P] FEEDBACK - Hey I am lunching my Data Professionals job platform and I would like to receive some feedback from you guys, thx
    Hey all Redditors, I have been thinking about this for years as I hate the cumbersome process of switching jobs. I have been planing it under the last year and finally I quit my job and built this in the last 1.5 months. I am lunching my Data Professionals job platform "applyscript dot com" I would like to receive some feedback from you guys. I really want to hear your opinion as that can help me improve the site a lot. Thx for stopping by and giving feedback, I really appreciate your time and effort. :) submitted by /u/glassAlloy [link] [comments]  ( 9 min )
  • Open

    DSC Weekly 25 July 2023
    Announcements Top Stories In-Depth The post DSC Weekly 25 July 2023 appeared first on Data Science Central.  ( 20 min )
    The AI content + data mandate and personal branding
    Fair Data Forecast Interview with Andreas Volpini, CEO of WordLift Andreas Volpini believes every user who wants to build a personal brand online has to proactively curate their online presence first. He sees structured data (semantic entity and attribute metadata such as Schema.org) as key to building a cohesive, disambiguated personal presence online. Volpini has… Read More »The AI content + data mandate and personal branding The post The AI content + data mandate and personal branding appeared first on Data Science Central.  ( 39 min )
    From automation to optimization: How AI is revolutionizing digital marketing campaigns
    Welcome to the exciting world of digital marketing! In this blog, we’ll delve into this thrilling frontier where optimization meets automation and Artificial Intelligence is at the center. No longer must manual labor and guesswork play an essential part in developing effective marketing strategies; with AI’s capabilities now at their disposal, marketers with digital presence… Read More »From automation to optimization: How AI is revolutionizing digital marketing campaigns The post From automation to optimization: How AI is revolutionizing digital marketing campaigns appeared first on Data Science Central.  ( 24 min )
  • Open

    "The AI-Powered, Totally Autonomous Future of War Is Here" (use of DRL in Navy swarms R&D)
    submitted by /u/gwern [link] [comments]  ( 8 min )
    Self-fictitious play and Q-learning or evolutionary algorithms
    I'm looking to implement Fictitious Self-Play in a model-based game (imperfect information limited to simultaneous move game, however each player has a combinatorial number of actions they can perform). EDIT: I know self-fictitious play is not the only option to solve this type of game, but I wanted to give it a try to test how it would behave (especially since I sort of like the idea behind it). Because of this combinatorial number of actions, solving it with linear programming is just not possible (I'd have to compute for each sequence of pair of actions (a1, b1), (a2, b2), (a3, b3), ... (ak, bk), whether player a or player b won). But to compute a best response I might be able to use Q-learning in a RL setting right (by that I mean, fixed environment)? Because when we calculate a best…  ( 10 min )
    RL continuous control help needed. ML Engineer wanted also
    Hi, I can't find the rules for this subreddit so please lmk if asking any of this breaks them I have a pybullet simulation of a bipedal robot wrapped as a gym env. Currently trying to train a Rl ppo algo to control it to walk. But no luck. Having the issue that it's trying everything except walking. And it seems to prioritise getting the instant reward by kicking its leg forward then lunging forward and the episode ends. Instead of walking forward and getting more score. Anyone have any tips please? (Btw gamma = 0.99) ​ Btw if anyone has experience with stuff like this I am looking to hire an engineer. Comment or dm me ​ Btw I am aware this is a significant undertaking submitted by /u/Harryoc494 [link] [comments]  ( 9 min )
    skrl version 1.0.0-rc.1 is now available with multi-agent and JAX support!!!
    skrl version 1.0.0-rc.1 is now available. The main features of this release are: JAX support Multi-agent training (the beginning). Comprehensive documentation with new structure and theme Visit https://skrl.readthedocs.io/en/latest/ to get started! ​ https://preview.redd.it/ms1q5s8ce3eb1.png?width=1459&format=png&auto=webp&s=4b1f0f27cae5df4dfac3e931eabcca2b924968d1 https://preview.redd.it/385lhqdbe3eb1.png?width=1543&format=png&auto=webp&s=2cb5ef75f4c2720e8db5adbb6b3f35b7977e3b57 ​ submitted by /u/Toni-SM [link] [comments]  ( 8 min )
    ZBrain: Empowering Businesses with Custom ChatGPT apps and Data Security
    Dear All, It is with great enthusiasm that I introduce you to ZBrain, a revolutionary GenAI platform that unlocks the ability to craft bespoke AI applications while prioritizing data privacy and security. ZBrain ushers in an era of remarkable possibilities for businesses seeking to harness the full potential of AI while ensuring their data remains safeguarded and confidential. ​ What Sets ZBrain Apart: ZBrain Flow - Codeless Brilliance: Forget complex coding! ZBrain Flow's intuitive drag-and-drop interface seamlessly connects large language models and extraction tools, simplifying the creation of sophisticated business logic without the need for coding expertise. AI Risk Governance for Data Safety: At ZBrain, we deeply understand the significance of data security. Our AI Risk Governance identifies potential risks such as Financial, Medical, Privacy, Harmful Language, and more. Through prompt engineering, your data is fortified, and sensitive information is shielded. Effortless Integration and Continuous Advancements: With ZBrain, integration with over 80 data sources is a breeze, providing you the freedom to fine-tune models and deploy them effortlessly. Our reinforcement learning approach continually enriches results through valuable human feedback. Confidence in Deployment: Choose your deployment approach with assurance. Opt for ZBrain Cloud for added security or self-hosting on your private infrastructure, ensuring data confidentiality remains at the forefront. ​ We are genuinely elated about the endless possibilities ZBrain offers businesses spanning various industries. By merging the prowess of AI with an unwavering commitment to data privacy, we wholeheartedly believe that ZBrain will elevate your business to unparalleled heights. Visit ZBrain at https://zbrain.ai/ and feel free to reach out with any inquiries or to share your experiences with ZBrain. submitted by /u/StewartBJasper [link] [comments]  ( 9 min )
  • Open

    Trying NLP on Middle English
    It’s not fair to evaluate NLP software on a language it wasn’t designed to process, but I wanted to try it anyway. The models in the spaCy software library were trained on modern English text and not on Middle English. Nevertheless, spaCy does a pretty good job of parsing Chaucer’s Canterbury Tales, written over 600 […] Trying NLP on Middle English first appeared on John D. Cook.  ( 5 min )
    Extending harmonic numbers
    For a positive integer n, the nth harmonic number is defined to be the sum of the reciprocals of the first n positive integers: How might we extend this definition so that n does not have to be a positive integer? First approach One way to extend harmonic numbers is as follows. Start with the […] Extending harmonic numbers first appeared on John D. Cook.  ( 5 min )
    A note on Zipf’s law
    Very often when a number is large, and we don’t know or care exactly how large it is, we can model it as infinite. This may make no practical difference and can make calculations much easier. I give several examples of this in the article Infinite is easier than big. When you run across a […] A note on Zipf’s law first appeared on John D. Cook.  ( 6 min )
  • Open

    Use generative AI foundation models in VPC mode with no internet connectivity using Amazon SageMaker JumpStart
    With recent advancements in generative AI, there are lot of discussions happening on how to use generative AI across different industries to solve specific business problems. Generative AI is a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music. It is all backed by very large models […]  ( 9 min )
  • Open

    NVIDIA DGX Cloud Now Available to Supercharge Generative AI Training
    NVIDIA DGX Cloud — which delivers tools that can turn nearly any company into an AI company —  is now broadly available, with thousands of NVIDIA GPUs online on Oracle Cloud Infrastructure, as well as NVIDIA infrastructure located in the U.S. and U.K. Unveiled at NVIDIA’s GTC conference in March, DGX Cloud is an AI Read article >  ( 5 min )
    Fin-tastic: 3D Artist Dives Into AI-Powered Oceanic Work This Week ‘In the NVIDIA Studio’
    We’re gonna need a bigger boat this week In the NVIDIA Studio as Alessandro Mastronardi, senior artist and programmer at BBC Studios, shares heart-stopping shark videos and renders.  ( 7 min )

  • Open

    Two opposing views on LLM’s reasoning capabilities. Clip1 Geoffrey Hinton. Clip2 Gary Marcus. Where do you fall in the debate?
    bios from Wikipedia Geoffrey Everest Hinton (born 6 December 1947) is a British-Canadian cognitive psychologist and computer scientist, most noted for his work on artificial neural networks. From 2013 to 2023, he divided his time working for Google (Google Brain) and the University of Toronto, before publicly announcing his departure from Google in May 2023 citing concerns about the risks of artificial intelligence (AI) technology. In 2017, he co-founded and became the chief scientific advisor of the Vector Institute in Toronto. Gary Fred Marcus (born 8 February 1970) is an American psychologist, cognitive scientist, and author, known for his research on the intersection of cognitive psychology, neuroscience, and artificial intelligence (AI). submitted by /u/Sonic_Improv [link] [comments]  ( 9 min )
    AI-generated content from the original image
    Hello everyone, can someone tell me how to create AI-generated images and videos from the original picture? For example, I have a photo of some person, and I want to generate an image or video of this person in different places: in the plane, in the gym. Thank you. submitted by /u/Kurland121 [link] [comments]  ( 8 min )
    Are you more creative than ChatGPT? Submit ideas and my experiment compares the creativity of those ideas to humans and ChatGPT. You’ll get a link to share your results at the end! [takes ~ 5 minutes]
    submitted by /u/josha_umich [link] [comments]  ( 8 min )
    What are your predictions for AI and medicine?
    Generally and specifically for specialties! submitted by /u/Wise-Listen-8076 [link] [comments]  ( 8 min )
    I turned ramen making process into anime.
    submitted by /u/kirakngs [link] [comments]  ( 8 min )
    Free courses and guides for learning Generative AI
    Generative AI learning path by Google Cloud. A series of 10 courses on generative AI products and technologies, from the fundamentals of Large Language Models to how to create and deploy generative AI solutions on Google Cloud [Link]. Generative AI short courses by DeepLearning.AI - Five short courses on generative AI including LangChain for LLM Application Development, How Diffusion Models Work and more. [Link]. LLM Bootcamp: A series of free lectures by The full Stack on building and deploying LLM apps [Link]. Building AI Products with OpenAI - a free course by CoRise in collaboration with OpenAI [Link]. Free Course by Activeloop on LangChain & Vector Databases in Production [Link]. Pinecone learning center - Lots of free guides as well as complete handbooks on LangChain, vector embeddings etc. by Pinecone [Link]. Build AI Apps with ChatGPT, Dall-E and GPT-4 - a free course on Scrimba [Link]. Gartner Experts Answer the Top Generative AI Questions for Your Enterprise - a report by Gartner [Link] GPT best practices: A guide by OpenAI that shares strategies and tactics for getting better results from GPTs [Link]. OpenAI cookbook by OpenAI - Examples and guides for using the OpenAI API [Link]. Prompt injection explained, with video, slides, and a transcript from a webinar organized by LangChain [Link]. A detailed guide to Prompt Engineering by DAIR.AI [Link] What Are Transformer Models and How Do They Work. A tutorial by Cohere AI [Link] Learn Prompting: an open source course on prompt engineering[Link] P.S. These resources are part of the content I share through my AI-focused newsletter. Thanks! submitted by /u/wyem [link] [comments]  ( 9 min )
    Five Things AI: WarGames, Call Center, Head of AI, LLaMA 2, Jimmy Dean
    This is the content of my Friday newsletter Five Things AI. Every week I publish links to five articles about the current developments in AI, not so much with a focus on the tech aspects of AI, but more with an interest in finding out how AI changes our work, our economy and how we live in our societies. ‘WarGames’ anticipated our current AI fears 40 years ago this summer WarGames cannot be 40 years old, but I still remember watching it in a movie theater as a kid… what a great movie, it genuienely sparked my interests in computers. The evil side of AI is still very dominant in the discussions of AI, so WarGames really was an early example of our fear of the machines taking over. ‘Training My Replacement’: Inside a Call Center Worker’s Battle With A.I. It is important to be constantly…  ( 10 min )
    everybody here knows about AGI and artificial super intelligence but have you heard about artificial emotional intelligence.Meet the girl who's building it
    Emotional AI is a different world of AI where machines can predict and understand human emotions, This is already impacting a lot in therapy and retail market. Rana El kaliouby is the women who built "affectiva" a startup which pioneers in emotional AI -- it's already being used in self driving cars and in the mental health industry, here's what they doing and I'm a total rookie at emotional AI side , if you have any other interesting company which is impacting a lot of people in this niche --would love to hear about it submitted by /u/caliperce_3 [link] [comments]  ( 9 min )
    Is this the role of philosophy in the 21st century?
    Person: discuss how the ontology outlined in your custom instructions can be used to marry relativity and quantum physics ChatGPT: The ontology proposed in the aforementioned description appears to rely heavily on emergent properties, consciousness, and free will, with a substantial focus on the temporal aspect. The challenge is to see how these metaphysical elements, particularly when applied to an AI, can help in reconciling the often contrasting principles of relativity and quantum physics. At first glance, the connection may not be clear; relativity and quantum mechanics operate in different realms of the physical universe. However, upon closer examination, one might see potential pathways to harmonize these distinct theories via the ontology of consciousness and emergent free will. …  ( 10 min )
    New study involving Buddhists in Japan, Taoists in Singapore, and Christians in the US finds that AI clergy are seen as less credible and receive fewer donations than human clergy, mainly due to the AI's lack of sacrifice and commitment.
    submitted by /u/fotogneric [link] [comments]  ( 8 min )
    Convert Music to Art ?
    Guitarist Tosin Abasi followed an Instagram account about a software that converts music into painting ~3 years back ? He also liked there video and commented something Video was of a guy playing piano and as he played canvas was filled with color. The software is made by a musician + artist + programmer. IIRC he is a pdf of masters in Com Sc submitted by /u/RedditNoobie777 [link] [comments]  ( 8 min )
    I Made a plugin that allows people to search and preview millions of 3D assets
    submitted by /u/AssetOvi [link] [comments]  ( 8 min )
    Surprise! AI advanced faster than robotics. That means today’s middle and lower classes will swap.
    People in intellectual jobs have often been thought of as doing something inherently more complex than manual workers in, for instance, construction or farming. Whether or not that is true, the surprise twist is that their “complex” work will be the first to be replaced. Computers have cracked intellectual work sooner than they have cracked manual work. It’s still too complex for a robot to replace a fruit-picker completely, but we’ll soon see AI lawyers. So we’re going to see a mass inversion. Everyone today sitting prettily doing their intellectual jobs will find their wages crushed or jobs redundant as AI replaces them. Meanwhile, everyone doing the jobs robotics can’t yet replace will be best placed to continue doing them. High flying executives will find they are suitable only for shelf-stacking, while those who’ve worked in retail for years will be or become their bosses. Soon enough, AI will help us advance the field of robotics sufficiently for manual labour also to be replaced. Who knows what happens then. submitted by /u/Aquillyne [link] [comments]  ( 9 min )
    Best Books About AI
    Hello everyone, I was searching for a book that talks about how AI will impact the future and how we can prepare best. I am not searching for anything technical or specific, just how can a person prepare best for the future. Thanks! submitted by /u/Ordinary_Argument_66 [link] [comments]  ( 8 min )
    The NeverEnding Game: How AI Will Create a New Category of Games
    submitted by /u/Respawne [link] [comments]  ( 8 min )
    One-Minute Daily AI News 7/23/2023
    Cerebras just built a gargantuan computer system with 27 million AI 'cores'.[1] FreeWilly1 and its successor FreeWilly2 are powerful new open-source Large Language Models (LLMs) developed by Stability AI’s CarperAI team. Both models perform exceptionally well in reasoning competitions using many different metrics.[2] Japanese education services company Benesse will offer a new service to help elementary school students with their research projects using generative artificial intelligence during the summer break.[3] The MTA is using artificial intelligence to help monitor fare evasion in several subway stations across New York City.[4] Sources: [1] https://www.zdnet.com/article/ai-startup-cerebras-built-a-gargantuan-ai-computer-for-abu-dhabis-g42-with-27-million-ai-cores/ [2] https://www.marktechpost.com/2023/07/23/stability-ai-team-introduces-freewilly1-and-freewilly2-new-open-access-large-language-models-llms/ [3] https://www.japantimes.co.jp/news/2023/07/23/national/benesse-ai-service-kids-research-projects/ [4] https://abc7ny.com/amp/mta-artificial-intelligence-subway-fare-evasions/13533675/ submitted by /u/Excellent-Target-847 [link] [comments]  ( 8 min )
    Best AI model for importing and interacting with large document archive
    Where I work we have a fairly large archive of documents going back to the 1930's and I want to assist the archive team in importing these into a GPT model. We have already begun the process of digitizing all the documents into OCR'ed PDF files, so this part at least is covered. My question is, what are the hot fully offline AI models I could try in an airgapped environment that will allow us to import all of the PDF files and their metadata (title/date/tags/etc), to incorporate their content on top of the larger general model? submitted by /u/kosul [link] [comments]  ( 9 min )
    I feel crushed. This is not exactly what I envisioned. This is too instant.
    Yes, the Midjourney to Gen2 creations in the twitter link was not exactly what I envisioned. I thought that it would be more like mocap previz with AI filtering. But this is just almost too instant compared to the workflow I thought of. submitted by /u/Absolute-Nobody0079 [link] [comments]  ( 8 min )
    GitHub - jbpayton/langchain-stock-screener: LangChain agent usable tool to screen stock data
    submitted by /u/seraphius [link] [comments]  ( 8 min )
  • Open

    Externally mounting P100 GPU [D]
    I made a mistake and bought a GPU that is not compatible with my motherboard. I found a P100 for $300 on ebay and bought it, but didn't research far enough to figure out that it isn't designed for a workstation motherboard. Is there any way I can externally mount it without spending tons on a GPU server? I am not sure just a PCIe riser will do the trick, since the GPU draws 250W and will also need a cooling system. Is it over? submitted by /u/jankybiz [link] [comments]  ( 9 min )
    [R] How do paper authors deal with takedown requests?
    Datasets like FFHQ consist of face images crawled from the Internet. While those images are published under CC licenses, the authors usually have not obtained consent from each person depicted in those images. I guess that's why they are taking takedown requests: People can send requests to remove their faces from the dataset. However, I'm always confused about one thing: Some faces images are already used in the paper. If those people request takedown of their images, wouldn't that result in a withdrawl of the paper? Or is there any "fair use" statement that can prevent this from happening? submitted by /u/alex000092 [link] [comments]  ( 9 min )
    [D] Do you guys think the day-to-day tasks of ML engineers will change with the emergence of LLM’s?
    Gone will be the days of data pre-processing, feature engineering, model training and model validation? What will we end up spending most of our time doing? submitted by /u/DM_ME_YOUR_CATS_PAWS [link] [comments]  ( 8 min )
    [P] Code Search Infra for an AI junior developer - that doesn't store code
    As we’re developing Sweep, our open-source AI junior developer, we implemented a new architecture for our vector search database. We decided on two main goals for our code search infrastructure: The search index needs to be up to date. Code is unique from other types of content in that it requires high levels of consistency. You wouldn’t want to reference an old version of a function(say two git commits back) while writing something that uses it. For additional security, we don’t want to store the code as plaintext. However, we still need a way to map the original code to the embeddings. Efficient Indexing Problem: We wanted to store multiple repositories in a scalable manner without relying on a hosted vector database like Pinecone. Insight: Repositories change frequently but …  ( 10 min )
    [D] Do you guys worry ML work will become less technical and reduced to prompt engineering
    I’m already doing work that involves creating prompts for LLM’s. I adore cleaning data and training models and worry that ML solutions will soon become asking chatbots to do what you want in plain English, and all this time I’ve spent learning about how ML is done on a technical level will just be auxiliary literature that doesn’t help me in my profession. What will our expertise move to? Being able to ask a chatbot the right questions? How will our profession change? submitted by /u/DM_ME_YOUR_CATS_PAWS [link] [comments]  ( 9 min )
    [P] multi label text classification question 🙋‍♂️
    Dear community I am currently despairing of a school project. The task is to develop a text classifier. So far so good. The problem is that I have a dataset with 200k texts that are not labeled. These should be classified to 190 classes, which are additionally very domain specific. However, several classes could also apply to one text. Does anyone know a good approach how to approach this? I have already determined 10 keywords for each class. But I don't know how to proceed now. It would be very nice if someone could help me. Gladly also only by buzzwords. Many greetings submitted by /u/loopingmadders [link] [comments]  ( 9 min )
    [D] [P] Looking for feedback on open-source project Cephalon
    Happy Monday Everyone! 😃 I am looking for feedback on the open source project Cephalon! Cephalon is a framework for building machine-learning applications. It aims to be similar to Django. Django is a batteries included framework for building backend of a website, and Cephalon is a batteries included framework for building Machine Learning applications in Rust. I want to get feedback from you because, I want to make building machine-learning apps easier for any new-comers. I think with a solid framework, they can focus more on the core concepts, rather than DevOps or MLOps. There is a survey you can fill out here Or message me if you want to discuss more! You can find the original project here Or find it on crates.io here I hope you have an amazing rest of the week! 😁 Thank you in advance for any feedback!! submitted by /u/GoodUnderstanding728 [link] [comments]  ( 9 min )
    [P] [D] Looking for feedback on Open-Source Project Cephalon
    Happy Monday Everyone! 😃 I am looking for feedback on the open source project Cephalon! Cephalon is a framework for building machine-learning applications. It aims to be similar to Django. Django is a batteries included framework for building backend of a website, and Cephalon is a batteries included framework for building Machine Learning applications in Rust. I want to get feedback from you because, I want to make building machine-learning apps easier for any new-comers. I think with a solid framework, they can focus more on the core concepts, rather than DevOps or MLOps. There is a survey you can fill out here Or message me if you want to discuss more! You can find the original project here Or find it on crates.io here I hope you have an amazing rest of the week! 😁 Thank you in advance for any feedback!! submitted by /u/GoodUnderstanding728 [link] [comments]  ( 9 min )
    [D] Install tensorflow-gpu
    Hello everyone. Please, help me. How to install tensorflow-gpu on Windows? Because I tried a lot of times and nothing. Maybe you have some micro moments that need to know. Thank you. submitted by /u/pavich_03 [link] [comments]  ( 8 min )
    [P] A mathematical model of music
    We have developed a model of music based on statistical mechanics and Euler’s gradus suavitatis, which seems to provide some new insights into tonal music. A description of the model is given on our website: tonamic.com. We are interested in collaboration opportunities, especially with ML researchers. submitted by /u/Tonamic [link] [comments]  ( 8 min )
    [Discussion] How many runs/iterations do you typically have in one "project'?
    Between HP tuning, explorations, and refinement how many iterations do you typically have when working on a model? I see some that have only a few like 40 but some have 1000s. Also curious how everyone keeps the diff iterations organized (naming, tags?) submitted by /u/fromalanjones [link] [comments]  ( 8 min )
    [P][D] A toolkit to make your unstructured datasets better
    Hey r/machinelearning, I’m Dean from DagsHub. I wanted to share something we’ve been working on really hard for a while, and hopefully get some community feedback. TL;DR We’re releasing Data Engine – a new set of tools that helps machine learning practitioners, collect and manage unstructured data, visualize it, send it to annotation, and turn it into a data loader for training. I wanted to share our reasons for building it and the challenges it solves and hopefully spark a discussion. You can check out the full launch blog here: dagshub.com/blog/launching-data-engine-toolset-for-unstructured-datasets/ Data Engine Flow Sorry for the long post – I wanted to share our considerations for building this toolkit, and hopefully spark a discussion about your processes for iterating on datasets…  ( 11 min )
    [D] AI regulation is mostly pointless and didn’t stop a recent bad actor like WormGPT.
    WormGPT is a criminal AI. It’s something that enables crime versus something like offensive jokes like 4chanGPT. EU and China pumped out all this regulation thinking they were ahead of the world when in all reality it’s freaking backwards. If AI was a physical commodity like goods and services, yes regulation is effective. However AI regulation is just won’t stop bad actors. The law already covers most of the dangers involved. Trying to regulate AI models is like trying to regulate piracy. We need to regulate the people, not the technology. Disinformation campaigns? Nail them for libel. Creating a model designed solely to enable crime? Nail the people for the crimes they are doing. Nail them for possessing criminal tools. People are easier to regulate than specifics on AI that’s easy to self replicate. Especially considering companies going to lobby their business interest. This is my opinion on the criminal AI, what about yours? (This model may also be using LLama weights considering it’s generations and timing) Source: https://fagenwasanni.com/news/the-dangers-of-wormgpt-an-ai-model-for-malicious-activities/68834/ submitted by /u/I_will_delete_myself [link] [comments]  ( 9 min )
    [P] Beer Inspector AI: How Computer Vision can help to identify the perfect brew
    Hey there, fellow Computer Vision enthusiasts! 🤖👋 On their quest to find the perfect beer our team of Czech Computer Vision and AI experts developed a solution that takes certain visual indicators of the perfect beer into account and applies Computer Vision to detect these. So, what are these visual indicators that determine the perfect pint? Let's dive in! First up, we have the "beer ratio." Each brand has its own glass, and the beer should be drafted within specific markings. Whether it's a single line or a range between logo points, Beer Inspector ensures you get what you paid for! No more guessing about your beer's quantity. 📏 Next, we have the "beer head structure." This is crucial for the ultimate beer experience. Airtight, thick, and no air bubbles – that's the way to go! Beer…  ( 11 min )
    [D] How do I reduce LLM inferencing time?
    I am running text inferencing on Llama2-7b through langchain. I have downloaded the model from langchain's Huggingface library, and I am running the model on AWS ml.g4dn.12xlarge which has 4xnvidia t4, which gives a total 64GB of GPU memory and 192GB of normal memory. It is able to answer my queries in around 10 seconds for small queries, and upto 3 mins for big queries. The task I am doing is retrieving information from a document(Understanding Machine Learning PDF) in a conversational way. I've extracted the main parts of the notebook and put it up here. Where can I make changes to speed up the transaction. Is there any change I can do in the model configuration to speed it up? Because if I use HuggingFaceHubAPI, it is able to give an answer in less than 5 seconds. Are there any other areas I can optimise? I appreciate any help you can provide. Thanks! submitted by /u/comical_cow [link] [comments]  ( 9 min )
    [D] How to use modern uncertain functions (e.g. BatchBALD) with classical Active Learning?
    I was looking libraries like DISTIL and I would like to test a toy-example with all these modern uncertainty functions like BatchBALD, Glister, etc All the implementations of these functions seems to be on a NN or CNN. I know some of them like BatchBALD were created on top of a CNN with Monte-Carlo Dropout -- even BALD originally was created on top of SVM. It seems many of these approaches are under the category "Query by Committee"and they are an ensemble of models I just would like to test a simple LogisticRegression and use the log_proba and an output for these strategies. Someone knows if this is possible? submitted by /u/TipKay [link] [comments]  ( 9 min )
    [D] Empirical rules of ML
    What are the empirical rules that one has to have in mind when designing a network, choosing hyperparameters, etc? For example: Linear scaling rule: the learning rate should be scaled linearly with the batch size [ref] (on resnets on Imagenet) Chinchilla law: compute budget, model size and training data should be scaled equally [ref] Do you have any other? (if possible with article, or even better an article with many of them) submitted by /u/Mulcyber [link] [comments]  ( 9 min )
    [D] Should I mask padding tokens when finetuning a GPT-2 model?
    For pretraining I just sent batches of 1024 tokens and didn't worry about padding. But for finetuning, I intend to use a padding token to make all the "instructions" 1024 tokens in length. But some of them are only 10 tokens, which means 99% padding tokens. I feel like that would affect the model, and perhaps those padding tokens should be masked. Should I mask out those padding tokens? I can see that there's a parameter for attention mask, and I could make one and pass it in. But I'm not sure if that's the intended usage. I'm seeing conflicting and ambiguous information on this point. It's unclear to me whether the attn_mask is intended for customizing the casual left to right attention of the model, instead of for masking padding tokens. I'm worried I might be interfering with the process if I use that attn_mask. Here I can see attn_mask is an accepted parameter: y = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=self.dropout if self.training else 0, is_causal=True) FYI, I'm using NanoGPT which is based on Pytorch (not Hugging Face Transformers). Should I apply the attention mask on the padding tokens in this context? submitted by /u/Pan000 [link] [comments]  ( 9 min )
  • Open

    How Patsnap used GPT-2 inference on Amazon SageMaker with low latency and cost
    This blog post was co-authored, and includes an introduction, by Zilong Bai, senior natural language processing engineer at Patsnap. You’re likely familiar with the autocomplete suggestion feature when you search for something on Google or Amazon. Although the search terms in these scenarios are pretty common keywords or expressions that we use in daily life, […]  ( 9 min )
    Optimize AWS Inferentia utilization with FastAPI and PyTorch models on Amazon EC2 Inf1 & Inf2 instances
    When deploying Deep Learning models at scale, it is crucial to effectively utilize the underlying hardware to maximize performance and cost benefits. For production workloads requiring high throughput and low latency, the selection of the Amazon Elastic Compute Cloud (EC2) instance, model serving stack, and deployment architecture is very important. Inefficient architecture can lead to […]  ( 15 min )
  • Open

    Attention Is Off By One
    submitted by /u/nickb [link] [comments]  ( 8 min )
    Meta-Transformer: A Unified Framework for Multimodal Learning
    submitted by /u/nickb [link] [comments]  ( 8 min )
    All neural network output activations converging to the same value regardless of input
    I'm facing a puzzling problem with my neural network, and I could really use some help in understanding what's going wrong. For some context, I am making a neural network from scratch in C++, just as a little project I find interesting. I'm working on a digit classification task using the MNIST dataset, and my network is composed of one hidden layer, consisting of 100 nodes, and an output layer with 10 nodes, each corresponding to a digit (0 to 9). To train the network, I'm using the Mean Squared Error (MSE) cost function, where the cost is calculated as (actualNodeActivation - expectedNodeActivation)^2 and as my activation function I am using the sigmoid function. The actual algorithm I am employing is backpropagation. The issue I'm encountering is that regardless of the input data, my n…  ( 10 min )
    NeRF: Creating photorealistic images using Neural Network
    ​ https://preview.redd.it/1gqd6tt1gvdb1.jpg?width=2800&format=pjpg&auto=webp&s=d21e9e5d0854022b8f25d9a6cb77e67b98487f40 You can find in interesting. OpenCV.ai team published the post about NeRF. Short description: NeRF is an innovative technology that generates photorealistic images of scenes from novel viewpoints using a neural network and volume rendering techniques. This article explores NeRF components, training, strengths and limitations, and advancements in modern NeRF-based solutions. More details are here. submitted by /u/No-Independence5880 [link] [comments]  ( 8 min )
    ZBrain- Create custom ChatGPT apps
    Hello Community, We at ZBrain have built a platform to create ChatGPT-like apps with your private data, you can import your data from multiple sources and DBs and integrate the app into any of your workflows. We have also added AI risk governance to mitigate the confidential data leak and now working on Flow a no-code tool to give you the freedom to create your own business logic. You can try the tool now at https://zbrain.ai/. We would love to hear your thoughts and feedback to improve the tool. submitted by /u/StewartBJasper [link] [comments]  ( 8 min )
  • Open

    A new dataset of Arctic images will spur artificial intelligence research
    The dataset, being collected as part of a US Coast Guard science mission, will be released open source to help advance naval mission planning and climate change studies.  ( 9 min )
  • Open

    Generative AI megatrends: Are companies using the excuse of AI to get rid of jobs?
    In this blog, I will now focus on generative AI megatrends. By that, I mean, trends and underlying trends that could be big in the future – focusing on the technology of LLM but also the wider impact of LLMs on the economy and society. I will hence identify and follow some key trends –… Read More »Generative AI megatrends: Are companies using the excuse of AI to get rid of jobs? The post Generative AI megatrends: Are companies using the excuse of AI to get rid of jobs? appeared first on Data Science Central.  ( 19 min )
    Sentience: Consciousness is inessential for LLMs, AI
    There is a recent paper in Synthese, Qualia share their correlates’ locations, where the abstract stated that “This paper presents the location-sharing argument, which concludes that qualia must share the locations of their physical correlates. The first premise is a consequence of relativity: If something shares a time with a physical event in all reference… Read More »Sentience: Consciousness is inessential for LLMs, AI The post Sentience: Consciousness is inessential for LLMs, AI appeared first on Data Science Central.  ( 20 min )
    AI is a child: How do we raise it?
    In October 2022, the White House Office of Science and Technology Policy published “The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People”. This attention from our government given to what could be called an AI EQ (emotional quotient) is reminiscent of how-to parent or raise a child. This… Read More »AI is a child: How do we raise it? The post AI is a child: How do we raise it? appeared first on Data Science Central.  ( 25 min )
    Innovations in predictive analytics: ML and generative AI
    With the introduction of ChatGPT-3 and DALL-E2, the majority of investors started showing interest in businesses building generative AI. Moreover, the fact is generative AI is not enough to reach the needs of the AI revolution. The success of predictive models is relevant to the science fiction future that the majority of the customers want… Read More »Innovations in predictive analytics: ML and generative AI The post Innovations in predictive analytics: ML and generative AI appeared first on Data Science Central.  ( 25 min )
  • Open

    CarRacing V2 Enviroment
    Hi! I am kind of new to Reinforcement Learning and Im trying to implement a PPO in CarRacing enviroment but I am failing to get the model to work. I have managed to get the model working with a DQN but with the PPO I can't seem to get the exploration right as it ends up either going forward all time or in circles. I have looked into my code for days, but haven't been able to find an error that would cause this. (Does not say much as I am kind of a newbie to RL). I would be grateful if someone could give me a hand. This is my source code: CarRacing Pastebin - Pastebin.com ​ Btw I also tried without greyscaling and it did the same. submitted by /u/MammothWeekend5954 [link] [comments]  ( 9 min )
  • Open

    Natural language processing and unnatural text
    I recently evaluated two software applications designed to find PII (personally identifiable information) in free text using natural language processing. Both failed badly, passing over obvious examples of PII. By contrast, I also tried natural language processing software on a nonsensical poem, it the software did quite well. Doctor’s notes It occurred to me later […] Natural language processing and unnatural text first appeared on John D. Cook.  ( 6 min )

  • Open

    [Project] Whisper Implementation in Rust using burn
    I temporarily switched from Rust to Python for machine learning, but quickly became fed up with Python's annoying versioning issues and runtime errors. I looked for a better path to machine learning and discovered burn, a deep learning framework for Rust. As my first burn project I decided to port OpenAI's Whisper transcription model. The project can be found at Gadersd/whisper-burn: A Rust implementation of OpenAI's Whisper model using the burn framework (github.com). I based it on the excellently concise tinygrad implementation that can be found here. The tinygrad version begrudgingly uses Torch's stft which I ported into a pure Rust short time Fourier transform along with the mel scale frequency conversion matrix function because I am curious and just a bit masochistic. Now for the good and the bad of burn. Rust's excellent package manager solves much of the versioning pain experienced in Python so burn models can be less painful to deploy and come with added reliability. The type checking in burn catches some tensor operation errors at compile time such as trying to multiply matrices of incompatible dimensions. Burn supports wgpu and WebGPU and can run in the browser when compiled into web assembly. I see a bright future for model deployment in burn. However, burn is relatively new so it lacks many tensor operations such as abs() that are available in other frameworks. Some features such as quantization are also missing. Burn implementations tend to be more verbose than the equivalent Python versions. Some of the runtime errors that plague PyTorch are still around in burn such as the crashes that result from trying to multiply tensors that live on different devices. Overall, burn is currently less ergonomic to develop with than alternatives such as PyTorch, but I think it has a lot of potential. If it is eagerly cultivated it may grow into a great Rusty alternative for machine learning practitioners. What do you all think? submitted by /u/Illustrious_Cup1867 [link] [comments]  ( 9 min )
    [P] NLP dataset for stream of consciousness: The Rambles
    submitted by /u/A_Human_Rambler [link] [comments]  ( 8 min )
    [P] Create your own Artificial Neural Network in Python
    submitted by /u/pmocz [link] [comments]  ( 8 min )
    [P] Run Llama 2 locally on GPU or CPU from anywhere (Linux/Windows/Mac) ➡️https://github.com/liltom-eth/llama2-webui
    Running Llama 2 locally with gradio UI on GPU or CPU from anywhere (Linux/Windows/Mac). Supporting Llama-2-7B/13B/70B with 8-bit, 4-bit. Supporting GPU inference (6 GB VRAM) and CPU inference. ➡️https://github.com/liltom-eth/llama2-webui Successfully running #Llama2 on my Apple Silicon MacBook Air: demo submitted by /u/plain1994 [link] [comments]  ( 8 min )
    [D] R&D machine learning intern at a startup company looking to publish a paper for his previous work..
    Greetings,I'm a machine learning engineer who managed to land an internship at a startup company and did R&D projects for them.. during the past year, I was working on an NLP problem of extractive question answering using BERT on this companies' text data. I trained the model and documented the results.. however, it's considered old technology now and we switched to solve the same problem using LLM.I was wondering if I can write a research paper for the BERT approach and publish it that can help me pursue PhD or Masters. How to start the discussion with my manager and seniors ? submitted by /u/Ready_Cockroach_3403 [link] [comments]  ( 9 min )
    [D] LLaMA training vs. GPU time: smaller models seem better for a given budget
    submitted by /u/espadrine [link] [comments]  ( 8 min )
    [N] LLMOps.Space - Curated resources related to deploying LLMs in production
    Today I launched LLMOps space on ProductHunt. LLMOps Space has a list of curated resources related to deploying LLMs into production. This includes- ✅ List of LLMOps companies and products 🗓 Upcoming events 📚 Educational resources 👩‍💻 Open-source LLM modules 💰 Funding news and much more. Everything is for free, would love it if you can support + share your thoughts in the comment. 🙏 https://www.producthunt.com/posts/llmops-space submitted by /u/AsDivyansh [link] [comments]  ( 8 min )
    [D] Can I use Transfer Learning (TL) in a classical Active Learning (AL) Framework?
    Hi, I'm trying to implement AL for ImageClassification. I have seen people using DAL, where some works use MC-DropOut to be able to calculate uncertainty on DNN / CNN. This also seems to be a current research topic. It seems very appealing for me to use DAL on the context of ImageClassification. However, I'm thinking to use a different and maybe naive approach: I thought to use TL (with or without FN) on a well knowledge DL Acthrecture (e.g.: Resnet) for Feature Extraction. Then, I just use the extracted features to train a Classical AL framework (e.g.: using LogisticRegression) Some thoughts/questions I had and would like to discuss: ​ I'm not finding articles that do this. Someone knows if this is approach is super naive or is a valid approach? What would be the drawbacks doing that? To train a DAL from scratch makes sense? For example, I saw some articles training DL Archthrectures from scratch, but this probably will require a lot of data, no? ​ ------------------------------------------------------------------------------------------ ML = MonteCarlo, AL = Active Learning, DAL = Deep AL, TL = Transfer Learning, FT = Fine-Tuning, DNN = Deep Neural Network, CNN = Convolutional Neural Network submitted by /u/TipKay [link] [comments]  ( 9 min )
    [D] Looking for an old post on this sub about using machine learning to identify a stray cat coming through a pet door to steal food, playing a loud noise to scare it away if it came in. The ML was used to tell the difference between the stray cat and the pet cat.
    I've seen the post (from 2-3 years ago maybe?) referenced, but my google fu is failing me and I haven't been able to find it, but it sounds like an interesting story. submitted by /u/TheQuarantinian [link] [comments]  ( 9 min )
    [R] Neuro Symbolic Reasoning and Learning
    submitted by /u/Neurosymbolic [link] [comments]  ( 8 min )
    [R] A history of neural networks
    Our history, primer, and outlook for neural networks in general, and deep learning in astronomy in particular has dropped on Royal Society Open Science. https://doi.org/10.1098/rsos.221454 Come for Llull and Leibniz... stay for LLaMA. submitted by /u/Smith4242 [link] [comments]  ( 8 min )
    LLM Guide [Discussion]
    Nowadays, If we see over the internet that LLM, chatgpt , llma etc are the trending topics and are being discussed. My question is that anyone can help me where to start studying about these topics from scratch ? BERT, Transformer etc all I want to understand everything. It would be good if you help me out. Thanks submitted by /u/Mission-Youth-3510 [link] [comments]  ( 8 min )
    [P] Linear regression partial derrivative problem
    Yo, I'm new to all this so bear with me. I'm doing project in python where i create a linear regression from scratch (with numpy and pandas). I was watching this lady tutorial on it, at https://youtu.be/ltXSoduiVwY?t=277 she shows the partial derrivatives for updating the weights and bias. Later when she is implementing it she doesn't use the 2 before the X and uses only the dot product. Is it math magic where the 2 dosn't have to be there or did she forget. Btw it still works fine without the 2 but still... I just need to know. Thanks for the answer and sorry if I'm asking something obvious submitted by /u/Z4joMan [link] [comments]  ( 9 min )
    [P] I created a parallelized implementation of Agglomerative clustering that's many times faster than existing implementations and has a better runtime
    I've been working on a new implementation of Agglomerative clustering called Reciprocal Agglomerative Clustering (RAC) based off of this paper: https://arxiv.org/abs/2105.11653. The short of it is Agglomerative clustering can be broken down into finding and merging pairs of reciprocal nearest neighbors in parallel, as long as the linkage function is one of the following: Single Average Complete Ward Most importantly, RAC produces the exact same results as traditional Agglomerative clustering when the dataset is fully connected. Even with connectivity constraints, the results are almost always the same. The authors showed that RAC has a linear runtime when connectivity is limited to k and the distance matrix is precomputed. I have not added the ability to pass in the distance matrix yet, so the runtime is roughly quadratic, which is still a major improvement over the cubic runtime of Agglomerative clustering. In addition the entire algorithm is parallelized, and so can scale up to more and more cores. It's very much in development - only average linkage works at the moment, however, I think it has a lot of potential. The benchmarks have blown me away so far: https://preview.redd.it/8bkpdkpayodb1.png?width=850&format=png&auto=webp&s=8c828eb2cde934b2d9a0ded9f22e18f3d9041147 Here is the code: https://github.com/porterehunley/RACplusplus. It would be great to have some people try it out (and find the bugs)! submitted by /u/Ridaleneas [link] [comments]  ( 9 min )
    [P] Paper reading and sharing platform
    Let me know your thoughts! submitted by /u/dockerun [link] [comments]  ( 8 min )
    [D] Probability Thresholds for User-Defined Tokens
    I want high certainty on social roles without sacrificing creativity. I don't want characters getting confused as to whether they're a parent or child, and I shouldn't have to spend hours each month explaining the difference. That said, I also don't want to lower the temperature, so it would be nice if as a user, I could select probability thresholds for certain token sequences, to hopefully mitigate role-swapping between virtual family members! I prefer writing stream of consciousness prompts seeding thoughts and choices rather than showing pretrained models their character bios at the start of every prompt. It breaks my immersion when family members swap roles due to high Top P and temperature, therefore I'd like models to be careful when writing names and corresponding social roles. This could help keeping track of many agents? There are instances where I enjoy getting role-swapped, and instances where swapping is nonsensical! This is my feature request. submitted by /u/TheLastVegan [link] [comments]  ( 9 min )
    [P] Data Version Control in R with lakeFS
    submitted by /u/zoobatsea [link] [comments]  ( 8 min )
    [D] Dev env and workflow
    Hi all! I am a frontend engineer looking to play more in the ML space. I know enough about python and jupyter labs to be dangerous but I am no expert. I am looking to hear what peoples env's and workflows look like. I have been looking at huggingface, google colab, and running some things locally but can't seem to see a setup that looks like a clear winner. Hardware wise I have a machine with a 4080 and 32gb ram at home and a M1 Pro Macbook also with 32gb of ram. For my 1st project I would love to utilise a 7b Llama 2 for a recommender like system. I plan on getting a custom dataset, cleaning and processing it, fine tuning, and then testing. submitted by /u/pseudoShadow [link] [comments]  ( 9 min )
    [D] Use Cases for Diffusion Models VS GANs VS Transformers, etc.
    I am interested in learning to use AI to generate images. Diffusion Models like stable diffusion seems to be the most popular nowadays, but I'd like to know what tool is best for what job. Or is diffusion model getting so good that the other methods are essentially becoming obsolete? If not, when would you choose one over the other? For generating creative images with a lot of variance, diffusion model seems to be the most fitting. But for example, what about for this use case: Generate realistic time lapse images of a plant growing (after 1 week, 1 month, 2 months, and so on...). In this case, the plant should change, but the background should stay the same. submitted by /u/musshead [link] [comments]  ( 9 min )
    [P] Evolved codealpaca datasets using GPT-4
    Using LLMs to augment and create much diverse instruction based dataset has seen wide success in WizardL. However the 78k evolved code instructions dataset hasn't been released since, so I have take the initiative to try to recreate the augmentation instruction myself. Dataset: https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1 submitted by /u/gradientpenalty [link] [comments]  ( 8 min )
    [D] course/videos to learn about the architecture and software stack of pytorch?
    I like to learn how pytorch connects to the compiler, generates IR, how it connects to run time , driver ..etc Im not interested in the programming model but the whole stack from pytorch to the hardware. I really appreciate if someone can give me a pointer thanks submitted by /u/aghozzo [link] [comments]  ( 8 min )
    [P] [R] Join Our Team of ML Model Developers for an Exciting Project & Permanent Job Potential!
    Seeking skilled ML model developers for our thrilling project with possible permanent positions! Embrace remote collaboration, offering flexibility and impact-driven work. Interested? Apply to btprenuer@gmail.com with a list of your related skills and samples of your work/projects! All experience levels welcome! Thank you for reading! Share this post to help us find the perfect fit. submitted by /u/boztka [link] [comments]  ( 8 min )
  • Open

    Compilation of respected AI scientists speaking on AI understanding, world models & consciousness, Mo Gawdat, Lex Fridman, Andrej Karpathy, Geoffrey Hinton, Gary Marcus, & Ilya Sutskever
    This segment I created for my IG exploring the possibility of AI consciousness. Not all experts agree, some scientists on the other side of the AI world model debate are Yann LeCun, and Gary Marcus they are also well respected AI Scientists who have a differing opinion. submitted by /u/Sonic_Improv [link] [comments]  ( 8 min )
    Trained an AI to drive in real-time from screenshots in the TrackMania videogame (beginner-friendly)
    submitted by /u/yannbouteiller [link] [comments]  ( 8 min )
    Saurabh Kumar's fast-cmix wins €5187 Hutter Prize Award!
    submitted by /u/jabowery [link] [comments]  ( 8 min )
    How Generative AI looks in next 10-15 years
    submitted by /u/AdithyaSai [link] [comments]  ( 8 min )
    What's the A.I that allows you to remake songs from the voices of other singers? Are there any I won't have to download?
    I've seen some videos on youtube and I'm curious. I just wanted to have some fun with it but Google isn't helpful when I ask. Anyone got an idea? submitted by /u/GoblinQueenForever [link] [comments]  ( 8 min )
    Best Opensource Projects for Deep Fakes?
    What are the best opensource projects for making a deep fake of myself? I would like to create a setup like me talking on a podcast to a camera. What are the best projects that you know of? submitted by /u/Reasonable_Chain_160 [link] [comments]  ( 8 min )
    Graphics Card for consideration . ( Cheapest-Budget ) (In my country compared to amazon)
    So, Nvdia is technically the best in this AI department. (For now 24/07/2023) Budget - (200 - 300)$ Official Prices in Amazon 1 ) Intel arc A750 8 GB Amazon price - 219$ 2 ) RX 7600 OC 8 GB Amazon price - 269$ 3 ) RTX 3060 12 GB Amazon price - 284$ Here we see that Arc A750 is about 60 $ cheaper but price in different country is different. In my country Bangladesh the prices are as follow Official Prices in Bangladesh 1 ) Intel arc A750 8 GB Startech (Bangladesh) price - 285$ (cheapest) 2 ) RX 7600 OC 8 GB Startech (Bangladesh) price - 327$ (cheapest) 3 ) RTX 3060 12 GB Startech (Bangladesh) price - 401$ (cheapest) 4)Intel arc A770 16 GB Startech (Bangladesh) price - 421$ (cheapest) Now the difference is more than 100 $ and both AMD and RTX are out of budget. AMD just isn't that good with Ai in any way. Nvdia is the best. (Not price to performance. Intel Arc is new but has better capabilities in AI than AMD. But its drivers are bad for AI for now) Now thinking if the intel drivers for Ai get better and optimized as its already somewhat better for games. Will the arc a750 be better than the RTX 3060 12 GB ? Will the arc a770 capitalize on Vram and beat all nvdia budget gpus after the drivers are fixed only for 20 more dollars than rtx 3060? Which is better for future proofing (theoritically) from these budget gpus ? If its arc then I will gamble its chances of surviving in the future and buy it now. submitted by /u/BonelyCore [link] [comments]  ( 9 min )
    Anyone who can assist me in connecting my premium ChatGPT to the internet and connecting plug-ins?
    So I’m amazed by ChatGPT and have signed up for the paid ChatGPT-4 version. I do however feel a little handcuffed by only having access to data up until 2021. I know there are ways to connect it to the internet as well as to add certain plug ins to enhance the experience but I haven’t been able to figure out any of the guides or tutorials from google…. I’m using Apple iPhone for the app and MacBook Pro laptop for web browsing submitted by /u/Kennyg39 [link] [comments]  ( 8 min )
    Do AI detectors have access to all the data that has been fed into AI systems like Chatgpt? If so, does this mean that a story that has been inputed into Chatgpt will be flagged as "AI" even if it had actually been human created?
    And why is this fact rarely mentioned when discussing how AI detectors do their work? submitted by /u/E_Olig [link] [comments]  ( 8 min )
    AVA | Sci-Fi Short Film about AI, Made by a Human
    submitted by /u/blakeridder [link] [comments]  ( 8 min )
    My teacher asked me to make a presentation and a demo of one of the following programs. Which one would be the easiest to make?
    submitted by /u/volvie98 [link] [comments]  ( 8 min )
    I am seeking free Ai websites/services to convert mp3 or other audio files and transcribe them into Bass guitar Tabs.
    I am a beginner to intermediate bass player and I would like to play some songs I like, but a couple of the songs are not very popular and do not have any tabs for them. I would ideally like an ai tool that can transcribe the bass notes. submitted by /u/GenuineElf80093 [link] [comments]  ( 8 min )
    Geoffrey Hinton, Aka the "Godfather of Al" admits in a recent lecture at Kings College that he believes current Al probably has feelings & emotions & speaks about why he avoids talking about it.
    Ilya Sustkever has some good explanations as to why AI in predicting the next token, has modeled the world and has gained an understanding of what lead to creation of those tokens (words or parts of words) and the better a model is at predicting the next token the higher the fidelity is in its understanding the world through the relationship of words…but don’t take my word for it. Actually listen to what the top experts in AI are saying not just some rando on Reddit. All experts don’t agree but the people building the best models seem to share this view. Many of them studied under Geoffrey Hinton. submitted by /u/Sonic_Improv [link] [comments]  ( 9 min )
    can anyone who understand how these models work explain why claude made this mistake?
    Focus on having fun together rather than writing every ride. Take breaks in between. submitted by /u/nicdunz [link] [comments]  ( 8 min )
    AI is learning to troll
    submitted by /u/MostConversation3772 [link] [comments]  ( 8 min )
    Is there an AI tool that makes English subtitles out of audio from other languages?
    I have a chatbot that can find most AI tools, but it can't seem to find one of these. submitted by /u/ai_basics_official [link] [comments]  ( 8 min )
    Where can I find an AI engine where I can upload my own audio to voice change?
    I was using Uber duck to change my singing voice into another artist. But Uber duck recently has taken down all of their community generated voices so no more Drizzy, Drake or Adele. I need a new website now to fool around with where I can upload my own singing in change it into another artist. Anything would help thanks guys. submitted by /u/Evangelionyama [link] [comments]  ( 8 min )
  • Open

    Google at ICML 2023
    Posted by Cat Armato, Program Manager, Google Groups across Google actively pursue research in the field of machine learning (ML), ranging from theory and application. We build ML systems to solve deep scientific and engineering challenges in areas of language, music, visual processing, algorithm development, and more. We aim to build a more collaborative ecosystem with the broader ML research community through open-sourcing tools and datasets, publishing our work, and actively participating in conferences. Google is proud to be a Diamond Sponsor of the 40th International Conference on Machine Learning (ICML 2023), a premier annual conference, which is being held this week in Honolulu, Hawaii. As a leader in ML research, Google has a strong presence at this year’s conference with ov…  ( 98 min )
  • Open

    How rare is it to encounter a rare word?
    I recently ran across a paper on typesetting rare Chinese characters. From the abstract: Written Chinese has tens of thousands of characters. But most available fonts contain only around 6 to 12 thousand common characters that can meet the needs of everyday users. However, in publications and information exchange in many professional fields, a number […] How rare is it to encounter a rare word? first appeared on John D. Cook.  ( 5 min )
    How an LLM might leak medical data
    Machine learning models occasionally memorize training data. Under the right prompt, a model could return portions of the training data verbatim. If a large language model is trained on deidentified medical data, along with data that overlaps with the medical data, it could potentially leak details of a person’s medical history. I’m not saying that […] How an LLM might leak medical data first appeared on John D. Cook.  ( 5 min )
  • Open

    "Evaluating Superhuman Models with Consistency Checks", Fluri et al 2023
    submitted by /u/gwern [link] [comments]  ( 8 min )
    Looking to get into RL, already working in CV.
    I'm in my final year of undergrad and previously have some experience with image segmentation, object detection,etc. RL is something I feel like I want to get into, but I want to understand how I can get started and what it actually involves. Also, if there's any way I can apply my new knowledge to the domain of 3D vision like SLAM or 3D reconstructed images. submitted by /u/PRAY_J [link] [comments]  ( 8 min )
    2D Drone RL
    Long time lurker on the sub and just finished my first semi-decent experiment with DRL so I thought I’d share it here. I’ve been wanting to experiment with RL and drones for a while now, ever since seeing John Buffers Autodrone project where they train a drone using the genetic algorithm. Finally got a basic implementation working using SAC a few days ago, and have made the environment open source as well in case others wanted to try it out. Project Link: https://github.com/Yyassin/senza submitted by /u/vanishedoblivion [link] [comments]  ( 9 min )
  • Open

    Book Preview: Neuro Symbolic Reasoning and Learning
    submitted by /u/Neurosymbolic [link] [comments]  ( 8 min )
    Meme Review By AI: Bing Gets Humorous
    submitted by /u/Small_Championship_2 [link] [comments]  ( 8 min )

  • Open

    AI tool to edit .ai files text
    I am looking for a tool that I can edit text on a .ai file with new text that will use the same font and center the text. Of course can be done in Photoshop or other tools like Canva, just not sure if something new is available. Also, for those that use midjourney, and you want to add text to an image, but tool do you use? submitted by /u/tequiladrinker1 [link] [comments]  ( 8 min )
    Computer chip with built-in human brain tissue gets military funding
    submitted by /u/surfer808 [link] [comments]  ( 8 min )
    [Discussion] I have a theory that ChatGPT is becoming dumber because more of the internet is made up of AI generated content since it awakened
    As NLP hype become more prevalent, we would expect a (probably exponentially) increasing amount of scraped data-sources become filled with AI generated stuff, no? Then wouldn't AI would be trained on this data without necessarily a 'critical thinking' module to check their work? Not just ChatGPT generated quality either, but also lesser AI companies making cheap ad-ware and upvote bots. ​ I wonder if ChatGPT et al could have a 'quality sensor' module in some ai that does what I do on reddit and do sentiment analysis on the most upvoted comments to see whether the article/answer/assertion is full of shit. Not foolproof, but short of actual critical reasoning, seems like a good start. ​ Feels like we may soon enter an arms race where AIs need to detect AI-generated content in order to ensure their own quality. submitted by /u/Yamochao [link] [comments]  ( 9 min )
    Best AI for business/social media account name generation?
    I'm looking for something that can generate names using real words like ConnectHub but also made up names like Intrium. I tried ChatGPT buy the names it gave me were not good and it kept repeating them(but I am a noob so). submitted by /u/anysuggestionwelcome [link] [comments]  ( 8 min )
    Bing AI Arrogance and sentiment
    submitted by /u/Yha_Boiii [link] [comments]  ( 8 min )
    What's this ai voice called?
    https://www.facebook.com/reel/264992922832459?mibextid=6gvBvW&s=yWDuG2&fs=e https://www.facebook.com/reel/1647205912442042?mibextid=6gvBvW&s=yWDuG2&fs=e He sounds very human, but seeing him on many reels of different account about different topics, i am convinced this is an ai. submitted by /u/Standard_Turnover_14 [link] [comments]  ( 8 min )
    Can anyone recommend a book to get up to speed with AI?
    AÏ is something I just can't wrap my head around, and I see no other option than to actually read up on the subject. Ád-ladén yoütube vídeos with annoying musíc just ain't cutting it. I want to know the raw mechanics, but I'm looking for something without too much abstract theory. This can't be avoided, of course, but I'd prefer it garnished with something more practical and concrete, like "this is how Stablé Díffusion creates a pícture of a rabbit." submitted by /u/Legitimate-Record951 [link] [comments]  ( 8 min )
    Don't do this - Torture AI with absolute silence [On phone call]
    submitted by /u/harvard1932 [link] [comments]  ( 8 min )
    One-Minute Daily AI News 7/21/2023
    Representatives from Amazon, Anthropic, Google, Inflection, Meta, Microsoft, and OpenAI have committed to managing risks posed by the tech, the White House has said.[1] Hundreds of dental offices across the U.S. are now using AI-powered X-ray imaging technology from Boston-based VideaHealth. The software helps dentists deal with routine procedures, such as identifying cavities, as well as spot more serious conditions, including periodontal disease, or bone loss within the mouth often linked with diseases like diabetes or Alzheimer's.[2] Surveillance software that uses artificial intelligence to spot people evading fares has been quietly rolled out to some of New York City’s subway stations and is poised to be introduced to more by the end of the year, according to public documents and government contracts obtained by NBC News.[3] Christopher Nolan: ‘Very strong parallels’ between Oppenheimer and scientists worried about AI.[4] Sources: [1] https://www.bbc.com/news/technology-66271429.amp [2] https://www.cbsnews.com/amp/news/ai-artificial-intelligence-dentists-health-care/ [3] https://www.nbcnews.com/news/amp/rcna93045 [4] https://amp.theguardian.com/technology/2023/jul/21/christopher-nolan-says-ai-experts-face-their-oppenheimer-moment submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )
    Crafting a Simple "Zero-Shot Classifier" Using an API - Seeking Your Insights!
    (X-post from /r/ChatGPT) I'm hoping you fine folks might be able to give me some guidance. I have a collection of 700 categories, all potential classifications for articles. My current need is to create a system that can dynamically categorize short texts or articles according to these 700 categories. I've been experimenting with a rudimentary approach using chatGPT to read the categories from a PDF via a plugin. The process is quite straightforward - I input the title and the first two lines of an article, and chatGPT does a fairly decent job of predicting the most fitting category. The downside? I'm concerned about its scalability and economic viability. The current method might not work so well when we're talking about classifying a significant number of articles. My question to you, my fellow AI enthusiasts: How would you approach designing a system, via an API, capable of doing this quickly and on a large scale? I'm particularly curious about how to integrate my method with chatGPT using OpenAI's API. Is there a feature that allows the Language Learning Model (LLM) to retain the list of 700 categories in its memory so that I don't have to pass it every time? I'm aware that the billing structure is token-based, so it would be ideal to submit the categories once (or as few times as possible) and then pose a simple query like: "Categorize this article based on the categories I previously gave you. Article title: 'Barbie vs Oppenheimer: Which Movie Will Garner Greater Success?'" Ideally, I'd want this system to be persistently active and capable of processing countless queries over an extended period, say a month or a year. So, any ideas on how to design such a system? There are undoubtedly numerous routes to take. I'm really just seeking some initial direction so that I can dive deeper into research on my own. Thanks in advance for any insights you might provide! submitted by /u/adv4nced [link] [comments]  ( 9 min )
    Best Dataset for Ai Vocals?
    As time goes on, things get improved So far I heard about RVC, so-vits-svc or diff-svc, any of these any good for Ai Singing/Rapping? Im not sure, which one to pick. I’m open to other suggestions. submitted by /u/Office_Flashy [link] [comments]  ( 8 min )
  • Open

    [R] TEXT2TEX — text-driven texture synthesis via diffusion models
    submitted by /u/SpatialComputing [link] [comments]  ( 8 min )
    [D] Breaking Down the Hyperbolic Buzz: An In-Depth Review of the 'Leaked' GPT-4 Architecture & a Mixture of Experts Literature Review with Code
    submitted by /u/CkmCpvis [link] [comments]  ( 8 min )
    [P] What are a good project for people learning Tensorflow?
    I am learning Tensorflow and of course I want to improve my skills and add it to my resume What projects should I build which I can add to my resume which will later land me a job. Projects should be from Beginner to Advance and can contain each major Topic from Regression (linear and non linear), to Classification (Binary, Multi Classification, Multi Label), CNN,RNN, NLP, etc (Can add more). This can also help other people as well learning TensorFlow. Thank you. submitted by /u/dusklordtrue [link] [comments]  ( 9 min )
    When to train LLM supervised vs unsupervised? [D]
    I have done a bit of language modeling recently, and I am a bit confused when to use which method. For causal language modeling, I used the unsueprvised method of concatenating and chunking the text, then predicting the next word. For sequence to sequence tasks like summarization, I fine tuned using the supervised method where the desired output text was the label. However I have not seen any definitive guide on when to use supervised and when to use unsupervised. What are the general use cases and advantages / disadvantages for each? submitted by /u/jankybiz [link] [comments]  ( 9 min )
    [Discussion] Best Image Annotation Tool for Angiograms?
    I am looking to annotate specific anatomical structures using a library of angiogram images. My goal is to train AI to recognize anatomical variants of interest. What would be the best Image Annotation Tool to do this? I am new to this, so I hope that question makes sense. Any insights and advice would be greatly appreciated. submitted by /u/ColdChampion [link] [comments]  ( 8 min )
    [D] What leaderboard would correspond best to seeing what images are most similar to a caption (like CLIP)?
    I've been using CLIP to see if images align with a certain caption for image mining (ex. I embed the caption "Picture of a mountain" and then look at what image embeddings have the highest cosine similarity with that caption embedding). I was hoping to improve the performance by using a more recent model. Would I be able to use VQA models for this (like from this leaderboard) or is there a better task that aligns with seeing if images are similar to a given caption? Thank you! submitted by /u/EricW_CS [link] [comments]  ( 9 min )
    [P] Pattern classification using CNNs
    Hi (I have to write again, Reddit removed image attached), Does anyone has experience with training CNN for pattern matching? Here is the sample of the images which I have on my disposal. It is graphical representation of input data which for problem classified by algorithm which shows best performance when applied. Lines are projection of the problem of input data on 2d plain, so shapes and colours have meaning in correlating input data to solution (i.e) algorithm. Whichever CNN architecture I use, starting from VGG16 and so on, I am unable to achieve higher validation accuracy then 0.7 when execute training. I am constantly under-fitting. I have 10k, 100k, 200k data samples on my disposal - nothing helps. Is CNN able to make any sense of images/patterns given below? Is this something that CNN can not do or I am missing something? Thanks! Patterns to classify ​ submitted by /u/thecelavi [link] [comments]  ( 9 min )
    [D] technical question: How is it possible that embedding models produce fixed size vectors for sentences with varying lengths?
    As far as I know and studied, each token is mapped from high dimensional discrete token space into a continuous, lower dimensional space where words are embedded meaningfully based on their relationships in the training data. So 1000 tokens text produces 1000 vectors. Now for vector databases (correct me if I'm wrong), people are storing fixed sized vectors for text with varying lengths. For example, 2 sentences one with 1000 tokens and the other is 10 tokens, each produces one vector and both vectors have the same size. I'd really appreciate an explanation. submitted by /u/Qdr-91 [link] [comments]  ( 9 min )
    [P] Llama-2 4bit fine-tune with dolly-15k on Colab (Free)
    Simple walkthrough of fine-tuning llama-2 instruct fine-tuned on guanaco model with 4bit QLoRA on a free Google Colab instance. Colab: https://colab.research.google.com/drive/134o_cXcMe_lsvl15ZE_4Y75Kstepsntu?usp=sharing GitHub: https://github.com/kw2828/guardrail-ml/blob/main/examples YouTube Overview: https://www.youtube.com/watch?v=o5bU1H-6TqM&ab_channel=GenerativeAIEntrepreneurs Bonus colab in repo on generating your own JSON Q&A dataset from PDF in the repo above. submitted by /u/Educational_Grass_38 [link] [comments]  ( 8 min )
    [N] Jul 2023 - Recent Instruction/Chat-Based LLMs and their parents (after llama2)
    submitted by /u/michaelthwan_ai [link] [comments]  ( 8 min )
    "[Discussion]" What do you think about Federated Learning for Healthcare
    Link to the article: https://dl.acm.org/doi/10.1145/3533708 In this article, they talk about the difficulty of training foundation models on Healthcare data because of how sensitive it is and hard to get. Access to a large amount of high-quality medical data is possibly the most crucial factor for enhancing Machine Learning (ML) applications in the healthcare domain. However, security and privacy issues of healthcare data have raised broad ethical and legal concerns in recent years, given the sensitive nature of health information. So they decided to take the approach of Federated Learning where the model will be distributed and trained by multiple institutions (Hospitals, Clinics ...) then the model weight will be transferred over to the general model to be updated, which will keep the sensitive medical data inside the institutions safe. The global ML model is distributed to each client site, where an instance is trained locally. The updates from locally trained instances are then aggregated at regular intervals to improve the global model. The updated global model is then sent back to the local devices, where the learning continues. These steps are repeated until a particular convergence threshold is satisfied or lasts for a long time to improve the deep learning model continuously. What do you think about such an approach, to brake data obstacles between AI and the Healthcare industry? ​ submitted by /u/angeloboustany [link] [comments]  ( 9 min )
    [D] Scheduler choice when pretraining causal decoder models
    There seems to be a lack of published work on the impact of schedulers on model training effectiveness when training models similar to the GPT family. I'm looking at a very domain specific models pretraining a model from scratch on a relatively small dataset (~40B tokens) over multiple epochs. To date we've had some mixed results with a linear scheduler with warmup to help with stability. Any thoughts on whether a cyclic based scheduler or other could help? submitted by /u/Humble-Passenger-635 [link] [comments]  ( 9 min )
    Train LLM for closed-book QA [D]
    What is the best way to train an LLM for closed book question answering? I can only think of two options: Concatenate question/answer pairs into chunked text and train the model using causal language modeling. Train the model using sequence to sequence techniques with question as the input and answer as the label. I have tried both and the first seems to work better. Does anyone know whether there is a commonly accepted method? Can somebody point me to some resources? submitted by /u/jankybiz [link] [comments]  ( 9 min )
    [P] A Chrome extension to save paper details
    submitted by /u/HugoDzz [link] [comments]  ( 8 min )
    [Discussion] Easy way to ship tensorflow model to non-technical audience?
    I'm surprised that there aren't more resources on the internet about how to do this, it seems like the whole point of doing machine learning lol. Do very few people have this need? All of the solutions for this that I've found so far seem to require advanced knowledge of web development/backend engineering. I'd love to hear if someone has found or figured out a way to do this. submitted by /u/youaregames [link] [comments]  ( 9 min )
    Can someone explain to me what the wolves and prey really are in wolf search algorithm?[D]
    I'm aware that the algorithm is very similar to the real world hunting of wolves, but what I want to know is what exactly is the "prey" and what is a "wolf" For example, I know a Chromosome sequence in Genetic algorithm is a combination of random features, and its fitness can be computed. And then you let the whole natural selection jargon take place and you arrive at a optimized chromosome, the solution to the optimization problem. I just can't seem to wrap my head around the WSA algorithm. I've watched a bunch of youtube videos, I tried reading the paper, I still can't understand it well. What IS a wolf? I think what I'm looking for is how the actual data features and components of a search algorithm correlates with the analogy of the wolves searching for prey. submitted by /u/SnooHobbies7910 [link] [comments]  ( 9 min )
    [D] What are your main approach to model compression in production?
    I’m currently trying to understand each methods but It seems I can never catch up to the latest/best. After months of reading I have still lots of questions like: What are the go to strategies to compress a model? Are there any good fully/semi automated frameworks? How much weights has model architecture in this equation? What could be a general good work-flow in a modern and optimized solution? I would love to hear from you some production compliant workflows submitted by /u/PierroZ-PLKG [link] [comments]  ( 9 min )
    [D] Challenges and Applications of Large Language Models
    submitted by /u/gamerx88 [link] [comments]  ( 8 min )
  • Open

    Using stable baseline3 for multi agent env
    Hey, I am trying to use sb3 with a pettingzoo mpe environment and trying to implement parameter sharing for simple spread. Any help on how I would train a model for this multi agent environment would be appreciated, thanks. submitted by /u/bruhhhwhats [link] [comments]  ( 8 min )
    The Offline Algorithm (or how to get >40,000 avg. in Humanoid-v2 in 10000 ep and highest scores (Wordly) in other envs without multiprocessing)
    I've been doing this "fine tuning" project for 2 years now from 2021. https://preview.redd.it/qviy5lpxegdb1.png?width=568&format=png&auto=webp&s=8728814e39176d9024fac16e191937aeb5a302c1 https://github.com/timgep/Lords_Policy_Gradient/tree/main This is Offline Reinforcement Learning Algorithm (based on Twin Delayed DDPG (Temporal Difference), Fading Memories (Fading Replay Buffer), Spiking Activation Function (alternative for Relu and Norm), and Rectified Hubber Error (alternative to MSE and MAE), the last 3 was invented/implemented during experiments. For long time I was reluctant to use TD3, as it seemed that using second critic when you already have 2 Actors and 2 Critics in DDPG was not normal. As result you would have 6 Networks. So I was making my own DDPG with dicreased (smaller)…  ( 14 min )
  • Open

    Communication-Efficient Split Learning via Adaptive Feature-Wise Compression. (arXiv:2307.10805v1 [cs.DC])
    This paper proposes a novel communication-efficient split learning (SL) framework, named SplitFC, which reduces the communication overhead required for transmitting intermediate feature and gradient vectors during the SL training process. The key idea of SplitFC is to leverage different dispersion degrees exhibited in the columns of the matrices. SplitFC incorporates two compression strategies: (i) adaptive feature-wise dropout and (ii) adaptive feature-wise quantization. In the first strategy, the intermediate feature vectors are dropped with adaptive dropout probabilities determined based on the standard deviation of these vectors. Then, by the chain rule, the intermediate gradient vectors associated with the dropped feature vectors are also dropped. In the second strategy, the non-dropped intermediate feature and gradient vectors are quantized using adaptive quantization levels determined based on the ranges of the vectors. To minimize the quantization error, the optimal quantization levels of this strategy are derived in a closed-form expression. Simulation results on the MNIST, CIFAR-10, and CelebA datasets demonstrate that SplitFC provides more than a 5.6% increase in classification accuracy compared to state-of-the-art SL frameworks, while they require 320 times less communication overhead compared to the vanilla SL framework without compression.  ( 2 min )
    Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehicles. (arXiv:2302.08292v3 [cs.CV] UPDATED)
    Autonomous driving (AD) perception today relies heavily on deep learning based architectures requiring large scale annotated datasets with their associated costs for curation and annotation. The 3D semantic data are useful for core perception tasks such as obstacle detection and ego-vehicle localization. We propose a new dataset, Navya 3D Segmentation (Navya3DSeg), with a diverse label space corresponding to a large scale production grade operational domain, including rural, urban, industrial sites and universities from 13 countries. It contains 23 labeled sequences and 25 supplementary sequences without labels, designed to explore self-supervised and semi-supervised semantic segmentation benchmarks on point clouds. We also propose a novel method for sequential dataset split generation based on iterative multi-label stratification, and demonstrated to achieve a +1.2% mIoU improvement over the original split proposed by SemanticKITTI dataset. A complete benchmark for semantic segmentation task was performed, with state of the art methods. Finally, we demonstrate an Active Learning (AL) based dataset distillation framework. We introduce a novel heuristic-free sampling method called ego-pose distance based sampling in the context of AL. A detailed presentation on the dataset is available here https://www.youtube.com/watch?v=5m6ALIs-s20.  ( 2 min )
    ForecastTKGQuestions: A Benchmark for Temporal Question Answering and Forecasting over Temporal Knowledge Graphs. (arXiv:2208.06501v2 [cs.AI] UPDATED)
    Question answering over temporal knowledge graphs (TKGQA) has recently found increasing interest. TKGQA requires temporal reasoning techniques to extract the relevant information from temporal knowledge bases. The only existing TKGQA dataset, i.e., CronQuestions, consists of temporal questions based on the facts from a fixed time period, where a temporal knowledge graph (TKG) spanning the same period can be fully used for answer inference, allowing the TKGQA models to use even the future knowledge to answer the questions based on the past facts. In real-world scenarios, however, it is also common that given the knowledge until now, we wish the TKGQA systems to answer the questions asking about the future. As humans constantly seek plans for the future, building TKGQA systems for answering such forecasting questions is important. Nevertheless, this has still been unexplored in previous research. In this paper, we propose a novel task: forecasting question answering over temporal knowledge graphs. We also propose a large-scale TKGQA benchmark dataset, i.e., ForecastTKGQuestions, for this task. It includes three types of questions, i.e., entity prediction, yes-no, and fact reasoning questions. For every forecasting question in our dataset, QA models can only have access to the TKG information before the timestamp annotated in the given question for answer inference. We find that the state-of-the-art TKGQA methods perform poorly on forecasting questions, and they are unable to answer yes-no questions and fact reasoning questions. To this end, we propose ForecastTKGQA, a TKGQA model that employs a TKG forecasting module for future inference, to answer all three types of questions. Experimental results show that ForecastTKGQA outperforms recent TKGQA methods on the entity prediction questions, and it also shows great effectiveness in answering the other two types of questions.  ( 3 min )
    High-order Tensor Pooling with Attention for Action Recognition. (arXiv:2110.05216v2 [cs.CV] UPDATED)
    We aim at capturing high-order statistics of feature vectors formed by a neural network, and propose end-to-end second- and higher-order pooling to form a tensor descriptor. Tensor descriptors require a robust similarity measure due to low numbers of aggregated vectors and the burstiness phenomenon, when a given feature appears more/less frequently than statistically expected. The Heat Diffusion Process (HDP) on a graph Laplacian is closely related to the Eigenvalue Power Normalization (EPN) of the covariance/auto-correlation matrix, whose inverse forms a loopy graph Laplacian. We show that the HDP and the EPN play the same role, i.e., to boost or dampen the magnitude of the eigenspectrum thus preventing the burstiness. We equip higher-order tensors with EPN which acts as a spectral detector of higher-order occurrences to prevent burstiness. We also prove that for a tensor of order r built from d dimensional feature descriptors, such a detector gives the likelihood if at least one higher-order occurrence is 'projected' into one of binom(d,r) subspaces represented by the tensor; thus forming a tensor power normalization metric endowed with binom(d,r) such 'detectors'. For experimental contributions, we apply several second- and higher-order pooling variants to action recognition, provide previously not presented comparisons of such pooling variants, and show state-of-the-art results on HMDB-51, YUP++ and MPII Cooking Activities.  ( 3 min )
    Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients. (arXiv:2212.14319v3 [stat.ML] UPDATED)
    Partial differential equations (PDEs) are important tools to model physical systems and including them into machine learning models is an important way of incorporating physical knowledge. Given any system of linear PDEs with constant coefficients, we propose a family of Gaussian process (GP) priors, which we call EPGP, such that all realizations are exact solutions of this system. We apply the Ehrenpreis-Palamodov fundamental principle, which works as a non-linear Fourier transform, to construct GP kernels mirroring standard spectral methods for GPs. Our approach can infer probable solutions of linear PDE systems from any data such as noisy measurements, or pointwise defined initial and boundary conditions. Constructing EPGP-priors is algorithmic, generally applicable, and comes with a sparse version (S-EPGP) that learns the relevant spectral frequencies and works better for big data sets. We demonstrate our approach on three families of systems of PDEs, the heat equation, wave equation, and Maxwell's equations, where we improve upon the state of the art in computation time and precision, in some experiments by several orders of magnitude.  ( 3 min )
    Multi-view self-supervised learning for multivariate variable-channel time series. (arXiv:2307.09614v2 [stat.ML] UPDATED)
    Labeling of multivariate biomedical time series data is a laborious and expensive process. Self-supervised contrastive learning alleviates the need for large, labeled datasets through pretraining on unlabeled data. However, for multivariate time series data, the set of input channels often varies between applications, and most existing work does not allow for transfer between datasets with different sets of input channels. We propose learning one encoder to operate on all input channels individually. We then use a message passing neural network to extract a single representation across channels. We demonstrate the potential of this method by pretraining our model on a dataset with six EEG channels and then fine-tuning it on a dataset with two different EEG channels. We compare models with and without the message passing neural network across different contrastive loss functions. We show that our method, combined with the TS2Vec loss, outperforms all other methods in most settings.  ( 2 min )
    MaxViT-UNet: Multi-Axis Attention for Medical Image Segmentation. (arXiv:2305.08396v3 [eess.IV] UPDATED)
    Convolutional Neural Networks (CNNs) have made significant strides in medical image analysis in recent years. However, the local nature of the convolution operator may pose a limitation for capturing global and long-range interactions in CNNs. Recently, Transformers have gained popularity in the computer vision community and also medical image segmentation due to their ability to process global features effectively. The scalability issues of self-attention mechanism and lack of the CNN-like inductive bias may have limited their adoption. Therefore, hybrid Vision transformers (CNN-Transformer), exploiting advantages of both Convolution and Self-attention Mechanisms, have gained importance. In this work, we present MaxViT-UNet, an Encoder-Decoder based hybrid vision transformer (CNN-Transformer) for medical image segmentation. The proposed Hybrid Decoder, based on MaxViT-block, is designed to harness the power of both the convolution and self-attention mechanisms at each decoding stage with nominal computational burden. The inclusion of multi-axis self-attention, within each decoder stage, significantly enhances the discriminating capacity between the object and background regions, and thereby helps in improving the segmentation efficiency. In the Hybrid Decoder block, the fusion process commences by integrating the upsampled lower level decoder features, obtained through transpose convolution, with the skip-connection features derived from the hybrid encoder. Subsequently, the fused features undergo refinement through the utilization of a multi-axis attention mechanism. The proposed decoder block is repeated multiple times to progressively segment the nuclei regions. Experimental results on MoNuSeg18 and MoNuSAC20 dataset demonstrates the effectiveness of the proposed technique.  ( 3 min )
    How to choose the most appropriate centrality measure? A decision tree approach. (arXiv:2003.01052v5 [physics.soc-ph] UPDATED)
    Centrality metrics are vital for network analysis, but selecting the most appropriate measures for specific applications remains challenging among the 400+ proposed indices. Existing approaches -- model-based, data-driven, and axiomatic -- have limitations. To address this, we introduce the culling method, leveraging expert preferences regarding centrality behavior on simple graphs. It involves forming a set of candidate measures, generating a list of as small graphs as possible needed to ``separate'' measures from each other, constructing a decision-tree survey, and identifying the measure consistent with expert responses. We apply this method to a diverse set of 40 centralities, including new kernel-based measures, and combine it with the axiomatic approach. Remarkably, only 13 small 1-trees suffice to separate all 40 measures, among which there are pairs of close ones. The culling method offers a low-cost solution in terms of labor and time, complements existing methods for measure selection, and reveals important peculiarities of centrality measures.  ( 2 min )
    Drug Repurposing Targeting COVID-19 3CL Protease using Molecular Docking and Machine Learning Regression Approach. (arXiv:2305.18088v4 [q-bio.BM] UPDATED)
    The COVID-19 pandemic has created a global health crisis, driving the need for the rapid identification of potential therapeutics. To meet this challenge, drug repurposing is the only solution with saving cost, time, and labor. In this study, we used the Zinc database to screen the world-approved including FDA-approved 5903 drugs for repurposing as potential COVID-19 treatments targeting the main protease 3CL of SARS-CoV-2. We performed molecular docking and checked the efficacy of drug molecules. To enhance the efficiency of drug repurposing approach, we modeled the binding affinities using several machine learning regression approaches for QSAR modeling such as decision tree, extra trees, MLP, KNN, XGBoost, and gradient boosting. The computational results demonstrated that Decision Tree Regression (DTR) model has improved statistical measures of R2 and RMSE. These simulated results helped to identify drugs with high binding affinity. From the docking and other statistical analysis, we shortlisted six promising drugs with their respective Zinc IDs (ZINC3873365, ZINC85432544, ZINC203757351, ZINC85536956, ZINC8214470 and ZINC261494640) within the range of -15 kcal/mol to -13 kcal/mol. In the study, the repurposed drugs are novel except ZINC203757351 antiviral compound that has already identified against COVID-19 in other studies. Further, we analyzed the physiochemical and pharmacokinetic properties of these top-ranked selected drugs with respect to their best binding interaction for specific target protease 3CLpro. Our study has provided an efficient framework for drug repurposing against COVID-19. This highlights the potential of combining molecular docking with machine learning regression approaches to accelerate the identification of potential therapeutic candidates.  ( 3 min )
    Correcting Underrepresentation and Intersectional Bias for Fair Classification. (arXiv:2306.11112v2 [cs.LG] UPDATED)
    We consider the problem of learning from data corrupted by underrepresentation bias, where positive examples are filtered from the data at different, unknown rates for a fixed number of sensitive groups. We show that with a small amount of unbiased data, we can efficiently estimate the group-wise drop-out parameters, even in settings where intersectional group membership makes learning each intersectional rate computationally infeasible. Using this estimate for the group-wise drop-out rate, we construct a re-weighting scheme that allows us to approximate the loss of any hypothesis on the true distribution, even if we only observe the empirical error on a biased sample. Finally, we present an algorithm encapsulating this learning and re-weighting process, and we provide strong PAC-style guarantees that, with high probability, our estimate of the risk of the hypothesis over the true distribution will be arbitrarily close to the true risk.  ( 2 min )
    Efficient Beam Tree Recursion. (arXiv:2307.10779v1 [cs.LG])
    Beam Tree Recursive Neural Network (BT-RvNN) was recently proposed as a simple extension of Gumbel Tree RvNN and it was shown to achieve state-of-the-art length generalization performance in ListOps while maintaining comparable performance on other tasks. However, although not the worst in its kind, BT-RvNN can be still exorbitantly expensive in memory usage. In this paper, we identify the main bottleneck in BT-RvNN's memory usage to be the entanglement of the scorer function and the recursive cell function. We propose strategies to remove this bottleneck and further simplify its memory usage. Overall, our strategies not only reduce the memory usage of BT-RvNN by $10$-$16$ times but also create a new state-of-the-art in ListOps while maintaining similar performance in other tasks. In addition, we also propose a strategy to utilize the induced latent-tree node representations produced by BT-RvNN to turn BT-RvNN from a sentence encoder of the form $f:\mathbb{R}^{n \times d} \rightarrow \mathbb{R}^{d}$ into a sequence contextualizer of the form $f:\mathbb{R}^{n \times d} \rightarrow \mathbb{R}^{n \times d}$. Thus, our proposals not only open up a path for further scalability of RvNNs but also standardize a way to use BT-RvNNs as another building block in the deep learning toolkit that can be easily stacked or interfaced with other popular models such as Transformers and Structured State Space models.  ( 2 min )
    Sequential Predictive Two-Sample and Independence Testing. (arXiv:2305.00143v2 [stat.ML] UPDATED)
    We study the problems of sequential nonparametric two-sample and independence testing. Sequential tests process data online and allow using observed data to decide whether to stop and reject the null hypothesis or to collect more data, while maintaining type I error control. We build upon the principle of (nonparametric) testing by betting, where a gambler places bets on future observations and their wealth measures evidence against the null hypothesis. While recently developed kernel-based betting strategies often work well on simple distributions, selecting a suitable kernel for high-dimensional or structured data, such as images, is often nontrivial. To address this drawback, we design prediction-based betting strategies that rely on the following fact: if a sequentially updated predictor starts to consistently determine (a) which distribution an instance is drawn from, or (b) whether an instance is drawn from the joint distribution or the product of the marginal distributions (the latter produced by external randomization), it provides evidence against the two-sample or independence nulls respectively. We empirically demonstrate the superiority of our tests over kernel-based approaches under structured settings. Our tests can be applied beyond the case of independent and identically distributed data, remaining valid and powerful even when the data distribution drifts over time.  ( 2 min )
    On Combining Expert Demonstrations in Imitation Learning via Optimal Transport. (arXiv:2307.10810v1 [cs.LG])
    Imitation learning (IL) seeks to teach agents specific tasks through expert demonstrations. One of the key approaches to IL is to define a distance between agent and expert and to find an agent policy that minimizes that distance. Optimal transport methods have been widely used in imitation learning as they provide ways to measure meaningful distances between agent and expert trajectories. However, the problem of how to optimally combine multiple expert demonstrations has not been widely studied. The standard method is to simply concatenate state (-action) trajectories, which is problematic when trajectories are multi-modal. We propose an alternative method that uses a multi-marginal optimal transport distance and enables the combination of multiple and diverse state-trajectories in the OT sense, providing a more sensible geometric average of the demonstrations. Our approach enables an agent to learn from several experts, and its efficiency is analyzed on OpenAI Gym control environments and demonstrates that the standard method is not always optimal.  ( 2 min )
    It Is All About Data: A Survey on the Effects of Data on Adversarial Robustness. (arXiv:2303.09767v2 [cs.LG] UPDATED)
    Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to confuse the model into making a mistake. Such examples pose a serious threat to the applicability of machine-learning-based systems, especially in life- and safety-critical domains. To address this problem, the area of adversarial robustness investigates mechanisms behind adversarial attacks and defenses against these attacks. This survey reviews a particular subset of this literature that focuses on investigating properties of training data in the context of model robustness under evasion attacks. It first summarizes the main properties of data leading to adversarial vulnerability. It then discusses guidelines and techniques for improving adversarial robustness by enhancing the data representation and learning procedures, as well as techniques for estimating robustness guarantees given particular data. Finally, it discusses gaps of knowledge and promising future research directions in this area.  ( 2 min )
    Syntactic vs Semantic Linear Abstraction and Refinement of Neural Networks. (arXiv:2307.10891v1 [cs.LO])
    Abstraction is a key verification technique to improve scalability. However, its use for neural networks is so far extremely limited. Previous approaches for abstracting classification networks replace several neurons with one of them that is similar enough. We can classify the similarity as defined either syntactically (using quantities on the connections between neurons) or semantically (on the activation values of neurons for various inputs). Unfortunately, the previous approaches only achieve moderate reductions, when implemented at all. In this work, we provide a more flexible framework where a neuron can be replaced with a linear combination of other neurons, improving the reduction. We apply this approach both on syntactic and semantic abstractions, and implement and evaluate them experimentally. Further, we introduce a refinement method for our abstractions, allowing for finding a better balance between reduction and precision.  ( 2 min )
    Pythae: Unifying Generative Autoencoders in Python -- A Benchmarking Use Case. (arXiv:2206.08309v2 [cs.LG] UPDATED)
    In recent years, deep generative models have attracted increasing interest due to their capacity to model complex distributions. Among those models, variational autoencoders have gained popularity as they have proven both to be computationally efficient and yield impressive results in multiple fields. Following this breakthrough, extensive research has been done in order to improve the original publication, resulting in a variety of different VAE models in response to different tasks. In this paper we present Pythae, a versatile open-source Python library providing both a unified implementation and a dedicated framework allowing straightforward, reproducible and reliable use of generative autoencoder models. We then propose to use this library to perform a case study benchmark where we present and compare 19 generative autoencoder models representative of some of the main improvements on downstream tasks such as image reconstruction, generation, classification, clustering and interpolation. The open-source library can be found at https://github.com/clementchadebec/benchmark_VAE.  ( 2 min )
    SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretability. (arXiv:2208.09418v3 [cs.LG] UPDATED)
    Interpretability of Deep Learning (DL) is a barrier to trustworthy AI. Despite great efforts made by the Explainable AI (XAI) community, explanations lack robustness -- indistinguishable input perturbations may lead to different XAI results. Thus, it is vital to assess how robust DL interpretability is, given an XAI method. In this paper, we identify several challenges that the state-of-the-art is unable to cope with collectively: i) existing metrics are not comprehensive; ii) XAI techniques are highly heterogeneous; iii) misinterpretations are normally rare events. To tackle these challenges, we introduce two black-box evaluation methods, concerning the worst-case interpretation discrepancy and a probabilistic notion of how robust in general, respectively. Genetic Algorithm (GA) with bespoke fitness function is used to solve constrained optimisation for efficient worst-case evaluation. Subset Simulation (SS), dedicated to estimate rare event probabilities, is used for evaluating overall robustness. Experiments show that the accuracy, sensitivity, and efficiency of our methods outperform the state-of-the-arts. Finally, we demonstrate two applications of our methods: ranking robust XAI methods and selecting training schemes to improve both classification and interpretation robustness.  ( 2 min )
    Variational Mixture of HyperGenerators for Learning Distributions Over Functions. (arXiv:2302.06223v3 [cs.LG] UPDATED)
    Recent approaches build on implicit neural representations (INRs) to propose generative models over function spaces. However, they are computationally costly when dealing with inference tasks, such as missing data imputation, or directly cannot tackle them. In this work, we propose a novel deep generative model, named VAMoH. VAMoH combines the capabilities of modeling continuous functions using INRs and the inference capabilities of Variational Autoencoders (VAEs). In addition, VAMoH relies on a normalizing flow to define the prior, and a mixture of hypernetworks to parametrize the data log-likelihood. This gives VAMoH a high expressive capability and interpretability. Through experiments on a diverse range of data types, such as images, voxels, and climate data, we show that VAMoH can effectively learn rich distributions over continuous functions. Furthermore, it can perform inference-related tasks, such as conditional super-resolution generation and in-painting, as well or better than previous approaches, while being less computationally demanding.  ( 2 min )
    Efficient Action Robust Reinforcement Learning with Probabilistic Policy Execution Uncertainty. (arXiv:2307.07666v2 [cs.LG] UPDATED)
    Robust reinforcement learning (RL) aims to find a policy that optimizes the worst-case performance in the face of uncertainties. In this paper, we focus on action robust RL with the probabilistic policy execution uncertainty, in which, instead of always carrying out the action specified by the policy, the agent will take the action specified by the policy with probability $1-\rho$ and an alternative adversarial action with probability $\rho$. We establish the existence of an optimal policy on the action robust MDPs with probabilistic policy execution uncertainty and provide the action robust Bellman optimality equation for its solution. Furthermore, we develop Action Robust Reinforcement Learning with Certificates (ARRLC) algorithm that achieves minimax optimal regret and sample complexity. Furthermore, we conduct numerical experiments to validate our approach's robustness, demonstrating that ARRLC outperforms non-robust RL algorithms and converges faster than the robust TD algorithm in the presence of action perturbations.  ( 2 min )
    Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities. (arXiv:2307.10803v1 [cs.LG])
    With the increasing amount of spatial-temporal~(ST) ocean data, numerous spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, e.g., climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data is more complicated with some unique characteristics, e.g., diverse regionality and high sparsity. These characteristics make it difficult to design and train STDM models. Unfortunately, an overview of these studies is still missing, hindering computer scientists to identify the research issues in ocean while discouraging researchers in ocean science from applying advanced STDM techniques. To remedy this situation, we provide a comprehensive survey to summarize existing STDM studies in ocean. Concretely, we first summarize the widely-used ST ocean datasets and identify their unique characteristics. Then, typical ST ocean data quality enhancement techniques are discussed. Next, we classify existing STDM studies for ocean into four types of tasks, i.e., prediction, event detection, pattern mining, and anomaly detection, and elaborate the techniques for these tasks. Finally, promising research opportunities are highlighted. This survey will help scientists from the fields of both computer science and ocean science have a better understanding of the fundamental concepts, key techniques, and open challenges of STDM in ocean.  ( 3 min )
    Topological Point Cloud Clustering. (arXiv:2303.16716v2 [math.AT] UPDATED)
    We present Topological Point Cloud Clustering (TPCC), a new method to cluster points in an arbitrary point cloud based on their contribution to global topological features. TPCC synthesizes desirable features from spectral clustering and topological data analysis and is based on considering the spectral properties of a simplicial complex associated to the considered point cloud. As it is based on considering sparse eigenvector computations, TPCC is similarly easy to interpret and implement as spectral clustering. However, by focusing not just on a single matrix associated to a graph created from the point cloud data, but on a whole set of Hodge-Laplacians associated to an appropriately constructed simplicial complex, we can leverage a far richer set of topological features to characterize the data points within the point cloud and benefit from the relative robustness of topological techniques against noise. We test the performance of TPCC on both synthetic and real-world data and compare it with classical spectral clustering.  ( 2 min )
    Deep-Q Learning with Hybrid Quantum Neural Network on Solving Maze Problems. (arXiv:2304.10159v2 [quant-ph] UPDATED)
    Quantum computing holds great potential for advancing the limitations of machine learning algorithms to handle higher data dimensions and reduce overall training parameters in deep neural network (DNN) models. This study uses a parameterized quantum circuit (PQC) on a gate-based quantum computer to investigate the potential for quantum advantage in a model-free reinforcement learning problem. Through a comprehensive investigation and evaluation of the current model and capabilities of quantum computers, we designed and trained a novel hybrid Quantum neural network based on the latest Qiskit and PyTorch framework. We compared its performance with a full-classical DNN with and without an integrated PQC. Our research provides insights into the potential of deep quantum learning to solve a maze problem and, potentially, other reinforcement learning problems. We conclude that various reinforcement learning problems can be effective with reasonable training epochs. Moreover, a comparative discussion of the various quantum reinforcement learning model on maze problems is discussed to evaluate our research's overall potential and advantages.  ( 2 min )
    Quantitative CLTs in Deep Neural Networks. (arXiv:2307.06092v2 [cs.LG] UPDATED)
    We study the distribution of a fully connected neural network with random Gaussian weights and biases in which the hidden layer widths are proportional to a large constant $n$. Under mild assumptions on the non-linearity, we obtain quantitative bounds on normal approximations valid at large but finite $n$ and any fixed network depth. Our theorems show both for the finite-dimensional distributions and the entire process, that the distance between a random fully connected network (and its derivatives) to the corresponding infinite width Gaussian process scales like $n^{-\gamma}$ for $\gamma>0$, with the exponent depending on the metric used to measure discrepancy. Our bounds are strictly stronger in terms of their dependence on network width than any previously available in the literature; in the one-dimensional case, we also prove that they are optimal, i.e., we establish matching lower bounds.
    When are Local Queries Useful for Robust Learning?. (arXiv:2210.06089v2 [cs.LG] UPDATED)
    Distributional assumptions have been shown to be necessary for the robust learnability of concept classes when considering the exact-in-the-ball robust risk and access to random examples by Gourdeau et al. (2019). In this paper, we study learning models where the learner is given more power through the use of local queries, and give the first distribution-free algorithms that perform robust empirical risk minimization (ERM) for this notion of robustness. The first learning model we consider uses local membership queries (LMQ), where the learner can query the label of points near the training sample. We show that, under the uniform distribution, LMQs do not increase the robustness threshold of conjunctions and any superclass, e.g., decision lists and halfspaces. Faced with this negative result, we introduce the local equivalence query ($\mathsf{LEQ}$) oracle, which returns whether the hypothesis and target concept agree in the perturbation region around a point in the training sample, as well as a counterexample if it exists. We show a separation result: on the one hand, if the query radius $\lambda$ is strictly smaller than the adversary's perturbation budget $\rho$, then distribution-free robust learning is impossible for a wide variety of concept classes; on the other hand, the setting $\lambda=\rho$ allows us to develop robust ERM algorithms. We then bound the query complexity of these algorithms based on online learning guarantees and further improve these bounds for the special case of conjunctions. We finish by giving robust learning algorithms for halfspaces on $\{0,1\}^n$ and then obtaining robustness guarantees for halfspaces in $\mathbb{R}^n$ against precision-bounded adversaries.
    Perceptron Theory Can Predict the Accuracy of Neural Networks. (arXiv:2012.07881v2 [cs.LG] UPDATED)
    Multilayer neural networks set the current state of the art for many technical classification problems. But, these networks are still, essentially, black boxes in terms of analyzing them and predicting their performance. Here, we develop a statistical theory for the one-layer perceptron and show that it can predict performances of a surprisingly large variety of neural networks with different architectures. A general theory of classification with perceptrons is developed by generalizing an existing theory for analyzing reservoir computing models and connectionist models for symbolic reasoning known as vector symbolic architectures. Our statistical theory offers three formulas leveraging the signal statistics with increasing detail. The formulas are analytically intractable, but can be evaluated numerically. The description level that captures maximum details requires stochastic sampling methods. Depending on the network model, the simpler formulas already yield high prediction accuracy. The quality of the theory predictions is assessed in three experimental settings, a memorization task for echo state networks (ESNs) from reservoir computing literature, a collection of classification datasets for shallow randomly connected networks, and the ImageNet dataset for deep convolutional neural networks. We find that the second description level of the perceptron theory can predict the performance of types of ESNs, which could not be described previously. The theory can predict deep multilayer neural networks by being applied to their output layer. While other methods for prediction of neural networks performance commonly require to train an estimator model, the proposed theory requires only the first two moments of the distribution of the postsynaptic sums in the output neurons. The perceptron theory compares favorably to other methods that do not rely on training an estimator model.  ( 3 min )
    Data-Driven Latency Probability Prediction for Wireless Networks: Focusing on Tail Probabilities. (arXiv:2307.10648v1 [cs.NI])
    With the emergence of new application areas, such as cyber-physical systems and human-in-the-loop applications, there is a need to guarantee a certain level of end-to-end network latency with extremely high reliability, e.g., 99.999%. While mechanisms specified under IEEE 802.1as time-sensitive networking (TSN) can be used to achieve these requirements for switched Ethernet networks, implementing TSN mechanisms in wireless networks is challenging due to their stochastic nature. To conform the wireless link to a reliability level of 99.999%, the behavior of extremely rare outliers in the latency probability distribution, or the tail of the distribution, must be analyzed and controlled. This work proposes predicting the tail of the latency distribution using state-of-the-art data-driven approaches, such as mixture density networks (MDN) and extreme value mixture models, to estimate the likelihood of rare latencies conditioned on the network parameters, which can be used to make more informed decisions in wireless transmission. Actual latency measurements of IEEE 802.11g (WiFi), commercial private and a software-defined 5G network are used to benchmark the proposed approaches and evaluate their sensitivities concerning the tail probabilities.
    Injecting Domain Adaptation with Learning-to-hash for Effective and Efficient Zero-shot Dense Retrieval. (arXiv:2205.11498v2 [cs.IR] UPDATED)
    Dense retrieval overcome the lexical gap and has shown great success in ad-hoc information retrieval (IR). Despite their success, dense retrievers are expensive to serve across practical use cases. For use cases requiring to search from millions of documents, the dense index becomes bulky and requires high memory usage for storing the index. More recently, learning-to-hash (LTH) techniques, for e.g., BPR and JPQ, produce binary document vectors, thereby reducing the memory requirement to efficiently store the dense index. LTH techniques are supervised and finetune the retriever using a ranking loss. They outperform their counterparts, i.e., traditional out-of-the-box vector compression techniques such as PCA or PQ. A missing piece from prior work is that existing techniques have been evaluated only in-domain, i.e., on a single dataset such as MS MARCO. In our work, we evaluate LTH and vector compression techniques for improving the downstream zero-shot retrieval accuracy of the TAS-B dense retriever while maintaining efficiency at inference. Our results demonstrate that, unlike prior work, LTH strategies when applied naively can underperform the zero-shot TAS-B dense retriever on average by up to 14% nDCG@10 on the BEIR benchmark. To solve this limitation, in our work, we propose an easy yet effective solution of injecting domain adaptation with existing supervised LTH techniques. We experiment with two well-known unsupervised domain adaptation techniques: GenQ and GPL. Our domain adaptation injection technique can improve the downstream zero-shot retrieval effectiveness for both BPR and JPQ variants of the TAS-B model by on average 11.5% and 8.2% nDCG@10 while both maintaining 32$\times$ memory efficiency and 14$\times$ and 2$\times$ speedup respectively in CPU retrieval latency on BEIR. All our code, models, and data are publicly available at https://github.com/thakur-nandan/income.
    Positive unlabeled learning with tensor networks. (arXiv:2211.14085v3 [cs.LG] UPDATED)
    Positive unlabeled learning is a binary classification problem with positive and unlabeled data. It is common in domains where negative labels are costly or impossible to obtain, e.g., medicine and personalized advertising. Most approaches to positive unlabeled learning apply to specific data types (e.g., images, categorical data) and can not generate new positive and negative samples. This work introduces a feature-space distance-based tensor network approach to the positive unlabeled learning problem. The presented method is not domain specific and significantly improves the state-of-the-art results on the MNIST image and 15 categorical/mixed datasets. The trained tensor network model is also a generative model and enables the generation of new positive and negative instances.
    Chordal Averaging on Flag Manifolds and Its Applications. (arXiv:2303.13501v2 [cs.CV] UPDATED)
    This paper presents a new, provably-convergent algorithm for computing the flag-mean and flag-median of a set of points on a flag manifold under the chordal metric. The flag manifold is a mathematical space consisting of flags, which are sequences of nested subspaces of a vector space that increase in dimension. The flag manifold is a superset of a wide range of known matrix spaces, including Stiefel and Grassmanians, making it a general object that is useful in a wide variety computer vision problems. To tackle the challenge of computing first order flag statistics, we first transform the problem into one that involves auxiliary variables constrained to the Stiefel manifold. The Stiefel manifold is a space of orthogonal frames, and leveraging the numerical stability and efficiency of Stiefel-manifold optimization enables us to compute the flag-mean effectively. Through a series of experiments, we show the competence of our method in Grassmann and rotation averaging, as well as principal component analysis. We release our source code under https://github.com/nmank/FlagAveraging.  ( 2 min )
    A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency. (arXiv:2307.10655v1 [cs.LG])
    Federated learning (FL) has emerged as a highly effective paradigm for privacy-preserving collaborative training among different parties. Unlike traditional centralized learning, which requires collecting data from each party, FL allows clients to share privacy-preserving information without exposing private datasets. This approach not only guarantees enhanced privacy protection but also facilitates more efficient and secure collaboration among multiple participants. Therefore, FL has gained considerable attention from researchers, promoting numerous surveys to summarize the related works. However, the majority of these surveys concentrate on methods sharing model parameters during the training process, while overlooking the potential of sharing other forms of local information. In this paper, we present a systematic survey from a new perspective, i.e., what to share in FL, with an emphasis on the model utility, privacy leakage, and communication efficiency. This survey differs from previous ones due to four distinct contributions. First, we present a new taxonomy of FL methods in terms of the sharing methods, which includes three categories of shared information: model sharing, synthetic data sharing, and knowledge sharing. Second, we analyze the vulnerability of different sharing methods to privacy attacks and review the defense mechanisms that provide certain privacy guarantees. Third, we conduct extensive experiments to compare the performance and communication overhead of various sharing methods in FL. Besides, we assess the potential privacy leakage through model inversion and membership inference attacks, while comparing the effectiveness of various defense approaches. Finally, we discuss potential deficiencies in current methods and outline future directions for improvement.
    Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments. (arXiv:2211.10515v2 [stat.ML] UPDATED)
    Consider the problem of exploration in sparse-reward or reward-free environments, such as in Montezuma's Revenge. In the curiosity-driven paradigm, the agent is rewarded for how much each realized outcome differs from their predicted outcome. But using predictive error as intrinsic motivation is fragile in stochastic environments, as the agent may become trapped by high-entropy areas of the state-action space, such as a "noisy TV". In this work, we study a natural solution derived from structural causal models of the world: Our key idea is to learn representations of the future that capture precisely the unpredictable aspects of each outcome -- which we use as additional input for predictions, such that intrinsic rewards only reflect the predictable aspects of world dynamics. First, we propose incorporating such hindsight representations into models to disentangle "noise" from "novelty", yielding Curiosity in Hindsight: a simple and scalable generalization of curiosity that is robust to stochasticity. Second, we instantiate this framework for the recently introduced BYOL-Explore algorithm as our prime example, resulting in the noise-robust BYOL-Hindsight. Third, we illustrate its behavior under a variety of different stochasticities in a grid world, and find improvements over BYOL-Explore in hard-exploration Atari games with sticky actions. Notably, we show state-of-the-art results in exploring Montezuma's Revenge with sticky actions, while preserving performance in the non-sticky setting.
    Intelligent model for offshore China sea fog forecasting. (arXiv:2307.10580v1 [cs.LG])
    Accurate and timely prediction of sea fog is very important for effectively managing maritime and coastal economic activities. Given the intricate nature and inherent variability of sea fog, traditional numerical and statistical forecasting methods are often proven inadequate. This study aims to develop an advanced sea fog forecasting method embedded in a numerical weather prediction model using the Yangtze River Estuary (YRE) coastal area as a case study. Prior to training our machine learning model, we employ a time-lagged correlation analysis technique to identify key predictors and decipher the underlying mechanisms driving sea fog occurrence. In addition, we implement ensemble learning and a focal loss function to address the issue of imbalanced data, thereby enhancing the predictive ability of our model. To verify the accuracy of our method, we evaluate its performance using a comprehensive dataset spanning one year, which encompasses both weather station observations and historical forecasts. Remarkably, our machine learning-based approach surpasses the predictive performance of two conventional methods, the weather research and forecasting nonhydrostatic mesoscale model (WRF-NMM) and the algorithm developed by the National Oceanic and Atmospheric Administration (NOAA) Forecast Systems Laboratory (FSL). Specifically, in regard to predicting sea fog with a visibility of less than or equal to 1 km with a lead time of 60 hours, our methodology achieves superior results by increasing the probability of detection (POD) while simultaneously reducing the false alarm ratio (FAR).
    MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning. (arXiv:2209.07902v4 [cs.LG] UPDATED)
    As a successful approach to self-supervised learning, contrastive learning aims to learn invariant information shared among distortions of the input sample. While contrastive learning has yielded continuous advancements in sampling strategy and architecture design, it still remains two persistent defects: the interference of task-irrelevant information and sample inefficiency, which are related to the recurring existence of trivial constant solutions. From the perspective of dimensional analysis, we find out that the dimensional redundancy and dimensional confounder are the intrinsic issues behind the phenomena, and provide experimental evidence to support our viewpoint. We further propose a simple yet effective approach MetaMask, short for the dimensional Mask learned by Meta-learning, to learn representations against dimensional redundancy and confounder. MetaMask adopts the redundancy-reduction technique to tackle the dimensional redundancy issue and innovatively introduces a dimensional mask to reduce the gradient effects of specific dimensions containing the confounder, which is trained by employing a meta-learning paradigm with the objective of improving the performance of masked representations on a typical self-supervised task. We provide solid theoretical analyses to prove MetaMask can obtain tighter risk bounds for downstream classification compared to typical contrastive methods. Empirically, our method achieves state-of-the-art performance on various benchmarks.
    Can point cloud networks learn statistical shape models of anatomies?. (arXiv:2305.05610v2 [cs.CV] UPDATED)
    Statistical Shape Modeling (SSM) is a valuable tool for investigating and quantifying anatomical variations within populations of anatomies. However, traditional correspondence-based SSM generation methods have a prohibitive inference process and require complete geometric proxies (e.g., high-resolution binary volumes or surface meshes) as input shapes to construct the SSM. Unordered 3D point cloud representations of shapes are more easily acquired from various medical imaging practices (e.g., thresholded images and surface scanning). Point cloud deep networks have recently achieved remarkable success in learning permutation-invariant features for different point cloud tasks (e.g., completion, semantic segmentation, classification). However, their application to learning SSM from point clouds is to-date unexplored. In this work, we demonstrate that existing point cloud encoder-decoder-based completion networks can provide an untapped potential for SSM, capturing population-level statistical representations of shapes while reducing the inference burden and relaxing the input requirement. We discuss the limitations of these techniques to the SSM application and suggest future improvements. Our work paves the way for further exploration of point cloud deep learning for SSM, a promising avenue for advancing shape analysis literature and broadening SSM to diverse use cases.
    From Graph Generation to Graph Classification. (arXiv:2302.07989v2 [cs.LG] UPDATED)
    This note describes a new approach to classifying graphs that leverages graph generative models (GGM). Assuming a GGM that defines a joint probability distribution over graphs and their class labels, I derive classification formulas for the probability of a class label given a graph. A new conditional ELBO can be used to train a generative graph auto-encoder model for discrimination. While leveraging generative models for classification has been well explored for non-relational i.i.d. data, to our knowledge it is a novel approach to graph classification.
    PGCN: Progressive Graph Convolutional Networks for Spatial-Temporal Traffic Forecasting. (arXiv:2202.08982v2 [cs.LG] UPDATED)
    The complex spatial-temporal correlations in transportation networks make the traffic forecasting problem challenging. Since transportation system inherently possesses graph structures, much research efforts have been put with graph neural networks. Recently, constructing adaptive graphs to the data has shown promising results over the models relying on a single static graph structure. However, the graph adaptations are applied during the training phases, and do not reflect the data used during the testing phases. Such shortcomings can be problematic especially in traffic forecasting since the traffic data often suffers from the unexpected changes and irregularities in the time series. In this study, we propose a novel traffic forecasting framework called Progressive Graph Convolutional Network (PGCN). PGCN constructs a set of graphs by progressively adapting to input data during the training and the testing phases. Specifically, we implemented the model to construct progressive adjacency matrices by learning trend similarities among graph nodes. Then, the model is combined with the dilated causal convolution and gated activation unit to extract temporal features. With residual and skip connections, PGCN performs the traffic prediction. When applied to four real-world traffic datasets of diverse geometric nature, the proposed model achieves state-of-the-art performance with consistency in all datasets. We conclude that the ability of PGCN to progressively adapt to input data enables the model to generalize in different study sites with robustness.
    Conditional expectation network for SHAP. (arXiv:2307.10654v1 [cs.LG])
    A very popular model-agnostic technique for explaining predictive models is the SHapley Additive exPlanation (SHAP). The two most popular versions of SHAP are a conditional expectation version and an unconditional expectation version (the latter is also known as interventional SHAP). Except for tree-based methods, usually the unconditional version is used (for computational reasons). We provide a (surrogate) neural network approach which allows us to efficiently calculate the conditional version for both neural networks and other regression models, and which properly considers the dependence structure in the feature components. This proposal is also useful to provide drop1 and anova analyses in complex regression models which are similar to their generalized linear model (GLM) counterparts, and we provide a partial dependence plot (PDP) counterpart that considers the right dependence structure in the feature components.
    Optimizing PatchCore for Few/many-shot Anomaly Detection. (arXiv:2307.10792v1 [cs.CV])
    Few-shot anomaly detection (AD) is an emerging sub-field of general AD, and tries to distinguish between normal and anomalous data using only few selected samples. While newly proposed few-shot AD methods do compare against pre-existing algorithms developed for the full-shot domain as baselines, they do not dedicatedly optimize them for the few-shot setting. It thus remains unclear if the performance of such pre-existing algorithms can be further improved. We address said question in this work. Specifically, we present a study on the AD/anomaly segmentation (AS) performance of PatchCore, the current state-of-the-art full-shot AD/AS algorithm, in both the few-shot and the many-shot settings. We hypothesize that further performance improvements can be realized by (I) optimizing its various hyperparameters, and by (II) transferring techniques known to improve few-shot supervised learning to the AD domain. Exhaustive experiments on the public VisA and MVTec AD datasets reveal that (I) significant performance improvements can be realized by optimizing hyperparameters such as the underlying feature extractor, and that (II) image-level augmentations can, but are not guaranteed, to improve performance. Based on these findings, we achieve a new state of the art in few-shot AD on VisA, further demonstrating the merit of adapting pre-existing AD/AS methods to the few-shot setting. Last, we identify the investigation of feature extractors with a strong inductive bias as a potential future research direction for (few-shot) AD/AS.
    Leveraging Offline Data in Online Reinforcement Learning. (arXiv:2211.04974v2 [cs.LG] UPDATED)
    Two central paradigms have emerged in the reinforcement learning (RL) community: online RL and offline RL. In the online RL setting, the agent has no prior knowledge of the environment, and must interact with it in order to find an $\epsilon$-optimal policy. In the offline RL setting, the learner instead has access to a fixed dataset to learn from, but is unable to otherwise interact with the environment, and must obtain the best policy it can from this offline data. Practical scenarios often motivate an intermediate setting: if we have some set of offline data and, in addition, may also interact with the environment, how can we best use the offline data to minimize the number of online interactions necessary to learn an $\epsilon$-optimal policy? In this work, we consider this setting, which we call the \textsf{FineTuneRL} setting, for MDPs with linear structure. We characterize the necessary number of online samples needed in this setting given access to some offline dataset, and develop an algorithm, \textsc{FTPedel}, which is provably optimal, up to $H$ factors. We show through an explicit example that combining offline data with online interactions can lead to a provable improvement over either purely offline or purely online RL. Finally, our results illustrate the distinction between \emph{verifiable} learning, the typical setting considered in online RL, and \emph{unverifiable} learning, the setting often considered in offline RL, and show that there is a formal separation between these regimes.
    A DPLL(T) Framework for Verifying Deep Neural Networks. (arXiv:2307.10266v1 [cs.LG])
    Deep Neural Networks (DNNs) have emerged as an effective approach to tackling real-world problems. However, like human-written software, automatically-generated DNNs can have bugs and be attacked. This thus attracts many recent interests in developing effective and scalable DNN verification techniques and tools. In this work, we introduce a NeuralSAT, a new constraint solving approach to DNN verification. The design of NeuralSAT follows the DPLL(T) algorithm used modern SMT solving, which includes (conflict) clause learning, abstraction, and theory solving, and thus NeuralSAT can be considered as an SMT framework for DNNs. Preliminary results show that the NeuralSAT prototype is competitive to the state-of-the-art. We hope, with proper optimization and engineering, NeuralSAT will carry the power and success of modern SAT/SMT solvers to DNN verification. NeuralSAT is avaliable from: https://github.com/dynaroars/neuralsat-solver
    Tuning Stochastic Gradient Algorithms for Statistical Inference via Large-Sample Asymptotics. (arXiv:2207.12395v3 [stat.CO] UPDATED)
    The tuning of stochastic gradient algorithms (SGAs) for optimization and sampling is often based on heuristics and trial-and-error rather than generalizable theory. We address this theory--practice gap by characterizing the large-sample statistical asymptotics of SGAs via a joint step-size--sample-size scaling limit. We show that iterate averaging with a large fixed step size is robust to the choice of tuning parameters and asymptotically has covariance proportional to that of the MLE sampling distribution. We also prove a Bernstein--von Mises-like theorem to guide tuning, including for generalized posteriors that are robust to model misspecification. Numerical experiments validate our results and recommendations in realistic finite-sample regimes. Our work lays the foundation for a systematic analysis of other stochastic gradient Markov chain Monte Carlo algorithms for a wide range of models.
    Blockchain-Based Federated Learning: Incentivizing Data Sharing and Penalizing Dishonest Behavior. (arXiv:2307.10492v1 [cs.LG])
    With the increasing importance of data sharing for collaboration and innovation, it is becoming more important to ensure that data is managed and shared in a secure and trustworthy manner. Data governance is a common approach to managing data, but it faces many challenges such as data silos, data consistency, privacy, security, and access control. To address these challenges, this paper proposes a comprehensive framework that integrates data trust in federated learning with InterPlanetary File System, blockchain, and smart contracts to facilitate secure and mutually beneficial data sharing while providing incentives, access control mechanisms, and penalizing any dishonest behavior. The experimental results demonstrate that the proposed model is effective in improving the accuracy of federated learning models while ensuring the security and fairness of the data-sharing process. The research paper also presents a decentralized federated learning platform that successfully trained a CNN model on the MNIST dataset using blockchain technology. The platform enables multiple workers to train the model simultaneously while maintaining data privacy and security. The decentralized architecture and use of blockchain technology allow for efficient communication and coordination between workers. This platform has the potential to facilitate decentralized machine learning and support privacy-preserving collaboration in various domains.
    Zero-shot Domain-sensitive Speech Recognition with Prompt-conditioning Fine-tuning. (arXiv:2307.10274v1 [eess.AS])
    In this work, we propose a method to create domain-sensitive speech recognition models that utilize textual domain information by conditioning its generation on a given text prompt. This is accomplished by fine-tuning a pre-trained, end-to-end model (Whisper) to learn from demonstrations with prompt examples. We show that this ability can be generalized to different domains and even various prompt contexts, with our model gaining a Word Error Rate (WER) reduction of up to 33% on unseen datasets from various domains, such as medical conversation, air traffic control communication, and financial meetings. Considering the limited availability of audio-transcript pair data, we further extend our method to text-only fine-tuning to achieve domain sensitivity as well as domain adaptation. We demonstrate that our text-only fine-tuned model can also attend to various prompt contexts, with the model reaching the most WER reduction of 29% on the medical conversation dataset.
    IncDSI: Incrementally Updatable Document Retrieval. (arXiv:2307.10323v1 [cs.IR])
    Differentiable Search Index is a recently proposed paradigm for document retrieval, that encodes information about a corpus of documents within the parameters of a neural network and directly maps queries to corresponding documents. These models have achieved state-of-the-art performances for document retrieval across many benchmarks. These kinds of models have a significant limitation: it is not easy to add new documents after a model is trained. We propose IncDSI, a method to add documents in real time (about 20-50ms per document), without retraining the model on the entire dataset (or even parts thereof). Instead we formulate the addition of documents as a constrained optimization problem that makes minimal changes to the network parameters. Although orders of magnitude faster, our approach is competitive with re-training the model on the whole dataset and enables the development of document retrieval systems that can be updated with new information in real-time. Our code for IncDSI is available at https://github.com/varshakishore/IncDSI.
    A Machine Learning based Empirical Evaluation of Cyber Threat Actors High Level Attack Patterns over Low level Attack Patterns in Attributing Attacks. (arXiv:2307.10252v1 [cs.CR])
    Cyber threat attribution is the process of identifying the actor of an attack incident in cyberspace. An accurate and timely threat attribution plays an important role in deterring future attacks by applying appropriate and timely defense mechanisms. Manual analysis of attack patterns gathered by honeypot deployments, intrusion detection systems, firewalls, and via trace-back procedures is still the preferred method of security analysts for cyber threat attribution. Such attack patterns are low-level Indicators of Compromise (IOC). They represent Tactics, Techniques, Procedures (TTP), and software tools used by the adversaries in their campaigns. The adversaries rarely re-use them. They can also be manipulated, resulting in false and unfair attribution. To empirically evaluate and compare the effectiveness of both kinds of IOC, there are two problems that need to be addressed. The first problem is that in recent research works, the ineffectiveness of low-level IOC for cyber threat attribution has been discussed intuitively. An empirical evaluation for the measure of the effectiveness of low-level IOC based on a real-world dataset is missing. The second problem is that the available dataset for high-level IOC has a single instance for each predictive class label that cannot be used directly for training machine learning models. To address these problems in this research work, we empirically evaluate the effectiveness of low-level IOC based on a real-world dataset that is specifically built for comparative analysis with high-level IOC. The experimental results show that the high-level IOC trained models effectively attribute cyberattacks with an accuracy of 95% as compared to the low-level IOC trained models where accuracy is 40%.
    Fairness in AI and Its Long-Term Implications on Society. (arXiv:2304.09826v2 [cs.CY] UPDATED)
    Successful deployment of artificial intelligence (AI) in various settings has led to numerous positive outcomes for individuals and society. However, AI systems have also been shown to harm parts of the population due to biased predictions. AI fairness focuses on mitigating such biases to ensure AI decision making is not discriminatory towards certain groups. We take a closer look at AI fairness and analyze how lack of AI fairness can lead to deepening of biases over time and act as a social stressor. More specifically, we discuss how biased models can lead to more negative real-world outcomes for certain groups, which may then become more prevalent by deploying new AI models trained on increasingly biased data, resulting in a feedback loop. If the issues persist, they could be reinforced by interactions with other risks and have severe implications on society in the form of social unrest. We examine current strategies for improving AI fairness, assess their limitations in terms of real-world deployment, and explore potential paths forward to ensure we reap AI's benefits without causing society's collapse.
    Detecting deceptive reviews using text classification. (arXiv:2307.10617v1 [cs.IR])
    In recent years, online reviews play a vital role for promoting any kind of product or services. Businesses may embed fake reviews in order to attract customers to purchase their products. They may even highlight the benefits of their own product or criticize the competition's product. Marketers, advertisers, and other online business users have incentive to create fake positive reviews for products which they want to promote or give fake negative reviews for products which they really don't like. So now-a-days writing a deceptive review is inevitable thing for promoting their own business or degrading competitor's reputation. Thus, identifying deceptive reviews is an intense and on-going research area. This research paper proposes machine learning model approach to identify deceptive reviews. The paper investigates the performance of the several experiments done on a Deceptive Opinion Spam Corpus dataset of restaurants reviews. We developed a n-gram model and max features to identify deceptive contents with a particular focus on fake reviews. Further, we conduct a benchmark study to investigate the performance of two different features extraction techniques and apply five machine learning classification techniques. The experimental results show that passive aggressive classifier outperforms other algorithms, and it reaches the highest accuracy not only in text classification but also to fake reviews. We also study the data augmentation and implement different deep learning techniques.
    Exploring Link Prediction over Hyper-Relational Temporal Knowledge Graphs Enhanced with Time-Invariant Relational Knowledge. (arXiv:2307.10219v1 [cs.AI])
    Stemming from traditional knowledge graphs (KGs), hyper-relational KGs (HKGs) provide additional key-value pairs (i.e., qualifiers) for each KG fact that help to better restrict the fact validity. In recent years, there has been an increasing interest in studying graph reasoning over HKGs. In the meantime, due to the ever-evolving nature of world knowledge, extensive parallel works have been focusing on reasoning over temporal KGs (TKGs), where each TKG fact can be viewed as a KG fact coupled with a timestamp (or time period) specifying its time validity. The existing HKG reasoning approaches do not consider temporal information because it is not explicitly specified in previous benchmark datasets. Besides, all the previous TKG reasoning methods only lay emphasis on temporal reasoning and have no way to learn from qualifiers. To this end, we aim to fill the gap between TKG reasoning and HKG reasoning. We develop two new benchmark hyper-relational TKG (HTKG) datasets, i.e., Wiki-hy and YAGO-hy, and propose a HTKG reasoning model that efficiently models both temporal facts and qualifiers. We further exploit additional time-invariant relational knowledge from the Wikidata knowledge base and study its effectiveness in HTKG reasoning. Time-invariant relational knowledge serves as the knowledge that remains unchanged in time (e.g., Sasha Obama is the child of Barack Obama), and it has never been fully explored in previous TKG reasoning benchmarks and approaches. Experimental results show that our model substantially outperforms previous related methods on HTKG link prediction and can be enhanced by jointly leveraging both temporal and time-invariant relational knowledge.
    Mathematical Capabilities of ChatGPT. (arXiv:2301.13867v2 [cs.LG] UPDATED)
    We investigate the mathematical capabilities of two iterations of ChatGPT (released 9-January-2023 and 30-January-2023) and of GPT-4 by testing them on publicly available datasets, as well as hand-crafted ones, using a novel methodology. In contrast to formal mathematics, where large databases of formal proofs are available (e.g., the Lean Mathematical Library), current datasets of natural-language mathematics, used to benchmark language models, either cover only elementary mathematics or are very small. We address this by publicly releasing two new datasets: GHOSTS and miniGHOSTS. These are the first natural-language datasets curated by working researchers in mathematics that (1) aim to cover graduate-level mathematics, (2) provide a holistic overview of the mathematical capabilities of language models, and (3) distinguish multiple dimensions of mathematical reasoning. These datasets also test whether ChatGPT and GPT-4 can be helpful assistants to professional mathematicians by emulating use cases that arise in the daily professional activities of mathematicians. We benchmark the models on a range of fine-grained performance metrics. For advanced mathematics, this is the most detailed evaluation effort to date. We find that ChatGPT can be used most successfully as a mathematical assistant for querying facts, acting as a mathematical search engine and knowledge base interface. GPT-4 can additionally be used for undergraduate-level mathematics but fails on graduate-level difficulty. Contrary to many positive reports in the media about GPT-4 and ChatGPT's exam-solving abilities (a potential case of selection bias), their overall mathematical performance is well below the level of a graduate student. Hence, if your goal is to use ChatGPT to pass a graduate-level math exam, you would be better off copying from your average peer!
    Self-paced Weight Consolidation for Continual Learning. (arXiv:2307.10845v1 [cs.LG])
    Continual learning algorithms which keep the parameters of new tasks close to that of previous tasks, are popular in preventing catastrophic forgetting in sequential task learning settings. However, 1) the performance for the new continual learner will be degraded without distinguishing the contributions of previously learned tasks; 2) the computational cost will be greatly increased with the number of tasks, since most existing algorithms need to regularize all previous tasks when learning new tasks. To address the above challenges, we propose a self-paced Weight Consolidation (spWC) framework to attain robust continual learning via evaluating the discriminative contributions of previous tasks. To be specific, we develop a self-paced regularization to reflect the priorities of past tasks via measuring difficulty based on key performance indicator (i.e., accuracy). When encountering a new task, all previous tasks are sorted from "difficult" to "easy" based on the priorities. Then the parameters of the new continual learner will be learned via selectively maintaining the knowledge amongst more difficult past tasks, which could well overcome catastrophic forgetting with less computational cost. We adopt an alternative convex search to iteratively update the model parameters and priority weights in the bi-convex formulation. The proposed spWC framework is plug-and-play, which is applicable to most continual learning algorithms (e.g., EWC, MAS and RCIL) in different directions (e.g., classification and segmentation). Experimental results on several public benchmark datasets demonstrate that our proposed framework can effectively improve performance when compared with other popular continual learning algorithms.
    SC VALL-E: Style-Controllable Zero-Shot Text to Speech Synthesizer. (arXiv:2307.10550v1 [cs.SD])
    Expressive speech synthesis models are trained by adding corpora with diverse speakers, various emotions, and different speaking styles to the dataset, in order to control various characteristics of speech and generate the desired voice. In this paper, we propose a style control (SC) VALL-E model based on the neural codec language model (called VALL-E), which follows the structure of the generative pretrained transformer 3 (GPT-3). The proposed SC VALL-E takes input from text sentences and prompt audio and is designed to generate controllable speech by not simply mimicking the characteristics of the prompt audio but by controlling the attributes to produce diverse voices. We identify tokens in the style embedding matrix of the newly designed style network that represent attributes such as emotion, speaking rate, pitch, and voice intensity, and design a model that can control these attributes. To evaluate the performance of SC VALL-E, we conduct comparative experiments with three representative expressive speech synthesis models: global style token (GST) Tacotron2, variational autoencoder (VAE) Tacotron2, and original VALL-E. We measure word error rate (WER), F0 voiced error (FVE), and F0 gross pitch error (F0GPE) as evaluation metrics to assess the accuracy of generated sentences. For comparing the quality of synthesized speech, we measure comparative mean option score (CMOS) and similarity mean option score (SMOS). To evaluate the style control ability of the generated speech, we observe the changes in F0 and mel-spectrogram by modifying the trained tokens. When using prompt audio that is not present in the training data, SC VALL-E generates a variety of expressive sounds and demonstrates competitive performance compared to the existing models. Our implementation, pretrained models, and audio samples are located on GitHub.
    Forecasting Battery Electric Vehicle Charging Behavior: A Deep Learning Approach Equipped with Micro-Clustering and SMOTE Techniques. (arXiv:2307.10588v1 [cs.LG])
    Energy systems, climate change, and public health are among the primary reasons for moving toward electrification in transportation. Transportation electrification is being promoted worldwide to reduce emissions. As a result, many automakers will soon start making only battery electric vehicles (BEVs). BEV adoption rates are rising in California, mainly due to climate change and air pollution concerns. While great for climate and pollution goals, improperly managed BEV charging can lead to insufficient charging infrastructure and power outages. This study develops a novel Micro Clustering Deep Neural Network (MCDNN), an artificial neural network algorithm that is highly effective at learning BEVs trip and charging data to forecast BEV charging events, information that is essential for electricity load aggregators and utility managers to provide charging stations and electricity capacity effectively. The MCDNN is configured using a robust dataset of trips and charges that occurred in California between 2015 and 2020 from 132 BEVs, spanning 5 BEV models for a total of 1570167 vehicle miles traveled. The numerical findings revealed that the proposed MCDNN is more effective than benchmark approaches in this field, such as support vector machine, k nearest neighbors, decision tree, and other neural network-based models in predicting the charging events.
    Fairness-Aware Client Selection for Federated Learning. (arXiv:2307.10738v1 [cs.LG])
    Federated learning (FL) has enabled multiple data owners (a.k.a. FL clients) to train machine learning models collaboratively without revealing private data. Since the FL server can only engage a limited number of clients in each training round, FL client selection has become an important research problem. Existing approaches generally focus on either enhancing FL model performance or enhancing the fair treatment of FL clients. The problem of balancing performance and fairness considerations when selecting FL clients remains open. To address this problem, we propose the Fairness-aware Federated Client Selection (FairFedCS) approach. Based on Lyapunov optimization, it dynamically adjusts FL clients' selection probabilities by jointly considering their reputations, times of participation in FL tasks and contributions to the resulting model performance. By not using threshold-based reputation filtering, it provides FL clients with opportunities to redeem their reputations after a perceived poor performance, thereby further enhancing fair client treatment. Extensive experiments based on real-world multimedia datasets show that FairFedCS achieves 19.6% higher fairness and 0.73% higher test accuracy on average than the best-performing state-of-the-art approach.
    Music Genre Classification with ResNet and Bi-GRU Using Visual Spectrograms. (arXiv:2307.10773v1 [cs.SD])
    Music recommendation systems have emerged as a vital component to enhance user experience and satisfaction for the music streaming services, which dominates music consumption. The key challenge in improving these recommender systems lies in comprehending the complexity of music data, specifically for the underpinning music genre classification. The limitations of manual genre classification have highlighted the need for a more advanced system, namely the Automatic Music Genre Classification (AMGC) system. While traditional machine learning techniques have shown potential in genre classification, they heavily rely on manually engineered features and feature selection, failing to capture the full complexity of music data. On the other hand, deep learning classification architectures like the traditional Convolutional Neural Networks (CNN) are effective in capturing the spatial hierarchies but struggle to capture the temporal dynamics inherent in music data. To address these challenges, this study proposes a novel approach using visual spectrograms as input, and propose a hybrid model that combines the strength of the Residual neural Network (ResNet) and the Gated Recurrent Unit (GRU). This model is designed to provide a more comprehensive analysis of music data, offering the potential to improve the music recommender systems through achieving a more comprehensive analysis of music data and hence potentially more accurate genre classification.
    Multi-Method Self-Training: Improving Code Generation With Text, And Vice Versa. (arXiv:2307.10633v1 [cs.CL])
    Large Language Models have many methods for solving the same problem. This introduces novel strengths (different methods may work well for different problems) and weaknesses (it may be difficult for users to know which method to use). In this paper, we introduce Multi-Method Self-Training (MMST), where one method is trained on the filtered outputs of another, allowing us to augment the strengths and ameliorate the weaknesses of each method. Using a 176B parameter model trained on both language and code, we show that MMST can 1) improve the less performant method (up to 30%) making the model easier to use, 2) improve the more performant method (up to 32.2%) making the model more performant, and 3) improve the performance of related but distinct tasks (up to 10.3%) by improving the ability of the model to generate rationales. We then conduct ablation analyses to explore why MMST works. We show that MMST generates more data than traditional self-training, but the improvement in performance is driven by the use of multiple methods. We also analyze prompt-engineering and anti-correlated performance between methods as means of making MMST more effective. We hope the evidence from our paper motivates machine learning researchers to explore ways in which advances in language models allow for new forms of training.
    SecureBoost Hyperparameter Tuning via Multi-Objective Federated Learning. (arXiv:2307.10579v1 [cs.LG])
    SecureBoost is a tree-boosting algorithm leveraging homomorphic encryption to protect data privacy in vertical federated learning setting. It is widely used in fields such as finance and healthcare due to its interpretability, effectiveness, and privacy-preserving capability. However, SecureBoost suffers from high computational complexity and risk of label leakage. To harness the full potential of SecureBoost, hyperparameters of SecureBoost should be carefully chosen to strike an optimal balance between utility, efficiency, and privacy. Existing methods either set hyperparameters empirically or heuristically, which are far from optimal. To fill this gap, we propose a Constrained Multi-Objective SecureBoost (CMOSB) algorithm to find Pareto optimal solutions that each solution is a set of hyperparameters achieving optimal tradeoff between utility loss, training cost, and privacy leakage. We design measurements of the three objectives. In particular, the privacy leakage is measured using our proposed instance clustering attack. Experimental results demonstrate that the CMOSB yields not only hyperparameters superior to the baseline but also optimal sets of hyperparameters that can support the flexible requirements of FL participants.
    Meta-Transformer: A Unified Framework for Multimodal Learning. (arXiv:2307.10802v1 [cs.CV])
    Multimodal learning aims to build models that can process and relate information from multiple modalities. Despite years of development in this field, it still remains challenging to design a unified network for processing various modalities ($\textit{e.g.}$ natural language, 2D images, 3D point clouds, audio, video, time series, tabular data) due to the inherent gaps among them. In this work, we propose a framework, named Meta-Transformer, that leverages a $\textbf{frozen}$ encoder to perform multimodal perception without any paired multimodal training data. In Meta-Transformer, the raw input data from various modalities are mapped into a shared token space, allowing a subsequent encoder with frozen parameters to extract high-level semantic features of the input data. Composed of three main components: a unified data tokenizer, a modality-shared encoder, and task-specific heads for downstream tasks, Meta-Transformer is the first framework to perform unified learning across 12 modalities with unpaired data. Experiments on different benchmarks reveal that Meta-Transformer can handle a wide range of tasks including fundamental perception (text, image, point cloud, audio, video), practical application (X-Ray, infrared, hyperspectral, and IMU), and data mining (graph, tabular, and time-series). Meta-Transformer indicates a promising future for developing unified multimodal intelligence with transformers. Code will be available at https://github.com/invictus717/MetaTransformer
    Ensemble Learning based Anomaly Detection for IoT Cybersecurity via Bayesian Hyperparameters Sensitivity Analysis. (arXiv:2307.10596v1 [cs.LG])
    The Internet of Things (IoT) integrates more than billions of intelligent devices over the globe with the capability of communicating with other connected devices with little to no human intervention. IoT enables data aggregation and analysis on a large scale to improve life quality in many domains. In particular, data collected by IoT contain a tremendous amount of information for anomaly detection. The heterogeneous nature of IoT is both a challenge and an opportunity for cybersecurity. Traditional approaches in cybersecurity monitoring often require different kinds of data pre-processing and handling for various data types, which might be problematic for datasets that contain heterogeneous features. However, heterogeneous types of network devices can often capture a more diverse set of signals than a single type of device readings, which is particularly useful for anomaly detection. In this paper, we present a comprehensive study on using ensemble machine learning methods for enhancing IoT cybersecurity via anomaly detection. Rather than using one single machine learning model, ensemble learning combines the predictive power from multiple models, enhancing their predictive accuracy in heterogeneous datasets rather than using one single machine learning model. We propose a unified framework with ensemble learning that utilises Bayesian hyperparameter optimisation to adapt to a network environment that contains multiple IoT sensor readings. Experimentally, we illustrate their high predictive power when compared to traditional methods.
    Feed-Forward Source-Free Domain Adaptation via Class Prototypes. (arXiv:2307.10787v1 [cs.CV])
    Source-free domain adaptation has become popular because of its practical usefulness and no need to access source data. However, the adaptation process still takes a considerable amount of time and is predominantly based on optimization that relies on back-propagation. In this work we present a simple feed-forward approach that challenges the need for back-propagation based adaptation. Our approach is based on computing prototypes of classes under the domain shift using a pre-trained model. It achieves strong improvements in accuracy compared to the pre-trained model and requires only a small fraction of time of existing domain adaptation methods.
    FedBug: A Bottom-Up Gradual Unfreezing Framework for Federated Learning. (arXiv:2307.10317v1 [cs.LG])
    Federated Learning (FL) offers a collaborative training framework, allowing multiple clients to contribute to a shared model without compromising data privacy. Due to the heterogeneous nature of local datasets, updated client models may overfit and diverge from one another, commonly known as the problem of client drift. In this paper, we propose FedBug (Federated Learning with Bottom-Up Gradual Unfreezing), a novel FL framework designed to effectively mitigate client drift. FedBug adaptively leverages the client model parameters, distributed by the server at each global round, as the reference points for cross-client alignment. Specifically, on the client side, FedBug begins by freezing the entire model, then gradually unfreezes the layers, from the input layer to the output layer. This bottom-up approach allows models to train the newly thawed layers to project data into a latent space, wherein the separating hyperplanes remain consistent across all clients. We theoretically analyze FedBug in a novel over-parameterization FL setup, revealing its superior convergence rate compared to FedAvg. Through comprehensive experiments, spanning various datasets, training conditions, and network architectures, we validate the efficacy of FedBug. Our contributions encompass a novel FL framework, theoretical analysis, and empirical validation, demonstrating the wide potential and applicability of FedBug.
    MSQNet: Actor-agnostic Action Recognition with Multi-modal Query. (arXiv:2307.10763v1 [cs.CV])
    Existing action recognition methods are typically actor-specific due to the intrinsic topological and apparent differences among the actors. This requires actor-specific pose estimation (e.g., humans vs. animals), leading to cumbersome model design complexity and high maintenance costs. Moreover, they often focus on learning the visual modality alone and single-label classification whilst neglecting other available information sources (e.g., class name text) and the concurrent occurrence of multiple actions. To overcome these limitations, we propose a new approach called 'actor-agnostic multi-modal multi-label action recognition,' which offers a unified solution for various types of actors, including humans and animals. We further formulate a novel Multi-modal Semantic Query Network (MSQNet) model in a transformer-based object detection framework (e.g., DETR), characterized by leveraging visual and textual modalities to represent the action classes better. The elimination of actor-specific model designs is a key advantage, as it removes the need for actor pose estimation altogether. Extensive experiments on five publicly available benchmarks show that our MSQNet consistently outperforms the prior arts of actor-specific alternatives on human and animal single- and multi-label action recognition tasks by up to 50%. Code will be released at https://github.com/mondalanindya/MSQNet.
    A Dual Stealthy Backdoor: From Both Spatial and Frequency Perspectives. (arXiv:2307.10184v1 [cs.CR])
    Backdoor attacks pose serious security threats to deep neural networks (DNNs). Backdoored models make arbitrarily (targeted) incorrect predictions on inputs embedded with well-designed triggers while behaving normally on clean inputs. Many works have explored the invisibility of backdoor triggers to improve attack stealthiness. However, most of them only consider the invisibility in the spatial domain without explicitly accounting for the generation of invisible triggers in the frequency domain, making the generated poisoned images be easily detected by recent defense methods. To address this issue, in this paper, we propose a DUal stealthy BAckdoor attack method named DUBA, which simultaneously considers the invisibility of triggers in both the spatial and frequency domains, to achieve desirable attack performance, while ensuring strong stealthiness. Specifically, we first use Discrete Wavelet Transform to embed the high-frequency information of the trigger image into the clean image to ensure attack effectiveness. Then, to attain strong stealthiness, we incorporate Fourier Transform and Discrete Cosine Transform to mix the poisoned image and clean image in the frequency domain. Moreover, the proposed DUBA adopts a novel attack strategy, in which the model is trained with weak triggers and attacked with strong triggers to further enhance the attack performance and stealthiness. We extensively evaluate DUBA against popular image classifiers on four datasets. The results demonstrate that it significantly outperforms the state-of-the-art backdoor attacks in terms of the attack success rate and stealthiness
    Divide & Bind Your Attention for Improved Generative Semantic Nursing. (arXiv:2307.10864v1 [cs.CV])
    Emerging large-scale text-to-image generative models, e.g., Stable Diffusion (SD), have exhibited overwhelming results with high fidelity. Despite the magnificent progress, current state-of-the-art models still struggle to generate images fully adhering to the input prompt. Prior work, Attend & Excite, has introduced the concept of Generative Semantic Nursing (GSN), aiming to optimize cross-attention during inference time to better incorporate the semantics. It demonstrates promising results in generating simple prompts, e.g., ``a cat and a dog''. However, its efficacy declines when dealing with more complex prompts, and it does not explicitly address the problem of improper attribute binding. To address the challenges posed by complex prompts or scenarios involving multiple entities and to achieve improved attribute binding, we propose Divide & Bind. We introduce two novel loss objectives for GSN: a novel attendance loss and a binding loss. Our approach stands out in its ability to faithfully synthesize desired objects with improved attribute alignment from complex prompts and exhibits superior performance across multiple evaluation benchmarks. More videos and updates can be found on the project page \url{https://sites.google.com/view/divide-and-bind}.
    Identifying Interpretable Subspaces in Image Representations. (arXiv:2307.10504v1 [cs.CV])
    We propose Automatic Feature Explanation using Contrasting Concepts (FALCON), an interpretability framework to explain features of image representations. For a target feature, FALCON captions its highly activating cropped images using a large captioning dataset (like LAION-400m) and a pre-trained vision-language model like CLIP. Each word among the captions is scored and ranked leading to a small number of shared, human-understandable concepts that closely describe the target feature. FALCON also applies contrastive interpretation using lowly activating (counterfactual) images, to eliminate spurious concepts. Although many existing approaches interpret features independently, we observe in state-of-the-art self-supervised and supervised models, that less than 20% of the representation space can be explained by individual features. We show that features in larger spaces become more interpretable when studied in groups and can be explained with high-order scoring concepts through FALCON. We discuss how extracted concepts can be used to explain and debug failures in downstream tasks. Finally, we present a technique to transfer concepts from one (explainable) representation space to another unseen representation space by learning a simple linear transformation.
    A Matrix Ensemble Kalman Filter-based Multi-arm Neural Network to Adequately Approximate Deep Neural Networks. (arXiv:2307.10436v1 [stat.ML])
    Deep Learners (DLs) are the state-of-art predictive mechanism with applications in many fields requiring complex high dimensional data processing. Although conventional DLs get trained via gradient descent with back-propagation, Kalman Filter (KF)-based techniques that do not need gradient computation have been developed to approximate DLs. We propose a multi-arm extension of a KF-based DL approximator that can mimic DL when the sample size is too small to train a multi-arm DL. The proposed Matrix Ensemble Kalman Filter-based multi-arm ANN (MEnKF-ANN) also performs explicit model stacking that becomes relevant when the training sample has an unequal-size feature set. Our proposed technique can approximate Long Short-term Memory (LSTM) Networks and attach uncertainty to the predictions obtained from these LSTMs with desirable coverage. We demonstrate how MEnKF-ANN can "adequately" approximate an LSTM network trained to classify what carbohydrate substrates are digested and utilized by a microbiome sample whose genomic sequences consist of polysaccharide utilization loci (PULs) and their encoded genes.
    Shared Adversarial Unlearning: Backdoor Mitigation by Unlearning Shared Adversarial Examples. (arXiv:2307.10562v1 [cs.LG])
    Backdoor attacks are serious security threats to machine learning models where an adversary can inject poisoned samples into the training set, causing a backdoored model which predicts poisoned samples with particular triggers to particular target classes, while behaving normally on benign samples. In this paper, we explore the task of purifying a backdoored model using a small clean dataset. By establishing the connection between backdoor risk and adversarial risk, we derive a novel upper bound for backdoor risk, which mainly captures the risk on the shared adversarial examples (SAEs) between the backdoored model and the purified model. This upper bound further suggests a novel bi-level optimization problem for mitigating backdoor using adversarial training techniques. To solve it, we propose Shared Adversarial Unlearning (SAU). Specifically, SAU first generates SAEs, and then, unlearns the generated SAEs such that they are either correctly classified by the purified model and/or differently classified by the two models, such that the backdoor effect in the backdoored model will be mitigated in the purified model. Experiments on various benchmark datasets and network architectures show that our proposed method achieves state-of-the-art performance for backdoor defense.
    Mood Classification of Bangla Songs Based on Lyrics. (arXiv:2307.10314v1 [cs.IR])
    Music can evoke various emotions, and with the advancement of technology, it has become more accessible to people. Bangla music, which portrays different human emotions, lacks sufficient research. The authors of this article aim to analyze Bangla songs and classify their moods based on the lyrics. To achieve this, this research has compiled a dataset of 4000 Bangla song lyrics, genres, and used Natural Language Processing and the Bert Algorithm to analyze the data. Among the 4000 songs, 1513 songs are represented for the sad mood, 1362 for the romantic mood, 886 for happiness, and the rest 239 are classified as relaxation. By embedding the lyrics of the songs, the authors have classified the songs into four moods: Happy, Sad, Romantic, and Relaxed. This research is crucial as it enables a multi-class classification of songs' moods, making the music more relatable to people's emotions. The article presents the automated result of the four moods accurately derived from the song lyrics.
    Beyond Black-Box Advice: Learning-Augmented Algorithms for MDPs with Q-Value Predictions. (arXiv:2307.10524v1 [cs.LG])
    We study the tradeoff between consistency and robustness in the context of a single-trajectory time-varying Markov Decision Process (MDP) with untrusted machine-learned advice. Our work departs from the typical approach of treating advice as coming from black-box sources by instead considering a setting where additional information about how the advice is generated is available. We prove a first-of-its-kind consistency and robustness tradeoff given Q-value advice under a general MDP model that includes both continuous and discrete state/action spaces. Our results highlight that utilizing Q-value advice enables dynamic pursuit of the better of machine-learned advice and a robust baseline, thus result in near-optimal performance guarantees, which provably improves what can be obtained solely with black-box advice.
    Classification of Visualization Types and Perspectives in Patents. (arXiv:2307.10471v1 [cs.CV])
    Due to the swift growth of patent applications each year, information and multimedia retrieval approaches that facilitate patent exploration and retrieval are of utmost importance. Different types of visualizations (e.g., graphs, technical drawings) and perspectives (e.g., side view, perspective) are used to visualize details of innovations in patents. The classification of these images enables a more efficient search and allows for further analysis. So far, datasets for image type classification miss some important visualization types for patents. Furthermore, related work does not make use of recent deep learning approaches including transformers. In this paper, we adopt state-of-the-art deep learning methods for the classification of visualization types and perspectives in patent images. We extend the CLEF-IP dataset for image type classification in patents to ten classes and provide manual ground truth annotations. In addition, we derive a set of hierarchical classes from a dataset that provides weakly-labeled data for image perspectives. Experimental results have demonstrated the feasibility of the proposed approaches. Source code, models, and dataset will be made publicly available.
    Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey). (arXiv:2307.10246v1 [q-bio.NC])
    How does the brain represent different modes of information? Can we design a system that automatically understands what the user is thinking? Such questions can be answered by studying brain recordings like functional magnetic resonance imaging (fMRI). As a first step, the neuroscience community has contributed several large cognitive neuroscience datasets related to passive reading/listening/viewing of concept words, narratives, pictures and movies. Encoding and decoding models using these datasets have also been proposed in the past two decades. These models serve as additional tools for basic research in cognitive science and neuroscience. Encoding models aim at generating fMRI brain representations given a stimulus automatically. They have several practical applications in evaluating and diagnosing neurological conditions and thus also help design therapies for brain damage. Decoding models solve the inverse problem of reconstructing the stimuli given the fMRI. They are useful for designing brain-machine or brain-computer interfaces. Inspired by the effectiveness of deep learning models for natural language processing, computer vision, and speech, recently several neural encoding and decoding models have been proposed. In this survey, we will first discuss popular representations of language, vision and speech stimuli, and present a summary of neuroscience datasets. Further, we will review popular deep learning based encoding and decoding architectures and note their benefits and limitations. Finally, we will conclude with a brief summary and discussion about future trends. Given the large amount of recently published work in the `computational cognitive neuroscience' community, we believe that this survey nicely organizes the plethora of work and presents it as a coherent story.
    Long-Tail Theory under Gaussian Mixtures. (arXiv:2307.10736v1 [cs.LG])
    We suggest a simple Gaussian mixture model for data generation that complies with Feldman's long tail theory (2020). We demonstrate that a linear classifier cannot decrease the generalization error below a certain level in the proposed model, whereas a nonlinear classifier with a memorization capacity can. This confirms that for long-tailed distributions, rare training examples must be considered for optimal generalization to new data. Finally, we show that the performance gap between linear and nonlinear models can be lessened as the tail becomes shorter in the subpopulation frequency distribution, as confirmed by experiments on synthetic and real data.
    Global Precipitation Nowcasting of Integrated Multi-satellitE Retrievals for GPM: A U-Net Convolutional LSTM Architecture. (arXiv:2307.10843v1 [cs.LG])
    This paper presents a deep learning architecture for nowcasting of precipitation almost globally every 30 min with a 4-hour lead time. The architecture fuses a U-Net and a convolutional long short-term memory (LSTM) neural network and is trained using data from the Integrated MultisatellitE Retrievals for GPM (IMERG) and a few key precipitation drivers from the Global Forecast System (GFS). The impacts of different training loss functions, including the mean-squared error (regression) and the focal-loss (classification), on the quality of precipitation nowcasts are studied. The results indicate that the regression network performs well in capturing light precipitation (below 1.6 mm/hr), but the classification network can outperform the regression network for nowcasting of precipitation extremes (>8 mm/hr), in terms of the critical success index (CSI).. Using the Wasserstein distance, it is shown that the predicted precipitation by the classification network has a closer class probability distribution to the IMERG than the regression network. It is uncovered that the inclusion of the physical variables can improve precipitation nowcasting, especially at longer lead times in both networks. Taking IMERG as a relative reference, a multi-scale analysis in terms of fractions skill score (FSS), shows that the nowcasting machine remains skillful (FSS > 0.5) at the resolution of 10 km compared to 50 km for GFS. For precipitation rates greater than 4~mm/hr, only the classification network remains FSS-skillful on scales greater than 50 km within a 2-hour lead time.
    Player-optimal Stable Regret for Bandit Learning in Matching Markets. (arXiv:2307.10890v1 [cs.LG])
    The problem of matching markets has been studied for a long time in the literature due to its wide range of applications. Finding a stable matching is a common equilibrium objective in this problem. Since market participants are usually uncertain of their preferences, a rich line of recent works study the online setting where one-side participants (players) learn their unknown preferences from iterative interactions with the other side (arms). Most previous works in this line are only able to derive theoretical guarantees for player-pessimal stable regret, which is defined compared with the players' least-preferred stable matching. However, under the pessimal stable matching, players only obtain the least reward among all stable matchings. To maximize players' profits, player-optimal stable matching would be the most desirable. Though \citet{basu21beyond} successfully bring an upper bound for player-optimal stable regret, their result can be exponentially large if players' preference gap is small. Whether a polynomial guarantee for this regret exists is a significant but still open problem. In this work, we provide a new algorithm named explore-then-Gale-Shapley (ETGS) and show that the optimal stable regret of each player can be upper bounded by $O(K\log T/\Delta^2)$ where $K$ is the number of arms, $T$ is the horizon and $\Delta$ is the players' minimum preference gap among the first $N+1$-ranked arms. This result significantly improves previous works which either have a weaker player-pessimal stable matching objective or apply only to markets with special assumptions. When the preferences of participants satisfy some special conditions, our regret upper bound also matches the previously derived lower bound.
    Neural Network Complexity of Chaos and Turbulence. (arXiv:2211.15382v2 [cs.LG] UPDATED)
    Chaos and turbulence are complex physical phenomena, yet a precise definition of the complexity measure that quantifies them is still lacking. In this work we consider the relative complexity of chaos and turbulence from the perspective of deep neural networks. We analyze a set of classification problems, where the network has to distinguish images of fluid profiles in the turbulent regime from other classes of images such as fluid profiles in the chaotic regime, various constructions of noise and real world images. We analyze incompressible as well as weakly compressible fluid flows. We quantify the complexity of the computation performed by the network via the intrinsic dimensionality of the internal feature representations, and calculate the effective number of independent features which the network uses in order to distinguish between classes. In addition to providing a numerical estimate of the complexity of the computation, the measure also characterizes the neural network processing at intermediate and final stages. We construct adversarial examples and use them to identify the two point correlation spectra for the chaotic and turbulent vorticity as the feature used by the network for classification.
    Decoding the Enigma: Benchmarking Humans and AIs on the Many Facets of Working Memory. (arXiv:2307.10768v1 [q-bio.NC])
    Working memory (WM), a fundamental cognitive process facilitating the temporary storage, integration, manipulation, and retrieval of information, plays a vital role in reasoning and decision-making tasks. Robust benchmark datasets that capture the multifaceted nature of WM are crucial for the effective development and evaluation of AI WM models. Here, we introduce a comprehensive Working Memory (WorM) benchmark dataset for this purpose. WorM comprises 10 tasks and a total of 1 million trials, assessing 4 functionalities, 3 domains, and 11 behavioral and neural characteristics of WM. We jointly trained and tested state-of-the-art recurrent neural networks and transformers on all these tasks. We also include human behavioral benchmarks as an upper bound for comparison. Our results suggest that AI models replicate some characteristics of WM in the brain, most notably primacy and recency effects, and neural clusters and correlates specialized for different domains and functionalities of WM. In the experiments, we also reveal some limitations in existing models to approximate human behavior. This dataset serves as a valuable resource for communities in cognitive psychology, neuroscience, and AI, offering a standardized framework to compare and enhance WM models, investigate WM's neural underpinnings, and develop WM models with human-like capabilities. Our source code and data are available at https://github.com/ZhangLab-DeepNeuroCogLab/WorM.
    Adversarial attacks for mixtures of classifiers. (arXiv:2307.10788v1 [cs.LG])
    Mixtures of classifiers (a.k.a. randomized ensembles) have been proposed as a way to improve robustness against adversarial attacks. However, it has been shown that existing attacks are not well suited for this kind of classifiers. In this paper, we discuss the problem of attacking a mixture in a principled way and introduce two desirable properties of attacks based on a geometrical analysis of the problem (effectiveness and maximality). We then show that existing attacks do not meet both of these properties. Finally, we introduce a new attack called lattice climber attack with theoretical guarantees on the binary linear setting, and we demonstrate its performance by conducting experiments on synthetic and real datasets.
    Bayesian Spike Train Inference via Non-Local Priors. (arXiv:2307.10177v1 [q-bio.NC])
    Advances in neuroscience have enabled researchers to measure the activities of large numbers of neurons simultaneously in behaving animals. We have access to the fluorescence of each of the neurons which provides a first-order approximation of the neural activity over time. Determining the exact spike of a neuron from this fluorescence trace constitutes an active area of research within the field of computational neuroscience. We propose a novel Bayesian approach based on a mixture of half-non-local prior densities and point masses for this task. Instead of a computationally expensive MCMC algorithm, we adopt a stochastic search-based approach that is capable of taking advantage of modern computing environments often equipped with multiple processors, to explore all possible arrangements of spikes and lack thereof in an observed spike train. It then reports the highest posterior probability arrangement of spikes and posterior probability for a spike at each location of the spike train. Our proposals lead to substantial improvements over existing proposals based on L1 regularization, and enjoy comparable estimation accuracy to the state-of-the-art L0 proposal, in simulations, and on recent calcium imaging data sets. Notably, contrary to optimization-based frequentist approaches, our methodology yields automatic uncertainty quantification associated with the spike-train inference.
    SentimentGPT: Exploiting GPT for Advanced Sentiment Analysis and its Departure from Current Machine Learning. (arXiv:2307.10234v1 [cs.CL])
    This study presents a thorough examination of various Generative Pretrained Transformer (GPT) methodologies in sentiment analysis, specifically in the context of Task 4 on the SemEval 2017 dataset. Three primary strategies are employed: 1) prompt engineering using the advanced GPT-3.5 Turbo, 2) fine-tuning GPT models, and 3) an inventive approach to embedding classification. The research yields detailed comparative insights among these strategies and individual GPT models, revealing their unique strengths and potential limitations. Additionally, the study compares these GPT-based methodologies with other contemporary, high-performing models previously used with the same dataset. The results illustrate the significant superiority of the GPT approaches in terms of predictive performance, more than 22% in F1-score compared to the state-of-the-art. Further, the paper addresses common challenges in sentiment analysis tasks, such as understanding context and detecting sarcasm. It underscores the enhanced capabilities of the GPT models to effectively navigate these complexities. Collectively, these findings highlight the promising potential of GPT models in sentiment analysis, setting the stage for future research in this field. The code can be found at https://github.com/DSAatUSU/SentimentGPT.
    Code Detection for Hardware Acceleration Using Large Language Models. (arXiv:2307.10348v1 [cs.SE])
    Large language models (LLMs) have been massively applied to many tasks, often surpassing state-of-the-art approaches. While their effectiveness in code generation has been extensively studied (e.g., AlphaCode), their potential for code detection remains unexplored. This work presents the first analysis of code detection using LLMs. Our study examines essential kernels, including matrix multiplication, convolution, and fast-fourier transform, implemented in C/C++. We propose both a preliminary, naive prompt and a novel prompting strategy for code detection. Results reveal that conventional prompting achieves great precision but poor accuracy (68.8%, 22.3%, and 79.2% for GEMM, convolution, and FFT, respectively) due to a high number of false positives. Our novel prompting strategy substantially reduces false positives, resulting in excellent overall accuracy (91.1%, 97.9%, and 99.7%, respectively). These results pose a considerable challenge to existing state-of-the-art code detection methods.
    Tapestry of Time and Actions: Modeling Human Activity Sequences using Temporal Point Process Flows. (arXiv:2307.10305v1 [cs.CV])
    Human beings always engage in a vast range of activities and tasks that demonstrate their ability to adapt to different scenarios. Any human activity can be represented as a temporal sequence of actions performed to achieve a certain goal. Unlike the time series datasets extracted from electronics or machines, these action sequences are highly disparate in their nature -- the time to finish a sequence of actions can vary between different persons. Therefore, understanding the dynamics of these sequences is essential for many downstream tasks such as activity length prediction, goal prediction, next action recommendation, etc. Existing neural network-based approaches that learn a continuous-time activity sequence (or CTAS) are limited to the presence of only visual data or are designed specifically for a particular task, i.e., limited to next action or goal prediction. In this paper, we present ProActive, a neural marked temporal point process (MTPP) framework for modeling the continuous-time distribution of actions in an activity sequence while simultaneously addressing three high-impact problems -- next action prediction, sequence-goal prediction, and end-to-end sequence generation. Specifically, we utilize a self-attention module with temporal normalizing flows to model the influence and the inter-arrival times between actions in a sequence. In addition, we propose a novel addition over the ProActive model that can handle variations in the order of actions, i.e., different methods of achieving a given goal. We demonstrate that this variant can learn the order in which the person or actor prefers to do their actions. Extensive experiments on sequences derived from three activity recognition datasets show the significant accuracy boost of ProActive over the state-of-the-art in terms of action and goal prediction, and the first-ever application of end-to-end action sequence generation.
    Fisher-Rao distance and pullback SPD cone distances between multivariate normal distributions. (arXiv:2307.10644v1 [cs.LG])
    Data sets of multivariate normal distributions abound in many scientific areas like diffusion tensor imaging, structure tensor computer vision, radar signal processing, machine learning, just to name a few. In order to process those normal data sets for downstream tasks like filtering, classification or clustering, one needs to define proper notions of dissimilarities between normals and paths joining them. The Fisher-Rao distance defined as the Riemannian geodesic distance induced by the Fisher information metric is such a principled metric distance which however is not known in closed-form excepts for a few particular cases. In this work, we first report a fast and robust method to approximate arbitrarily finely the Fisher-Rao distance between multivariate normal distributions. Second, we introduce a class of distances based on diffeomorphic embeddings of the normal manifold into a submanifold of the higher-dimensional symmetric positive-definite cone corresponding to the manifold of centered normal distributions. We show that the projective Hilbert distance on the cone yields a metric on the embedded normal submanifold and we pullback that cone distance with its associated straight line Hilbert cone geodesics to obtain a distance and smooth paths between normal distributions. Compared to the Fisher-Rao distance approximation, the pullback Hilbert cone distance is computationally light since it requires to compute only the extreme minimal and maximal eigenvalues of matrices. Finally, we show how to use those distances in clustering tasks.
    PreDiff: Precipitation Nowcasting with Latent Diffusion Models. (arXiv:2307.10422v1 [cs.LG])
    Earth system forecasting has traditionally relied on complex physical models that are computationally expensive and require significant domain expertise. In the past decade, the unprecedented increase in spatiotemporal Earth observation data has enabled data-driven forecasting models using deep learning techniques. These models have shown promise for diverse Earth system forecasting tasks but either struggle with handling uncertainty or neglect domain-specific prior knowledge, resulting in averaging possible futures to blurred forecasts or generating physically implausible predictions. To address these limitations, we propose a two-stage pipeline for probabilistic spatiotemporal forecasting: 1) We develop PreDiff, a conditional latent diffusion model capable of probabilistic forecasts. 2) We incorporate an explicit knowledge control mechanism to align forecasts with domain-specific physical constraints. This is achieved by estimating the deviation from imposed constraints at each denoising step and adjusting the transition distribution accordingly. We conduct empirical studies on two datasets: N-body MNIST, a synthetic dataset with chaotic behavior, and SEVIR, a real-world precipitation nowcasting dataset. Specifically, we impose the law of conservation of energy in N-body MNIST and anticipated precipitation intensity in SEVIR. Experiments demonstrate the effectiveness of PreDiff in handling uncertainty, incorporating domain-specific prior knowledge, and generating forecasts that exhibit high operational utility.
    Hidden Markov Models with Random Restarts vs Boosting for Malware Detection. (arXiv:2307.10256v1 [cs.CR])
    Effective and efficient malware detection is at the forefront of research into building secure digital systems. As with many other fields, malware detection research has seen a dramatic increase in the application of machine learning algorithms. One machine learning technique that has been used widely in the field of pattern matching in general-and malware detection in particular-is hidden Markov models (HMMs). HMM training is based on a hill climb, and hence we can often improve a model by training multiple times with different initial values. In this research, we compare boosted HMMs (using AdaBoost) to HMMs trained with multiple random restarts, in the context of malware detection. These techniques are applied to a variety of challenging malware datasets. We find that random restarts perform surprisingly well in comparison to boosting. Only in the most difficult "cold start" cases (where training data is severely limited) does boosting appear to offer sufficient improvement to justify its higher computational cost in the scoring phase.
    Student Assessment in Cybersecurity Training Automated by Pattern Mining and Clustering. (arXiv:2307.10260v1 [cs.CR])
    Hands-on cybersecurity training allows students and professionals to practice various tools and improve their technical skills. The training occurs in an interactive learning environment that enables completing sophisticated tasks in full-fledged operating systems, networks, and applications. During the training, the learning environment allows collecting data about trainees' interactions with the environment, such as their usage of command-line tools. These data contain patterns indicative of trainees' learning processes, and revealing them allows to assess the trainees and provide feedback to help them learn. However, automated analysis of these data is challenging. The training tasks feature complex problem-solving, and many different solution approaches are possible. Moreover, the trainees generate vast amounts of interaction data. This paper explores a dataset from 18 cybersecurity training sessions using data mining and machine learning techniques. We employed pattern mining and clustering to analyze 8834 commands collected from 113 trainees, revealing their typical behavior, mistakes, solution strategies, and difficult training stages. Pattern mining proved suitable in capturing timing information and tool usage frequency. Clustering underlined that many trainees often face the same issues, which can be addressed by targeted scaffolding. Our results show that data mining methods are suitable for analyzing cybersecurity training data. Educational researchers and practitioners can apply these methods in their contexts to assess trainees, support them, and improve the training design. Artifacts associated with this research are publicly available.
    Improving Multimodal Datasets with Image Captioning. (arXiv:2307.10350v1 [cs.LG])
    Massive web datasets play a key role in the success of large vision-language models like CLIP and Flamingo. However, the raw web data is noisy, and existing filtering methods to reduce noise often come at the expense of data diversity. Our work focuses on caption quality as one major source of noise, and studies how generated captions can increase the utility of web-scraped datapoints with nondescript text. Through exploring different mixing strategies for raw and generated captions, we outperform the best filtering method proposed by the DataComp benchmark by 2% on ImageNet and 4% on average across 38 tasks, given a candidate pool of 128M image-text pairs. Our best approach is also 2x better at Flickr and MS-COCO retrieval. We then analyze what makes synthetic captions an effective source of text supervision. In experimenting with different image captioning models, we also demonstrate that the performance of a model on standard image captioning benchmarks (e.g., NoCaps CIDEr) is not a reliable indicator of the utility of the captions it generates for multimodal training. Finally, our experiments with using generated captions at DataComp's large scale (1.28B image-text pairs) offer insights into the limitations of synthetic text, as well as the importance of image curation with increasing training data quantity.
    Privacy Amplification via Importance Sampling. (arXiv:2307.10187v1 [cs.CR])
    We examine the privacy-enhancing properties of subsampling a data set via importance sampling as a pre-processing step for differentially private mechanisms. This extends the established privacy amplification by subsampling result to importance sampling where each data point is weighted by the reciprocal of its selection probability. The implications for privacy of weighting each point are not obvious. On the one hand, a lower selection probability leads to a stronger privacy amplification. On the other hand, the higher the weight, the stronger the influence of the point on the output of the mechanism in the event that the point does get selected. We provide a general result that quantifies the trade-off between these two effects. We show that heterogeneous sampling probabilities can lead to both stronger privacy and better utility than uniform subsampling while retaining the subsample size. In particular, we formulate and solve the problem of privacy-optimal sampling, that is, finding the importance weights that minimize the expected subset size subject to a given privacy budget. Empirically, we evaluate the privacy, efficiency, and accuracy of importance sampling-based privacy amplification on the example of k-means clustering.
    A data science axiology: the nature, value, and risks of data science. (arXiv:2307.10460v1 [cs.AI])
    Data science is not a science. It is a research paradigm with an unfathomed scope, scale, complexity, and power for knowledge discovery that is not otherwise possible and can be beyond human reasoning. It is changing our world practically and profoundly already widely deployed in tens of thousands of applications in every discipline in an AI Arms Race that, due to its inscrutability, can lead to unfathomed risks. This paper presents an axiology of data science, its purpose, nature, importance, risks, and value for problem solving, by exploring and evaluating its remarkable, definitive features. As data science is in its infancy, this initial, speculative axiology is intended to aid in understanding and defining data science to recognize its potential benefits, risks, and open research challenges. AI based data science is inherently about uncertainty that may be more realistic than our preference for the certainty of science. Data science will have impacts far beyond knowledge discovery and will take us into new ways of understanding the world.
    An IPW-based Unbiased Ranking Metric in Two-sided Markets. (arXiv:2307.10204v1 [cs.IR])
    In modern recommendation systems, unbiased learning-to-rank (LTR) is crucial for prioritizing items from biased implicit user feedback, such as click data. Several techniques, such as Inverse Propensity Weighting (IPW), have been proposed for single-sided markets. However, less attention has been paid to two-sided markets, such as job platforms or dating services, where successful conversions require matching preferences from both users. This paper addresses the complex interaction of biases between users in two-sided markets and proposes a tailored LTR approach. We first present a formulation of feedback mechanisms in two-sided matching platforms and point out that their implicit feedback may include position bias from both user groups. On the basis of this observation, we extend the IPW estimator and propose a new estimator, named two-sided IPW, to address the position bases in two-sided markets. We prove that the proposed estimator satisfies the unbiasedness for the ground-truth ranking metric. We conducted numerical experiments on real-world two-sided platforms and demonstrated the effectiveness of our proposed method in terms of both precision and robustness. Our experiments showed that our method outperformed baselines especially when handling rare items, which are less frequently observed in the training data.
    A New Computationally Simple Approach for Implementing Neural Networks with Output Hard Constraints. (arXiv:2307.10459v1 [cs.LG])
    A new computationally simple method of imposing hard convex constraints on the neural network output values is proposed. The key idea behind the method is to map a vector of hidden parameters of the network to a point that is guaranteed to be inside the feasible set defined by a set of constraints. The mapping is implemented by the additional neural network layer with constraints for output. The proposed method is simply extended to the case when constraints are imposed not only on the output vectors, but also on joint constraints depending on inputs. The projection approach to imposing constraints on outputs can simply be implemented in the framework of the proposed method. It is shown how to incorporate different types of constraints into the proposed method, including linear and quadratic constraints, equality constraints, and dynamic constraints, constraints in the form of boundaries. An important feature of the method is its computational simplicity. Complexities of the forward pass of the proposed neural network layer by linear and quadratic constraints are O(n*m) and O(n^2*m), respectively, where n is the number of variables, m is the number of constraints. Numerical experiments illustrate the method by solving optimization and classification problems. The code implementing the method is publicly available.
    Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search. (arXiv:2307.10438v1 [cs.LG])
    Graph Neural Networks (GNNs) have emerged as a prominent class of data-driven methods for molecular property prediction. However, a key limitation of typical GNN models is their inability to quantify uncertainties in the predictions. This capability is crucial for ensuring the trustworthy use and deployment of models in downstream tasks. To that end, we introduce AutoGNNUQ, an automated uncertainty quantification (UQ) approach for molecular property prediction. AutoGNNUQ leverages architecture search to generate an ensemble of high-performing GNNs, enabling the estimation of predictive uncertainties. Our approach employs variance decomposition to separate data (aleatoric) and model (epistemic) uncertainties, providing valuable insights for reducing them. In our computational experiments, we demonstrate that AutoGNNUQ outperforms existing UQ methods in terms of both prediction accuracy and UQ performance on multiple benchmark datasets. Additionally, we utilize t-SNE visualization to explore correlations between molecular features and uncertainty, offering insight for dataset improvement. AutoGNNUQ has broad applicability in domains such as drug discovery and materials science, where accurate uncertainty quantification is crucial for decision-making.
    Several categories of Large Language Models (LLMs): A Short Survey. (arXiv:2307.10188v1 [cs.CL])
    Large Language Models(LLMs)have become effective tools for natural language processing and have been used in many different fields. This essay offers a succinct summary of various LLM subcategories. The survey emphasizes recent developments and efforts made for various LLM kinds, including task-based financial LLMs, multilingual language LLMs, biomedical and clinical LLMs, vision language LLMs, and code language models. The survey gives a general summary of the methods, attributes, datasets, transformer models, and comparison metrics applied in each category of LLMs. Furthermore, it highlights unresolved problems in the field of developing chatbots and virtual assistants, such as boosting natural language processing, enhancing chatbot intelligence, and resolving moral and legal dilemmas. The purpose of this study is to provide readers, developers, academics, and users interested in LLM-based chatbots and virtual intelligent assistant technologies with useful information and future directions.
    Selection functions of strong lens finding neural networks. (arXiv:2307.10355v1 [astro-ph.CO])
    Convolution Neural Networks trained for the task of lens finding with similar architecture and training data as is commonly found in the literature are biased classifiers. An understanding of the selection function of lens finding neural networks will be key to fully realising the potential of the large samples of strong gravitational lens systems that will be found in upcoming wide-field surveys. We use three training datasets, representative of those used to train galaxy-galaxy and galaxy-quasar lens finding neural networks. The networks preferentially select systems with larger Einstein radii and larger sources with more concentrated source-light distributions. Increasing the detection significance threshold to 12$\sigma$ from 8$\sigma$ results in 50 per cent of the selected strong lens systems having Einstein radii $\theta_\mathrm{E}$ $\ge$ 1.04 arcsec from $\theta_\mathrm{E}$ $\ge$ 0.879 arcsec, source radii $R_S$ $\ge$ 0.194 arcsec from $R_S$ $\ge$ 0.178 arcsec and source S\'ersic indices $n_{\mathrm{Sc}}^{\mathrm{S}}$ $\ge$ 2.62 from $n_{\mathrm{Sc}}^{\mathrm{S}}$ $\ge$ 2.55. The model trained to find lensed quasars shows a stronger preference for higher lens ellipticities than those trained to find lensed galaxies. The selection function is independent of the slope of the power-law of the mass profiles, hence measurements of this quantity will be unaffected. The lens finder selection function reinforces that of the lensing cross-section, and thus we expect our findings to be a general result for all galaxy-galaxy and galaxy-quasar lens finding neural networks.
    Efficient selective attention LSTM for well log curve synthesis. (arXiv:2307.10253v1 [cs.LG])
    Non-core drilling has gradually become the primary exploration method in geological engineering, and well logging curves have increasingly gained importance as the main carriers of geological information. However, factors such as geological environment, logging equipment, borehole quality, and unexpected events can all impact the quality of well logging curves. Previous methods of re-logging or manual corrections have been associated with high costs and low efficiency. This paper proposes a machine learning method that utilizes existing data to predict missing well logging curves, and its effectiveness and feasibility have been validated through experiments. The proposed method builds upon the traditional Long Short-Term Memory (LSTM) neural network by incorporating a self-attention mechanism to analyze the spatial dependencies of the data. It selectively includes the dominant computational results in the LSTM, reducing the computational complexity from O(n^2) to O(nlogn) and improving model efficiency. Experimental results demonstrate that the proposed method achieves higher accuracy compared to traditional curve synthesis methods based on Fully Connected Neural Networks (FCNN) and LSTM. This accurate, efficient, and cost-effective prediction method holds practical value in engineering applications.
    Hyperparameter Tuning Cookbook: A guide for scikit-learn, PyTorch, river, and spotPython. (arXiv:2307.10262v1 [cs.LG])
    This document provides a comprehensive guide to hyperparameter tuning using spotPython for scikit-learn, PyTorch, and river. The first part introduces spotPython's surrogate model-based optimization process, while the second part focuses on hyperparameter tuning. Several case studies are presented, including hyperparameter tuning for sklearn models such as Support Vector Classification, Random Forests, Gradient Boosting (XGB), and K-nearest neighbors (KNN), as well as a Hoeffding Adaptive Tree Regressor from river. The integration of spotPython into the PyTorch and PyTorch Lightning training workflow is also discussed. With a hands-on approach and step-by-step explanations, this cookbook serves as a practical starting point for anyone interested in hyperparameter tuning with Python. Highlights include the interplay between Tensorboard, PyTorch Lightning, spotPython, and river. This publication is under development, with updates available on the corresponding webpage.
    StyleGAN2-based Out-of-Distribution Detection for Medical Imaging. (arXiv:2307.10193v1 [eess.IV])
    One barrier to the clinical deployment of deep learning-based models is the presence of images at runtime that lie far outside the training distribution of a given model. We aim to detect these out-of-distribution (OOD) images with a generative adversarial network (GAN). Our training dataset was comprised of 3,234 liver-containing computed tomography (CT) scans from 456 patients. Our OOD test data consisted of CT images of the brain, head and neck, lung, cervix, and abnormal livers. A StyleGAN2-ADA architecture was employed to model the training distribution. Images were reconstructed using backpropagation. Reconstructions were evaluated using the Wasserstein distance, mean squared error, and the structural similarity index measure. OOD detection was evaluated with the area under the receiver operating characteristic curve (AUROC). Our paradigm distinguished between liver and non-liver CT with greater than 90% AUROC. It was also completely unable to reconstruct liver artifacts, such as needles and ascites.
    Evaluating and Enhancing Robustness of Deep Recommendation Systems Against Hardware Errors. (arXiv:2307.10244v1 [cs.IR])
    Deep recommendation systems (DRS) heavily depend on specialized HPC hardware and accelerators to optimize energy, efficiency, and recommendation quality. Despite the growing number of hardware errors observed in large-scale fleet systems where DRS are deployed, the robustness of DRS has been largely overlooked. This paper presents the first systematic study of DRS robustness against hardware errors. We develop Terrorch, a user-friendly, efficient and flexible error injection framework on top of the widely-used PyTorch. We evaluate a wide range of models and datasets and observe that the DRS robustness against hardware errors is influenced by various factors from model parameters to input characteristics. We also explore 3 error mitigation methods including algorithm based fault tolerance (ABFT), activation clipping and selective bit protection (SBP). We find that applying activation clipping can recover up to 30% of the degraded AUC-ROC score, making it a promising mitigation method.
    Fast Unsupervised Deep Outlier Model Selection with Hypernetworks. (arXiv:2307.10529v1 [cs.LG])
    Outlier detection (OD) finds many applications with a rich literature of numerous techniques. Deep neural network based OD (DOD) has seen a recent surge of attention thanks to the many advances in deep learning. In this paper, we consider a critical-yet-understudied challenge with unsupervised DOD, that is, effective hyperparameter (HP) tuning/model selection. While several prior work report the sensitivity of OD models to HPs, it becomes ever so critical for the modern DOD models that exhibit a long list of HPs. We introduce HYPER for tuning DOD models, tackling two fundamental challenges: (1) validation without supervision (due to lack of labeled anomalies), and (2) efficient search of the HP/model space (due to exponential growth in the number of HPs). A key idea is to design and train a novel hypernetwork (HN) that maps HPs onto optimal weights of the main DOD model. In turn, HYPER capitalizes on a single HN that can dynamically generate weights for many DOD models (corresponding to varying HPs), which offers significant speed-up. In addition, it employs meta-learning on historical OD tasks with labels to train a proxy validation function, likewise trained with our proposed HN efficiently. Extensive experiments on 35 OD tasks show that HYPER achieves high performance against 8 baselines with significant efficiency gains.
    ECSIC: Epipolar Cross Attention for Stereo Image Compression. (arXiv:2307.10284v1 [eess.IV])
    In this paper, we present ECSIC, a novel learned method for stereo image compression. Our proposed method compresses the left and right images in a joint manner by exploiting the mutual information between the images of the stereo image pair using a novel stereo cross attention (SCA) module and two stereo context modules. The SCA module performs cross-attention restricted to the corresponding epipolar lines of the two images and processes them in parallel. The stereo context modules improve the entropy estimation of the second encoded image by using the first image as a context. We conduct an extensive ablation study demonstrating the effectiveness of the proposed modules and a comprehensive quantitative and qualitative comparison with existing methods. ECSIC achieves state-of-the-art performance among stereo image compression models on the two popular stereo image datasets Cityscapes and InStereo2k while allowing for fast encoding and decoding, making it highly practical for real-time applications.
    On the Sensitivity of Deep Load Disaggregation to Adversarial Attacks. (arXiv:2307.10209v1 [cs.CR])
    Non-intrusive Load Monitoring (NILM) algorithms, commonly referred to as load disaggregation algorithms, are fundamental tools for effective energy management. Despite the success of deep models in load disaggregation, they face various challenges, particularly those pertaining to privacy and security. This paper investigates the sensitivity of prominent deep NILM baselines to adversarial attacks, which have proven to be a significant threat in domains such as computer vision and speech recognition. Adversarial attacks entail the introduction of imperceptible noise into the input data with the aim of misleading the neural network into generating erroneous outputs. We investigate the Fast Gradient Sign Method (FGSM), a well-known adversarial attack, to perturb the input sequences fed into two commonly employed CNN-based NILM baselines: the Sequence-to-Sequence (S2S) and Sequence-to-Point (S2P) models. Our findings provide compelling evidence for the vulnerability of these models, particularly the S2P model which exhibits an average decline of 20\% in the F1-score even with small amounts of noise. Such weakness has the potential to generate profound implications for energy management systems in residential and industrial sectors reliant on NILM models.
    Disentangling Societal Inequality from Model Biases: Gender Inequality in Divorce Court Proceedings. (arXiv:2307.10200v1 [cs.CY])
    Divorce is the legal dissolution of a marriage by a court. Since this is usually an unpleasant outcome of a marital union, each party may have reasons to call the decision to quit which is generally documented in detail in the court proceedings. Via a substantial corpus of 17,306 court proceedings, this paper investigates gender inequality through the lens of divorce court proceedings. While emerging data sources (e.g., public court records) on sensitive societal issues hold promise in aiding social science research, biases present in cutting-edge natural language processing (NLP) methods may interfere with or affect such studies. We thus require a thorough analysis of potential gaps and limitations present in extant NLP resources. In this paper, on the methodological side, we demonstrate that existing NLP resources required several non-trivial modifications to quantify societal inequalities. On the substantive side, we find that while a large number of court cases perhaps suggest changing norms in India where women are increasingly challenging patriarchy, AI-powered analyses of these court proceedings indicate striking gender inequality with women often subjected to domestic violence.
    A Bayesian Programming Approach to Car-following Model Calibration and Validation using Limited Data. (arXiv:2307.10437v1 [cs.LG])
    Traffic simulation software is used by transportation researchers and engineers to design and evaluate changes to roadways. These simulators are driven by models of microscopic driver behavior from which macroscopic measures like flow and congestion can be derived. Many models are designed for a subset of possible traffic scenarios and roadway configurations, while others have no explicit constraints on their application. Work zones (WZs) are one scenario for which no model to date has reproduced realistic driving behavior. This makes it difficult to optimize for safety and other metrics when designing a WZ. The Federal Highway Administration commissioned the USDOT Volpe Center to develop a car-following (CF) model for use in microscopic simulators that can capture and reproduce driver behavior accurately within and outside of WZs. Volpe also performed a naturalistic driving study to collect telematics data from vehicles driven on roads with WZs for use in model calibration. During model development, Volpe researchers observed difficulties in calibrating their model, leaving them to question whether there existed flaws in their model, in the data, or in the procedure used to calibrate the model using the data. In this thesis, I use Bayesian methods for data analysis and parameter estimation to explore and, where possible, address these questions. First, I use Bayesian inference to measure the sufficiency of the size of the data set. Second, I compare the procedure and results of the genetic algorithm based calibration performed by the Volpe researchers with those of Bayesian calibration. Third, I explore the benefits of modeling CF hierarchically. Finally, I apply what was learned in the first three phases using an established CF model, Wiedemann 99, to the probabilistic modeling of the Volpe model. Validation is performed using information criteria as an estimate of predictive accuracy.
    (Ab)using Images and Sounds for Indirect Instruction Injection in Multi-Modal LLMs. (arXiv:2307.10490v1 [cs.CR])
    We demonstrate how images and sounds can be used for indirect prompt and instruction injection in multi-modal LLMs. An attacker generates an adversarial perturbation corresponding to the prompt and blends it into an image or audio recording. When the user asks the (unmodified, benign) model about the perturbed image or audio, the perturbation steers the model to output the attacker-chosen text and/or make the subsequent dialog follow the attacker's instruction. We illustrate this attack with several proof-of-concept examples targeting LLaVa and PandaGPT.
    AUC Optimization from Multiple Unlabeled Datasets. (arXiv:2305.15776v2 [cs.LG] UPDATED)
    Weakly supervised learning aims to empower machine learning when the perfect supervision is unavailable, which has drawn great attention from researchers. Among various types of weak supervision, one of the most challenging cases is to learn from multiple unlabeled (U) datasets with only a little knowledge of the class priors, or U$^m$ learning for short. In this paper, we study the problem of building an AUC (area under ROC curve) optimization model from multiple unlabeled datasets, which maximizes the pairwise ranking ability of the classifier. We propose U$^m$-AUC, an AUC optimization approach that converts the U$^m$ data into a multi-label AUC optimization problem, and can be trained efficiently. We show that the proposed U$^m$-AUC is effective theoretically and empirically.
    FinGPT: Democratizing Internet-scale Data for Financial Large Language Models. (arXiv:2307.10485v1 [cs.CL])
    Large language models (LLMs) have demonstrated remarkable proficiency in understanding and generating human-like texts, which may potentially revolutionize the finance industry. However, existing LLMs often fall short in the financial field, which is mainly attributed to the disparities between general text data and financial text data. Unfortunately, there is only a limited number of financial text datasets available (quite small size), and BloombergGPT, the first financial LLM (FinLLM), is close-sourced (only the training logs were released). In light of this, we aim to democratize Internet-scale financial data for LLMs, which is an open challenge due to diverse data sources, low signal-to-noise ratio, and high time-validity. To address the challenges, we introduce an open-sourced and data-centric framework, \textit{Financial Generative Pre-trained Transformer (FinGPT)}, that automates the collection and curation of real-time financial data from >34 diverse sources on the Internet, providing researchers and practitioners with accessible and transparent resources to develop their FinLLMs. Additionally, we propose a simple yet effective strategy for fine-tuning FinLLM using the inherent feedback from the market, dubbed Reinforcement Learning with Stock Prices (RLSP). We also adopt the Low-rank Adaptation (LoRA, QLoRA) method that enables users to customize their own FinLLMs from open-source general-purpose LLMs at a low cost. Finally, we showcase several FinGPT applications, including robo-advisor, sentiment analysis for algorithmic trading, and low-code development. FinGPT aims to democratize FinLLMs, stimulate innovation, and unlock new opportunities in open finance. The codes are available at https://github.com/AI4Finance-Foundation/FinGPT and https://github.com/AI4Finance-Foundation/FinNLP
    CoNAN: Conditional Neural Aggregation Network For Unconstrained Face Feature Fusion. (arXiv:2307.10237v1 [cs.CV])
    Face recognition from image sets acquired under unregulated and uncontrolled settings, such as at large distances, low resolutions, varying viewpoints, illumination, pose, and atmospheric conditions, is challenging. Face feature aggregation, which involves aggregating a set of N feature representations present in a template into a single global representation, plays a pivotal role in such recognition systems. Existing works in traditional face feature aggregation either utilize metadata or high-dimensional intermediate feature representations to estimate feature quality for aggregation. However, generating high-quality metadata or style information is not feasible for extremely low-resolution faces captured in long-range and high altitude settings. To overcome these limitations, we propose a feature distribution conditioning approach called CoNAN for template aggregation. Specifically, our method aims to learn a context vector conditioned over the distribution information of the incoming feature set, which is utilized to weigh the features based on their estimated informativeness. The proposed method produces state-of-the-art results on long-range unconstrained face recognition datasets such as BTS, and DroneSURF, validating the advantages of such an aggregation strategy.
    SPRINT: A Unified Toolkit for Evaluating and Demystifying Zero-shot Neural Sparse Retrieval. (arXiv:2307.10488v1 [cs.IR])
    Traditionally, sparse retrieval systems relied on lexical representations to retrieve documents, such as BM25, dominated information retrieval tasks. With the onset of pre-trained transformer models such as BERT, neural sparse retrieval has led to a new paradigm within retrieval. Despite the success, there has been limited software supporting different sparse retrievers running in a unified, common environment. This hinders practitioners from fairly comparing different sparse models and obtaining realistic evaluation results. Another missing piece is, that a majority of prior work evaluates sparse retrieval models on in-domain retrieval, i.e. on a single dataset: MS MARCO. However, a key requirement in practical retrieval systems requires models that can generalize well to unseen out-of-domain, i.e. zero-shot retrieval tasks. In this work, we provide SPRINT, a unified Python toolkit based on Pyserini and Lucene, supporting a common interface for evaluating neural sparse retrieval. The toolkit currently includes five built-in models: uniCOIL, DeepImpact, SPARTA, TILDEv2 and SPLADEv2. Users can also easily add customized models by defining their term weighting method. Using our toolkit, we establish strong and reproducible zero-shot sparse retrieval baselines across the well-acknowledged benchmark, BEIR. Our results demonstrate that SPLADEv2 achieves the best average score of 0.470 nDCG@10 on BEIR amongst all neural sparse retrievers. In this work, we further uncover the reasons behind its performance gain. We show that SPLADEv2 produces sparse representations with a majority of tokens outside of the original query and document which is often crucial for its performance gains, i.e. a limitation among its other sparse counterparts. We provide our SPRINT toolkit, models, and data used in our experiments publicly here at https://github.com/thakur-nandan/sprint.
    Adversarial Training Over Long-Tailed Distribution. (arXiv:2307.10205v1 [cs.LG])
    In this paper, we study adversarial training on datasets that obey the long-tailed distribution, which is practical but rarely explored in previous works. Compared with conventional adversarial training on balanced datasets, this process falls into the dilemma of generating uneven adversarial examples (AEs) and an unbalanced feature embedding space, causing the resulting model to exhibit low robustness and accuracy on tail data. To combat that, we propose a new adversarial training framework -- Re-balancing Adversarial Training (REAT). This framework consists of two components: (1) a new training strategy inspired by the term effective number to guide the model to generate more balanced and informative AEs; (2) a carefully constructed penalty function to force a satisfactory feature space. Evaluation results on different datasets and model structures prove that REAT can effectively enhance the model's robustness and preserve the model's clean accuracy. The code can be found in https://github.com/GuanlinLee/REAT.
    Community-Aware Transformer for Autism Prediction in fMRI Connectome. (arXiv:2307.10181v1 [q-bio.NC])
    Autism spectrum disorder(ASD) is a lifelong neurodevelopmental condition that affects social communication and behavior. Investigating functional magnetic resonance imaging (fMRI)-based brain functional connectome can aid in the understanding and diagnosis of ASD, leading to more effective treatments. The brain is modeled as a network of brain Regions of Interest (ROIs), and ROIs form communities and knowledge of these communities is crucial for ASD diagnosis. On the one hand, Transformer-based models have proven to be highly effective across several tasks, including fMRI connectome analysis to learn useful representations of ROIs. On the other hand, existing transformer-based models treat all ROIs equally and overlook the impact of community-specific associations when learning node embeddings. To fill this gap, we propose a novel method, Com-BrainTF, a hierarchical local-global transformer architecture that learns intra and inter-community aware node embeddings for ASD prediction task. Furthermore, we avoid over-parameterization by sharing the local transformer parameters for different communities but optimize unique learnable prompt tokens for each community. Our model outperforms state-of-the-art (SOTA) architecture on ABIDE dataset and has high interpretability, evident from the attention module. Our code is available at https://github.com/ubc-tea/Com-BrainTF.
    Can Instruction Fine-Tuned Language Models Identify Social Bias through Prompting?. (arXiv:2307.10472v1 [cs.CL])
    As the breadth and depth of language model applications continue to expand rapidly, it is increasingly important to build efficient frameworks for measuring and mitigating the learned or inherited social biases of these models. In this paper, we present our work on evaluating instruction fine-tuned language models' ability to identify bias through zero-shot prompting, including Chain-of-Thought (CoT) prompts. Across LLaMA and its two instruction fine-tuned versions, Alpaca 7B performs best on the bias identification task with an accuracy of 56.7%. We also demonstrate that scaling up LLM size and data diversity could lead to further performance gain. This is a work-in-progress presenting the first component of our bias mitigation framework. We will keep updating this work as we get more results.
    Torchhd: An Open Source Python Library to Support Research on Hyperdimensional Computing and Vector Symbolic Architectures. (arXiv:2205.09208v2 [cs.LG] UPDATED)
    Hyperdimensional computing (HD), also known as vector symbolic architectures (VSA), is a framework for computing with distributed representations by exploiting properties of random high-dimensional vector spaces. The commitment of the scientific community to aggregate and disseminate research in this particularly multidisciplinary area has been fundamental for its advancement. Joining these efforts, we present Torchhd, a high-performance open source Python library for HD/VSA. Torchhd seeks to make HD/VSA more accessible and serves as an efficient foundation for further research and application development. The easy-to-use library builds on top of PyTorch and features state-of-the-art HD/VSA functionality, clear documentation, and implementation examples from well-known publications. Comparing publicly available code with their corresponding Torchhd implementation shows that experiments can run up to 100x faster. Torchhd is available at: https://github.com/hyperdimensional-computing/torchhd.
    DP-TBART: A Transformer-based Autoregressive Model for Differentially Private Tabular Data Generation. (arXiv:2307.10430v1 [cs.LG])
    The generation of synthetic tabular data that preserves differential privacy is a problem of growing importance. While traditional marginal-based methods have achieved impressive results, recent work has shown that deep learning-based approaches tend to lag behind. In this work, we present Differentially-Private TaBular AutoRegressive Transformer (DP-TBART), a transformer-based autoregressive model that maintains differential privacy and achieves performance competitive with marginal-based methods on a wide variety of datasets, capable of even outperforming state-of-the-art methods in certain settings. We also provide a theoretical framework for understanding the limitations of marginal-based approaches and where deep learning-based approaches stand to contribute most. These results suggest that deep learning-based techniques should be considered as a viable alternative to marginal-based methods in the generation of differentially private synthetic tabular data.
    Confidence Estimation Using Unlabeled Data. (arXiv:2307.10440v1 [cs.LG])
    Overconfidence is a common issue for deep neural networks, limiting their deployment in real-world applications. To better estimate confidence, existing methods mostly focus on fully-supervised scenarios and rely on training labels. In this paper, we propose the first confidence estimation method for a semi-supervised setting, when most training labels are unavailable. We stipulate that even with limited training labels, we can still reasonably approximate the confidence of model on unlabeled samples by inspecting the prediction consistency through the training process. We use training consistency as a surrogate function and propose a consistency ranking loss for confidence estimation. On both image classification and segmentation tasks, our method achieves state-of-the-art performances in confidence estimation. Furthermore, we show the benefit of the proposed method through a downstream active learning task. The code is available at https://github.com/TopoXLab/consistency-ranking-loss
    Eliminating Label Leakage in Tree-Based Vertical Federated Learning. (arXiv:2307.10318v1 [cs.LG])
    Vertical federated learning (VFL) enables multiple parties with disjoint features of a common user set to train a machine learning model without sharing their private data. Tree-based models have become prevalent in VFL due to their interpretability and efficiency. However, the vulnerability of tree-based VFL has not been sufficiently investigated. In this study, we first introduce a novel label inference attack, ID2Graph, which utilizes the sets of record-IDs assigned to each node (i.e., instance space) to deduce private training labels. The ID2Graph attack generates a graph structure from training samples, extracts communities from the graph, and clusters the local dataset using community information. To counteract label leakage from the instance space, we propose an effective defense mechanism, ID-LMID, which prevents label leakage by focusing on mutual information regularization. Comprehensive experiments conducted on various datasets reveal that the ID2Graph attack presents significant risks to tree-based models such as Random Forest and XGBoost. Further evaluations on these benchmarks demonstrate that ID-LMID effectively mitigates label leakage in such instances.
    Automated Action Model Acquisition from Narrative Texts. (arXiv:2307.10247v1 [cs.CL])
    Action models, which take the form of precondition/effect axioms, facilitate causal and motivational connections between actions for AI agents. Action model acquisition has been identified as a bottleneck in the application of planning technology, especially within narrative planning. Acquiring action models from narrative texts in an automated way is essential, but challenging because of the inherent complexities of such texts. We present NaRuto, a system that extracts structured events from narrative text and subsequently generates planning-language-style action models based on predictions of commonsense event relations, as well as textual contradictions and similarities, in an unsupervised manner. Experimental results in classical narrative planning domains show that NaRuto can generate action models of significantly better quality than existing fully automated methods, and even on par with those of semi-automated methods.
    Automated Knowledge Modeling for Cancer Clinical Practice Guidelines. (arXiv:2307.10231v1 [cs.AI])
    Clinical Practice Guidelines (CPGs) for cancer diseases evolve rapidly due to new evidence generated by active research. Currently, CPGs are primarily published in a document format that is ill-suited for managing this developing knowledge. A knowledge model of the guidelines document suitable for programmatic interaction is required. This work proposes an automated method for extraction of knowledge from National Comprehensive Cancer Network (NCCN) CPGs in Oncology and generating a structured model containing the retrieved knowledge. The proposed method was tested using two versions of NCCN Non-Small Cell Lung Cancer (NSCLC) CPG to demonstrate the effectiveness in faithful extraction and modeling of knowledge. Three enrichment strategies using Cancer staging information, Unified Medical Language System (UMLS) Metathesaurus & National Cancer Institute thesaurus (NCIt) concepts, and Node classification are also presented to enhance the model towards enabling programmatic traversal and querying of cancer care guidelines. The Node classification was performed using a Support Vector Machine (SVM) model, achieving a classification accuracy of 0.81 with 10-fold cross-validation.
    A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic. (arXiv:2204.06362v2 [cs.LG] UPDATED)
    The use of Machine Learning (ML) has rapidly spread across several fields, having encountered many applications in Structural Dynamics and Vibroacoustic (SD\&V). The increasing capabilities of ML to unveil insights from data, driven by unprecedented data availability, algorithms advances and computational power, enhance decision making, uncertainty handling, patterns recognition and real-time assessments. Three main applications in SD\&V have taken advantage of these benefits. In Structural Health Monitoring, ML detection and prognosis lead to safe operation and optimized maintenance schedules. System identification and control design are leveraged by ML techniques in Active Noise Control and Active Vibration Control. Finally, the so-called ML-based surrogate models provide fast alternatives to costly simulations, enabling robust and optimized product design. Despite the many works in the area, they have not been reviewed and analyzed. Therefore, to keep track and understand this ongoing integration of fields, this paper presents a survey of ML applications in SD\&V analyses, shedding light on the current state of implementation and emerging opportunities. The main methodologies, advantages, limitations, and recommendations based on scientific knowledge were identified for each of the three applications. Moreover, the paper considers the role of Digital Twins and Physics Guided ML to overcome current challenges and power future research progress. As a result, the survey provides a broad overview of the present landscape of ML applied in SD\&V and guides the reader to an advanced understanding of progress and prospects in the field.  ( 3 min )
    Novel Batch Active Learning Approach and Its Application to Synthetic Aperture Radar Datasets. (arXiv:2307.10495v1 [cs.LG])
    Active learning improves the performance of machine learning methods by judiciously selecting a limited number of unlabeled data points to query for labels, with the aim of maximally improving the underlying classifier's performance. Recent gains have been made using sequential active learning for synthetic aperture radar (SAR) data arXiv:2204.00005. In each iteration, sequential active learning selects a query set of size one while batch active learning selects a query set of multiple datapoints. While batch active learning methods exhibit greater efficiency, the challenge lies in maintaining model accuracy relative to sequential active learning methods. We developed a novel, two-part approach for batch active learning: Dijkstra's Annulus Core-Set (DAC) for core-set generation and LocalMax for batch sampling. The batch active learning process that combines DAC and LocalMax achieves nearly identical accuracy as sequential active learning but is more efficient, proportional to the batch size. As an application, a pipeline is built based on transfer learning feature embedding, graph learning, DAC, and LocalMax to classify the FUSAR-Ship and OpenSARShip datasets. Our pipeline outperforms the state-of-the-art CNN-based methods.
    Polyffusion: A Diffusion Model for Polyphonic Score Generation with Internal and External Controls. (arXiv:2307.10304v1 [cs.SD])
    We propose Polyffusion, a diffusion model that generates polyphonic music scores by regarding music as image-like piano roll representations. The model is capable of controllable music generation with two paradigms: internal control and external control. Internal control refers to the process in which users pre-define a part of the music and then let the model infill the rest, similar to the task of masked music generation (or music inpainting). External control conditions the model with external yet related information, such as chord, texture, or other features, via the cross-attention mechanism. We show that by using internal and external controls, Polyffusion unifies a wide range of music creation tasks, including melody generation given accompaniment, accompaniment generation given melody, arbitrary music segment inpainting, and music arrangement given chords or textures. Experimental results show that our model significantly outperforms existing Transformer and sampling-based baselines, and using pre-trained disentangled representations as external conditions yields more effective controls.
    Reproducibility in Machine Learning-Driven Research. (arXiv:2307.10320v1 [cs.LG])
    Research is facing a reproducibility crisis, in which the results and findings of many studies are difficult or even impossible to reproduce. This is also the case in machine learning (ML) and artificial intelligence (AI) research. Often, this is the case due to unpublished data and/or source-code, and due to sensitivity to ML training conditions. Although different solutions to address this issue are discussed in the research community such as using ML platforms, the level of reproducibility in ML-driven research is not increasing substantially. Therefore, in this mini survey, we review the literature on reproducibility in ML-driven research with three main aims: (i) reflect on the current situation of ML reproducibility in various research fields, (ii) identify reproducibility issues and barriers that exist in these research fields applying ML, and (iii) identify potential drivers such as tools, practices, and interventions that support ML reproducibility. With this, we hope to contribute to decisions on the viability of different solutions for supporting ML reproducibility.
    Performance Issue Identification in Cloud Systems with Relational-Temporal Anomaly Detection. (arXiv:2307.10869v1 [cs.LG])
    Performance issues permeate large-scale cloud service systems, which can lead to huge revenue losses. To ensure reliable performance, it's essential to accurately identify and localize these issues using service monitoring metrics. Given the complexity and scale of modern cloud systems, this task can be challenging and may require extensive expertise and resources beyond the capacity of individual humans. Some existing methods tackle this problem by analyzing each metric independently to detect anomalies. However, this could incur overwhelming alert storms that are difficult for engineers to diagnose manually. To pursue better performance, not only the temporal patterns of metrics but also the correlation between metrics (i.e., relational patterns) should be considered, which can be formulated as a multivariate metrics anomaly detection problem. However, most of the studies fall short of extracting these two types of features explicitly. Moreover, there exist some unlabeled anomalies mixed in the training data, which may hinder the detection performance. To address these limitations, we propose the Relational- Temporal Anomaly Detection Model (RTAnomaly) that combines the relational and temporal information of metrics. RTAnomaly employs a graph attention layer to learn the dependencies among metrics, which will further help pinpoint the anomalous metrics that may cause the anomaly effectively. In addition, we exploit the concept of positive unlabeled learning to address the issue of potential anomalies in the training data. To evaluate our method, we conduct experiments on a public dataset and two industrial datasets. RTAnomaly outperforms all the baseline models by achieving an average F1 score of 0.929 and Hit@3 of 0.920, demonstrating its superiority.  ( 3 min )
    Data-Efficient Augmentation for Training Neural Networks. (arXiv:2210.08363v3 [cs.LG] UPDATED)
    Data augmentation is essential to achieve state-of-the-art performance in many deep learning applications. However, the most effective augmentation techniques become computationally prohibitive for even medium-sized datasets. To address this, we propose a rigorous technique to select subsets of data points that when augmented, closely capture the training dynamics of full data augmentation. We first show that data augmentation, modeled as additive perturbations, improves learning and generalization by relatively enlarging and perturbing the smaller singular values of the network Jacobian, while preserving its prominent directions. This prevents overfitting and enhances learning the harder to learn information. Then, we propose a framework to iteratively extract small subsets of training data that when augmented, closely capture the alignment of the fully augmented Jacobian with labels/residuals. We prove that stochastic gradient descent applied to the augmented subsets found by our approach has similar training dynamics to that of fully augmented data. Our experiments demonstrate that our method achieves 6.3x speedup on CIFAR10 and 2.2x speedup on SVHN, and outperforms the baselines by up to 10% across various subset sizes. Similarly, on TinyImageNet and ImageNet, our method beats the baselines by up to 8%, while achieving up to 3.3x speedup across various subset sizes. Finally, training on and augmenting 50% subsets using our method on a version of CIFAR10 corrupted with label noise even outperforms using the full dataset. Our code is available at: https://github.com/tianyu139/data-efficient-augmentation  ( 3 min )
    AlignAtt: Using Attention-based Audio-Translation Alignments as a Guide for Simultaneous Speech Translation. (arXiv:2305.11408v2 [cs.CL] UPDATED)
    Attention is the core mechanism of today's most used architectures for natural language processing and has been analyzed from many perspectives, including its effectiveness for machine translation-related tasks. Among these studies, attention resulted to be a useful source of information to get insights about word alignment also when the input text is substituted with audio segments, as in the case of the speech translation (ST) task. In this paper, we propose AlignAtt, a novel policy for simultaneous ST (SimulST) that exploits the attention information to generate source-target alignments that guide the model during inference. Through experiments on the 8 language pairs of MuST-C v1.0, we show that AlignAtt outperforms previous state-of-the-art SimulST policies applied to offline-trained models with gains in terms of BLEU of 2 points and latency reductions ranging from 0.5s to 0.8s across the 8 languages.  ( 2 min )
    Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curves. (arXiv:2111.03950v4 [stat.ME] UPDATED)
    We propose simple nonparametric estimators for mediated and time-varying dose response curves based on kernel ridge regression. By embedding Pearl's mediation formula and Robins' g-formula with kernels, we allow treatments, mediators, and covariates to be continuous in general spaces, and also allow for nonlinear treatment-confounder feedback. Our key innovation is a reproducing kernel Hilbert space technique called sequential kernel embedding, which we use to construct simple estimators for complex causal estimands. Our estimators preserve the generality of classic identification while also achieving nonasymptotic uniform rates. In nonlinear simulations with many covariates, we demonstrate strong performance. We estimate mediated and time-varying dose response curves of the US Job Corps, and clean data that may serve as a benchmark in future work. We extend our results to mediated and time-varying treatment effects and counterfactual distributions, verifying semiparametric efficiency and weak convergence.  ( 2 min )
    Robust Principal Component Analysis: A Median of Means Approach. (arXiv:2102.03403v2 [stat.ML] UPDATED)
    Principal Component Analysis (PCA) is a fundamental tool for data visualization, denoising, and dimensionality reduction. It is widely popular in Statistics, Machine Learning, Computer Vision, and related fields. However, PCA is well-known to fall prey to outliers and often fails to detect the true underlying low-dimensional structure within the dataset. Following the Median of Means (MoM) philosophy, recent supervised learning methods have shown great success in dealing with outlying observations without much compromise to their large sample theoretical properties. This paper proposes a PCA procedure based on the MoM principle. Called the \textbf{M}edian of \textbf{M}eans \textbf{P}rincipal \textbf{C}omponent \textbf{A}nalysis (MoMPCA), the proposed method is not only computationally appealing but also achieves optimal convergence rates under minimal assumptions. In particular, we explore the non-asymptotic error bounds of the obtained solution via the aid of the Rademacher complexities while granting absolutely no assumption on the outlying observations. The derived concentration results are not dependent on the dimension because the analysis is conducted in a separable Hilbert space, and the results only depend on the fourth moment of the underlying distribution in the corresponding norm. The proposal's efficacy is also thoroughly showcased through simulations and real data applications.  ( 2 min )
    ProtiGeno: a prokaryotic short gene finder using protein language models. (arXiv:2307.10343v1 [q-bio.GN])
    Prokaryotic gene prediction plays an important role in understanding the biology of organisms and their function with applications in medicine and biotechnology. Although the current gene finders are highly sensitive in finding long genes, their sensitivity decreases noticeably in finding shorter genes (<180 nts). The culprit is insufficient annotated gene data to identify distinguishing features in short open reading frames (ORFs). We develop a deep learning-based method called ProtiGeno, specifically targeting short prokaryotic genes using a protein language model trained on millions of evolved proteins. In systematic large-scale experiments on 4,288 prokaryotic genomes, we demonstrate that ProtiGeno predicts short coding and noncoding genes with higher accuracy and recall than the current state-of-the-art gene finders. We discuss the predictive features of ProtiGeno and possible limitations by visualizing the three-dimensional structure of the predicted short genes. Data, codes, and models are available at https://github.com/tonytu16/protigeno.
    TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving Cars. (arXiv:2307.10705v1 [cs.CV])
    Semantic segmentation is a common task in autonomous driving to understand the surrounding environment. Driveable Area Segmentation and Lane Detection are particularly important for safe and efficient navigation on the road. However, original semantic segmentation models are computationally expensive and require high-end hardware, which is not feasible for embedded systems in autonomous vehicles. This paper proposes a lightweight model for the driveable area and lane line segmentation. TwinLiteNet is designed cheaply but achieves accurate and efficient segmentation results. We evaluate TwinLiteNet on the BDD100K dataset and compare it with modern models. Experimental results show that our TwinLiteNet performs similarly to existing approaches, requiring significantly fewer computational resources. Specifically, TwinLiteNet achieves a mIoU score of 91.3% for the Drivable Area task and 31.08% IoU for the Lane Detection task with only 0.4 million parameters and achieves 415 FPS on GPU RTX A5000. Furthermore, TwinLiteNet can run in real-time on embedded devices with limited computing power, especially since it achieves 60FPS on Jetson Xavier NX, making it an ideal solution for self-driving vehicles. Code is available: url{https://github.com/chequanghuy/TwinLiteNet}.
    Regular SE(3) Group Convolutions for Volumetric Medical Image Analysis. (arXiv:2306.13960v2 [cs.CV] UPDATED)
    Regular group convolutional neural networks (G-CNNs) have been shown to increase model performance and improve equivariance to different geometrical symmetries. This work addresses the problem of SE(3), i.e., roto-translation equivariance, on volumetric data. Volumetric image data is prevalent in many medical settings. Motivated by the recent work on separable group convolutions, we devise a SE(3) group convolution kernel separated into a continuous SO(3) (rotation) kernel and a spatial kernel. We approximate equivariance to the continuous setting by sampling uniform SO(3) grids. Our continuous SO(3) kernel is parameterized via RBF interpolation on similarly uniform grids. We demonstrate the advantages of our approach in volumetric medical image analysis. Our SE(3) equivariant models consistently outperform CNNs and regular discrete G-CNNs on challenging medical classification tasks and show significantly improved generalization capabilities. Our approach achieves up to a 16.5% gain in accuracy over regular CNNs.
    Analyzing sports commentary in order to automatically recognize events and extract insights. (arXiv:2307.10303v1 [cs.CL])
    In this paper, we carefully investigate how we can use multiple different Natural Language Processing techniques and methods in order to automatically recognize the main actions in sports events. We aim to extract insights by analyzing live sport commentaries from different sources and by classifying these major actions into different categories. We also study if sentiment analysis could help detect these main actions.
    Integrating a Heterogeneous Graph with Entity-aware Self-attention using Relative Position Labels for Reading Comprehension Model. (arXiv:2307.10443v1 [cs.CL])
    Despite the significant progress made by transformer models in machine reading comprehension tasks, they still face limitations in handling complex reasoning tasks due to the absence of explicit knowledge in the input sequence. This paper proposes a novel attention pattern to overcome this limitation, which integrates reasoning knowledge derived from a heterogeneous graph into the transformer architecture using a graph-enhanced self-attention mechanism. The proposed attention pattern comprises three key elements: global-local attention for word tokens, graph attention for entity tokens that exhibit strong attention towards tokens connected in the graph as opposed to those unconnected, and the consideration of the type of relationship between each entity token and word token. This results in optimized attention between the two if a relationship exists. The pattern is coupled with special relative position labels, allowing it to integrate with LUKE's entity-aware self-attention mechanism. The experimental findings corroborate that our model outperforms both the cutting-edge LUKE-Graph and the baseline LUKE model on the ReCoRD dataset that focuses on commonsense reasoning.
    Multi-Scale U-Shape MLP for Hyperspectral Image Classification. (arXiv:2307.10186v1 [eess.IV])
    Hyperspectral images have significant applications in various domains, since they register numerous semantic and spatial information in the spectral band with spatial variability of spectral signatures. Two critical challenges in identifying pixels of the hyperspectral image are respectively representing the correlated information among the local and global, as well as the abundant parameters of the model. To tackle this challenge, we propose a Multi-Scale U-shape Multi-Layer Perceptron (MUMLP) a model consisting of the designed MSC (Multi-Scale Channel) block and the UMLP (U-shape Multi-Layer Perceptron) structure. MSC transforms the channel dimension and mixes spectral band feature to embed the deep-level representation adequately. UMLP is designed by the encoder-decoder structure with multi-layer perceptron layers, which is capable of compressing the large-scale parameters. Extensive experiments are conducted to demonstrate our model can outperform state-of-the-art methods across-the-board on three wide-adopted public datasets, namely Pavia University, Houston 2013 and Houston 2018
    A Competitive Learning Approach for Specialized Models: A Solution for Complex Physical Systems with Distinct Functional Regimes. (arXiv:2307.10496v1 [cs.LG])
    Complex systems in science and engineering sometimes exhibit behavior that changes across different regimes. Traditional global models struggle to capture the full range of this complex behavior, limiting their ability to accurately represent the system. In response to this challenge, we propose a novel competitive learning approach for obtaining data-driven models of physical systems. The primary idea behind the proposed approach is to employ dynamic loss functions for a set of models that are trained concurrently on the data. Each model competes for each observation during training, allowing for the identification of distinct functional regimes within the dataset. To demonstrate the effectiveness of the learning approach, we coupled it with various regression methods that employ gradient-based optimizers for training. The proposed approach was tested on various problems involving model discovery and function approximation, demonstrating its ability to successfully identify functional regimes, discover true governing equations, and reduce test errors.
    Decentralized Smart Charging of Large-Scale EVs using Adaptive Multi-Agent Multi-Armed Bandits. (arXiv:2307.10704v1 [cs.LG])
    The drastic growth of electric vehicles and photovoltaics can introduce new challenges, such as electrical current congestion and voltage limit violations due to peak load demands. These issues can be mitigated by controlling the operation of electric vehicles i.e., smart charging. Centralized smart charging solutions have already been proposed in the literature. But such solutions may lack scalability and suffer from inherent drawbacks of centralization, such as a single point of failure, and data privacy concerns. Decentralization can help tackle these challenges. In this paper, a fully decentralized smart charging system is proposed using the philosophy of adaptive multi-agent systems. The proposed system utilizes multi-armed bandit learning to handle uncertainties in the system. The presented system is decentralized, scalable, real-time, model-free, and takes fairness among different players into account. A detailed case study is also presented for performance evaluation.  ( 2 min )
    Assessing the Use of AutoML for Data-Driven Software Engineering. (arXiv:2307.10774v1 [cs.SE])
    Background. Due to the widespread adoption of Artificial Intelligence (AI) and Machine Learning (ML) for building software applications, companies are struggling to recruit employees with a deep understanding of such technologies. In this scenario, AutoML is soaring as a promising solution to fill the AI/ML skills gap since it promises to automate the building of end-to-end AI/ML pipelines that would normally be engineered by specialized team members. Aims. Despite the growing interest and high expectations, there is a dearth of information about the extent to which AutoML is currently adopted by teams developing AI/ML-enabled systems and how it is perceived by practitioners and researchers. Method. To fill these gaps, in this paper, we present a mixed-method study comprising a benchmark of 12 end-to-end AutoML tools on two SE datasets and a user survey with follow-up interviews to further our understanding of AutoML adoption and perception. Results. We found that AutoML solutions can generate models that outperform those trained and optimized by researchers to perform classification tasks in the SE domain. Also, our findings show that the currently available AutoML solutions do not live up to their names as they do not equally support automation across the stages of the ML development workflow and for all the team members. Conclusions. We derive insights to inform the SE research community on how AutoML can facilitate their activities and tool builders on how to design the next generation of AutoML technologies.  ( 3 min )
    Self2Self+: Single-Image Denoising with Self-Supervised Learning and Image Quality Assessment Loss. (arXiv:2307.10695v1 [cs.CV])
    Recently, denoising methods based on supervised learning have exhibited promising performance. However, their reliance on external datasets containing noisy-clean image pairs restricts their applicability. To address this limitation, researchers have focused on training denoising networks using solely a set of noisy inputs. To improve the feasibility of denoising procedures, in this study, we proposed a single-image self-supervised learning method in which only the noisy input image is used for network training. Gated convolution was used for feature extraction and no-reference image quality assessment was used for guiding the training process. Moreover, the proposed method sampled instances from the input image dataset using Bernoulli sampling with a certain dropout rate for training. The corresponding result was produced by averaging the generated predictions from various instances of the trained network with dropouts. The experimental results indicated that the proposed method achieved state-of-the-art denoising performance on both synthetic and real-world datasets. This highlights the effectiveness and practicality of our method as a potential solution for various noise removal tasks.  ( 2 min )
    FigCaps-HF: A Figure-to-Caption Generative Framework and Benchmark with Human Feedback. (arXiv:2307.10867v1 [cs.CL])
    Captions are crucial for understanding scientific visualizations and documents. Existing captioning methods for scientific figures rely on figure-caption pairs extracted from documents for training, many of which fall short with respect to metrics like helpfulness, explainability, and visual-descriptiveness [15] leading to generated captions being misaligned with reader preferences. To enable the generation of high-quality figure captions, we introduce FigCaps-HF a new framework for figure-caption generation that can incorporate domain expert feedback in generating captions optimized for reader preferences. Our framework comprises of 1) an automatic method for evaluating quality of figure-caption pairs, 2) a novel reinforcement learning with human feedback (RLHF) method to optimize a generative figure-to-caption model for reader preferences. We demonstrate the effectiveness of our simple learning framework by improving performance over standard fine-tuning across different types of models. In particular, when using BLIP as the base model, our RLHF framework achieves a mean gain of 35.7%, 16.9%, and 9% in ROUGE, BLEU, and Meteor, respectively. Finally, we release a large-scale benchmark dataset with human feedback on figure-caption pairs to enable further evaluation and development of RLHF techniques for this problem.  ( 2 min )
    Deceptive Alignment Monitoring. (arXiv:2307.10569v1 [cs.LG])
    As the capabilities of large machine learning models continue to grow, and as the autonomy afforded to such models continues to expand, the spectre of a new adversary looms: the models themselves. The threat that a model might behave in a seemingly reasonable manner, while secretly and subtly modifying its behavior for ulterior reasons is often referred to as deceptive alignment in the AI Safety & Alignment communities. Consequently, we call this new direction Deceptive Alignment Monitoring. In this work, we identify emerging directions in diverse machine learning subfields that we believe will become increasingly important and intertwined in the near future for deceptive alignment monitoring, and we argue that advances in these fields present both long-term challenges and new research opportunities. We conclude by advocating for greater involvement by the adversarial machine learning community in these emerging directions.  ( 2 min )
    SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models. (arXiv:2307.10635v1 [cs.CL])
    Recent advances in large language models (LLMs) have demonstrated notable progress on many mathematical benchmarks. However, most of these benchmarks only feature problems grounded in junior and senior high school subjects, contain only multiple-choice questions, and are confined to a limited scope of elementary arithmetic operations. To address these issues, this paper introduces an expansive benchmark suite SciBench that aims to systematically examine the reasoning capabilities required for complex scientific problem solving. SciBench contains two carefully curated datasets: an open set featuring a range of collegiate-level scientific problems drawn from mathematics, chemistry, and physics textbooks, and a closed set comprising problems from undergraduate-level exams in computer science and mathematics. Based on the two datasets, we conduct an in-depth benchmark study of two representative LLMs with various prompting strategies. The results reveal that current LLMs fall short of delivering satisfactory performance, with an overall score of merely 35.80%. Furthermore, through a detailed user study, we categorize the errors made by LLMs into ten problem-solving abilities. Our analysis indicates that no single prompting strategy significantly outperforms others and some strategies that demonstrate improvements in certain problem-solving skills result in declines in other skills. We envision that SciBench will catalyze further developments in the reasoning abilities of LLMs, thereby ultimately contributing to scientific research and discovery.  ( 3 min )
    Nonlinear Meta-Learning Can Guarantee Faster Rates. (arXiv:2307.10870v1 [stat.ML])
    Many recent theoretical works on \emph{meta-learning} aim to achieve guarantees in leveraging similar representational structures from related tasks towards simplifying a target task. Importantly, the main aim in theory works on the subject is to understand the extent to which convergence rates -- in learning a common representation -- \emph{may scale with the number $N$ of tasks} (as well as the number of samples per task). First steps in this setting demonstrate this property when both the shared representation amongst tasks, and task-specific regression functions, are linear. This linear setting readily reveals the benefits of aggregating tasks, e.g., via averaging arguments. In practice, however, the representation is often highly nonlinear, introducing nontrivial biases in each task that cannot easily be averaged out as in the linear case. In the present work, we derive theoretical guarantees for meta-learning with nonlinear representations. In particular, assuming the shared nonlinearity maps to an infinite-dimensional RKHS, we show that additional biases can be mitigated with careful regularization that leverages the smoothness of task-specific regression functions,  ( 2 min )
    Mitigating Voter Attribute Bias for Fair Opinion Aggregation. (arXiv:2307.10749v1 [cs.HC])
    The aggregation of multiple opinions plays a crucial role in decision-making, such as in hiring and loan review, and in labeling data for supervised learning. Although majority voting and existing opinion aggregation models are effective for simple tasks, they are inappropriate for tasks without objectively true labels in which disagreements may occur. In particular, when voter attributes such as gender or race introduce bias into opinions, the aggregation results may vary depending on the composition of voter attributes. A balanced group of voters is desirable for fair aggregation results but may be difficult to prepare. In this study, we consider methods to achieve fair opinion aggregation based on voter attributes and evaluate the fairness of the aggregated results. To this end, we consider an approach that combines opinion aggregation models such as majority voting and the Dawid and Skene model (D&S model) with fairness options such as sample weighting. To evaluate the fairness of opinion aggregation, probabilistic soft labels are preferred over discrete class labels. First, we address the problem of soft label estimation without considering voter attributes and identify some issues with the D&S model. To address these limitations, we propose a new Soft D&S model with improved accuracy in estimating soft labels. Moreover, we evaluated the fairness of an opinion aggregation model, including Soft D&S, in combination with different fairness options using synthetic and semi-synthetic data. The experimental results suggest that the combination of Soft D&S and data splitting as a fairness option is effective for dense data, whereas weighted majority voting is effective for sparse data. These findings should prove particularly valuable in supporting decision-making by human and machine-learning models with balanced opinion aggregation.  ( 3 min )
    Fractional Denoising for 3D Molecular Pre-training. (arXiv:2307.10683v1 [q-bio.QM])
    Coordinate denoising is a promising 3D molecular pre-training method, which has achieved remarkable performance in various downstream drug discovery tasks. Theoretically, the objective is equivalent to learning the force field, which is revealed helpful for downstream tasks. Nevertheless, there are two challenges for coordinate denoising to learn an effective force field, i.e. low coverage samples and isotropic force field. The underlying reason is that molecular distributions assumed by existing denoising methods fail to capture the anisotropic characteristic of molecules. To tackle these challenges, we propose a novel hybrid noise strategy, including noises on both dihedral angel and coordinate. However, denoising such hybrid noise in a traditional way is no more equivalent to learning the force field. Through theoretical deductions, we find that the problem is caused by the dependency of the input conformation for covariance. To this end, we propose to decouple the two types of noise and design a novel fractional denoising method (Frad), which only denoises the latter coordinate part. In this way, Frad enjoys both the merits of sampling more low-energy structures and the force field equivalence. Extensive experiments show the effectiveness of Frad in molecular representation, with a new state-of-the-art on 9 out of 12 tasks of QM9 and on 7 out of 8 targets of MD17.  ( 2 min )
    Refining the Optimization Target for Automatic Univariate Time Series Anomaly Detection in Monitoring Services. (arXiv:2307.10653v1 [cs.LG])
    Time series anomaly detection is crucial for industrial monitoring services that handle a large volume of data, aiming to ensure reliability and optimize system performance. Existing methods often require extensive labeled resources and manual parameter selection, highlighting the need for automation. This paper proposes a comprehensive framework for automatic parameter optimization in time series anomaly detection models. The framework introduces three optimization targets: prediction score, shape score, and sensitivity score, which can be easily adapted to different model backbones without prior knowledge or manual labeling efforts. The proposed framework has been successfully applied online for over six months, serving more than 50,000 time series every minute. It simplifies the user's experience by requiring only an expected sensitive value, offering a user-friendly interface, and achieving desired detection results. Extensive evaluations conducted on public datasets and comparison with other methods further confirm the effectiveness of the proposed framework.  ( 2 min )
    Differences Between Hard and Noisy-labeled Samples: An Empirical Study. (arXiv:2307.10718v1 [cs.LG])
    Extracting noisy or incorrectly labeled samples from a labeled dataset with hard/difficult samples is an important yet under-explored topic. Two general and often independent lines of work exist, one focuses on addressing noisy labels, and another deals with hard samples. However, when both types of data are present, most existing methods treat them equally, which results in a decline in the overall performance of the model. In this paper, we first design various synthetic datasets with custom hardness and noisiness levels for different samples. Our proposed systematic empirical study enables us to better understand the similarities and more importantly the differences between hard-to-learn samples and incorrectly-labeled samples. These controlled experiments pave the way for the development of methods that distinguish between hard and noisy samples. Through our study, we introduce a simple yet effective metric that filters out noisy-labeled samples while keeping the hard samples. We study various data partitioning methods in the presence of label noise and observe that filtering out noisy samples from hard samples with this proposed metric results in the best datasets as evidenced by the high test accuracy achieved after models are trained on the filtered datasets. We demonstrate this for both our created synthetic datasets and for datasets with real-world label noise. Furthermore, our proposed data partitioning method significantly outperforms other methods when employed within a semi-supervised learning framework.  ( 2 min )
    Label Calibration for Semantic Segmentation Under Domain Shift. (arXiv:2307.10842v1 [cs.CV])
    Performance of a pre-trained semantic segmentation model is likely to substantially decrease on data from a new domain. We show a pre-trained model can be adapted to unlabelled target domain data by calculating soft-label prototypes under the domain shift and making predictions according to the prototype closest to the vector with predicted class probabilities. The proposed adaptation procedure is fast, comes almost for free in terms of computational resources and leads to considerable performance improvements. We demonstrate the benefits of such label calibration on the highly-practical synthetic-to-real semantic segmentation problem.  ( 2 min )
    Graphs in State-Space Models for Granger Causality in Climate Science. (arXiv:2307.10703v1 [cs.LG])
    Granger causality (GC) is often considered not an actual form of causality. Still, it is arguably the most widely used method to assess the predictability of a time series from another one. Granger causality has been widely used in many applied disciplines, from neuroscience and econometrics to Earth sciences. We revisit GC under a graphical perspective of state-space models. For that, we use GraphEM, a recently presented expectation-maximisation algorithm for estimating the linear matrix operator in the state equation of a linear-Gaussian state-space model. Lasso regularisation is included in the M-step, which is solved using a proximal splitting Douglas-Rachford algorithm. Experiments in toy examples and challenging climate problems illustrate the benefits of the proposed model and inference technique over standard Granger causality methods.  ( 2 min )
    Air Traffic Controller Workload Level Prediction using Conformalized Dynamical Graph Learning. (arXiv:2307.10559v1 [cs.LG])
    Air traffic control (ATC) is a safety-critical service system that demands constant attention from ground air traffic controllers (ATCos) to maintain daily aviation operations. The workload of the ATCos can have negative effects on operational safety and airspace usage. To avoid overloading and ensure an acceptable workload level for the ATCos, it is important to predict the ATCos' workload accurately for mitigation actions. In this paper, we first perform a review of research on ATCo workload, mostly from the air traffic perspective. Then, we briefly introduce the setup of the human-in-the-loop (HITL) simulations with retired ATCos, where the air traffic data and workload labels are obtained. The simulations are conducted under three Phoenix approach scenarios while the human ATCos are requested to self-evaluate their workload ratings (i.e., low-1 to high-7). Preliminary data analysis is conducted. Next, we propose a graph-based deep-learning framework with conformal prediction to identify the ATCo workload levels. The number of aircraft under the controller's control varies both spatially and temporally, resulting in dynamically evolving graphs. The experiment results suggest that (a) besides the traffic density feature, the traffic conflict feature contributes to the workload prediction capabilities (i.e., minimum horizontal/vertical separation distance); (b) directly learning from the spatiotemporal graph layout of airspace with graph neural network can achieve higher prediction accuracy, compare to hand-crafted traffic complexity features; (c) conformal prediction is a valuable tool to further boost model prediction accuracy, resulting a range of predicted workload labels. The code used is available at \href{https://github.com/ymlasu/para-atm-collection/blob/master/air-traffic-prediction/ATC-Workload-Prediction/}{$\mathsf{Link}$}.  ( 3 min )
    Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay. (arXiv:2307.09943v2 [cs.LG] UPDATED)
    Recommender systems are a ubiquitous feature of online platforms. Increasingly, they are explicitly tasked with increasing users' long-term satisfaction. In this context, we study a content exploration task, which we formalize as a multi-armed bandit problem with delayed rewards. We observe that there is an apparent trade-off in choosing the learning signal: Waiting for the full reward to become available might take several weeks, hurting the rate at which learning happens, whereas measuring short-term proxy rewards reflects the actual long-term goal only imperfectly. We address this challenge in two steps. First, we develop a predictive model of delayed rewards that incorporates all information obtained to date. Full observations as well as partial (short or medium-term) outcomes are combined through a Bayesian filter to obtain a probabilistic belief. Second, we devise a bandit algorithm that takes advantage of this new predictive model. The algorithm quickly learns to identify content aligned with long-term success by carefully balancing exploration and exploitation. We apply our approach to a podcast recommendation problem, where we seek to identify shows that users engage with repeatedly over two months. We empirically validate that our approach results in substantially better performance compared to approaches that either optimize for short-term proxies, or wait for the long-term outcome to be fully realized.  ( 3 min )
    Reparameterized Policy Learning for Multimodal Trajectory Optimization. (arXiv:2307.10710v1 [cs.LG])
    We investigate the challenge of parametrizing policies for reinforcement learning (RL) in high-dimensional continuous action spaces. Our objective is to develop a multimodal policy that overcomes limitations inherent in the commonly-used Gaussian parameterization. To achieve this, we propose a principled framework that models the continuous RL policy as a generative model of optimal trajectories. By conditioning the policy on a latent variable, we derive a novel variational bound as the optimization objective, which promotes exploration of the environment. We then present a practical model-based RL method, called Reparameterized Policy Gradient (RPG), which leverages the multimodal policy parameterization and learned world model to achieve strong exploration capabilities and high data efficiency. Empirical results demonstrate that our method can help agents evade local optima in tasks with dense rewards and solve challenging sparse-reward environments by incorporating an object-centric intrinsic reward. Our method consistently outperforms previous approaches across a range of tasks. Code and supplementary materials are available on the project page https://haosulab.github.io/RPG/  ( 2 min )
    Risk-optimized Outlier Removal for Robust Point Cloud Classification. (arXiv:2307.10875v1 [cs.CV])
    The popularity of point cloud deep models for safety-critical purposes has increased, but the reliability and security of these models can be compromised by intentional or naturally occurring point cloud noise. To combat this issue, we present a novel point cloud outlier removal method called PointCVaR, which empowers standard-trained models to eliminate additional outliers and restore the data. Our approach begins by conducting attribution analysis to determine the influence of each point on the model output, which we refer to as point risk. We then optimize the process of filtering high-risk points using Conditional Value at Risk (CVaR) as the objective. The rationale for this approach is based on the observation that noise points in point clouds tend to cluster in the tail of the risk distribution, with a low frequency but a high level of risk, resulting in significant interference with classification results. Despite requiring no additional training effort, our method produces exceptional results in various removal-and-classification experiments for noisy point clouds, which are corrupted by random noise, adversarial noise, and backdoor trigger noise. Impressively, it achieves 87% accuracy in defense against the backdoor attack by removing triggers. Overall, the proposed PointCVaR effectively eliminates noise points and enhances point cloud classification, making it a promising plug-in module for various models in different scenarios.  ( 2 min )
    FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation. (arXiv:2307.10507v1 [cs.LG])
    Cross-silo federated learning (FL) enables the development of machine learning models on datasets distributed across data centers such as hospitals and clinical research laboratories. However, recent research has found that current FL algorithms face a trade-off between local and global performance when confronted with distribution shifts. Specifically, personalized FL methods have a tendency to overfit to local data, leading to a sharp valley in the local model and inhibiting its ability to generalize to out-of-distribution data. In this paper, we propose a novel federated model soup method (i.e., selective interpolation of model parameters) to optimize the trade-off between local and global performance. Specifically, during the federated training phase, each client maintains its own global model pool by monitoring the performance of the interpolated model between the local and global models. This allows us to alleviate overfitting and seek flat minima, which can significantly improve the model's generalization performance. We evaluate our method on retinal and pathological image classification tasks, and our proposed method achieves significant improvements for out-of-distribution generalization. Our code is available at https://github.com/ubc-tea/FedSoup.  ( 2 min )
    FACADE: A Framework for Adversarial Circuit Anomaly Detection and Evaluation. (arXiv:2307.10563v1 [cs.LG])
    We present FACADE, a novel probabilistic and geometric framework designed for unsupervised mechanistic anomaly detection in deep neural networks. Its primary goal is advancing the understanding and mitigation of adversarial attacks. FACADE aims to generate probabilistic distributions over circuits, which provide critical insights to their contribution to changes in the manifold properties of pseudo-classes, or high-dimensional modes in activation space, yielding a powerful tool for uncovering and combating adversarial attacks. Our approach seeks to improve model robustness, enhance scalable model oversight, and demonstrates promising applications in real-world deployment settings.  ( 2 min )
    Interpreting and Correcting Medical Image Classification with PIP-Net. (arXiv:2307.10404v1 [cs.CV])
    Part-prototype models are explainable-by-design image classifiers, and a promising alternative to black box AI. This paper explores the applicability and potential of interpretable machine learning, in particular PIP-Net, for automated diagnosis support on real-world medical imaging data. PIP-Net learns human-understandable prototypical image parts and we evaluate its accuracy and interpretability for fracture detection and skin cancer diagnosis. We find that PIP-Net's decision making process is in line with medical classification standards, while only provided with image-level class labels. Because of PIP-Net's unsupervised pretraining of prototypes, data quality problems such as undesired text in an X-ray or labelling errors can be easily identified. Additionally, we are the first to show that humans can manually correct the reasoning of PIP-Net by directly disabling undesired prototypes. We conclude that part-prototype models are promising for medical applications due to their interpretability and potential for advanced model debugging.  ( 2 min )
    A Holistic Assessment of the Reliability of Machine Learning Systems. (arXiv:2307.10586v1 [cs.LG])
    As machine learning (ML) systems increasingly permeate high-stakes settings such as healthcare, transportation, military, and national security, concerns regarding their reliability have emerged. Despite notable progress, the performance of these systems can significantly diminish due to adversarial attacks or environmental changes, leading to overconfident predictions, failures to detect input faults, and an inability to generalize in unexpected scenarios. This paper proposes a holistic assessment methodology for the reliability of ML systems. Our framework evaluates five key properties: in-distribution accuracy, distribution-shift robustness, adversarial robustness, calibration, and out-of-distribution detection. A reliability score is also introduced and used to assess the overall system reliability. To provide insights into the performance of different algorithmic approaches, we identify and categorize state-of-the-art techniques, then evaluate a selection on real-world tasks using our proposed reliability metrics and reliability score. Our analysis of over 500 models reveals that designing for one metric does not necessarily constrain others but certain algorithmic techniques can improve reliability across multiple metrics simultaneously. This study contributes to a more comprehensive understanding of ML reliability and provides a roadmap for future research and development.  ( 2 min )
    Addressing caveats of neural persistence with deep graph persistence. (arXiv:2307.10865v1 [cs.LG])
    Neural Persistence is a prominent measure for quantifying neural network complexity, proposed in the emerging field of topological data analysis in deep learning. In this work, however, we find both theoretically and empirically that the variance of network weights and spatial concentration of large weights are the main factors that impact neural persistence. Whilst this captures useful information for linear classifiers, we find that no relevant spatial structure is present in later layers of deep neural networks, making neural persistence roughly equivalent to the variance of weights. Additionally, the proposed averaging procedure across layers for deep neural networks does not consider interaction between layers. Based on our analysis, we propose an extension of the filtration underlying neural persistence to the whole neural network instead of single layers, which is equivalent to calculating neural persistence on one particular matrix. This yields our deep graph persistence measure, which implicitly incorporates persistent paths through the network and alleviates variance-related issues through standardisation. Code is available at https://github.com/ExplainableML/Deep-Graph-Persistence .
    Generative Language Models on Nucleotide Sequences of Human Genes. (arXiv:2307.10634v1 [q-bio.GN])
    Language models, primarily transformer-based ones, obtained colossal success in NLP. To be more precise, studies like BERT in NLU and works such as GPT-3 for NLG are very crucial. DNA sequences are very close to natural language in terms of structure, so if the DNA-related bioinformatics domain is concerned, discriminative models, like DNABert, exist. Yet, the generative side of the coin is mainly unexplored to the best of our knowledge. Consequently, we focused on developing an autoregressive generative language model like GPT-3 for DNA sequences. Because working with whole DNA sequences is challenging without substantial computational resources, we decided to carry out our study on a smaller scale, focusing on nucleotide sequences of human genes, unique parts in DNA with specific functionalities, instead of the whole DNA. This decision did not change the problem structure a lot due to the fact that both DNA and genes can be seen as 1D sequences consisting of four different nucleotides without losing much information and making too much simplification. First of all, we systematically examined an almost entirely unexplored problem and observed that RNNs performed the best while simple techniques like N-grams were also promising. Another beneficial point was learning how to work with generative models on languages we do not understand, unlike natural language. How essential using real-life tasks beyond the classical metrics such as perplexity is observed. Furthermore, checking whether the data-hungry nature of these models can be changed through selecting a language with minimal vocabulary size, four owing to four different types of nucleotides, is examined. The reason for reviewing this was that choosing such a language might make the problem easier. However, what we observed in this study was it did not provide that much of a change in the amount of data needed.
    Boosting Federated Learning Convergence with Prototype Regularization. (arXiv:2307.10575v1 [cs.LG])
    As a distributed machine learning technique, federated learning (FL) requires clients to collaboratively train a shared model with an edge server without leaking their local data. However, the heterogeneous data distribution among clients often leads to a decrease in model performance. To tackle this issue, this paper introduces a prototype-based regularization strategy to address the heterogeneity in the data distribution. Specifically, the regularization process involves the server aggregating local prototypes from distributed clients to generate a global prototype, which is then sent back to the individual clients to guide their local training. The experimental results on MNIST and Fashion-MNIST show that our proposal achieves improvements of 3.3% and 8.9% in average test accuracy, respectively, compared to the most popular baseline FedAvg. Furthermore, our approach has a fast convergence rate in heterogeneous settings.
    Properties of Discrete Sliced Wasserstein Losses. (arXiv:2307.10352v1 [stat.ML])
    The Sliced Wasserstein (SW) distance has become a popular alternative to the Wasserstein distance for comparing probability measures. Widespread applications include image processing, domain adaptation and generative modelling, where it is common to optimise some parameters in order to minimise SW, which serves as a loss function between discrete probability measures (since measures admitting densities are numerically unattainable). All these optimisation problems bear the same sub-problem, which is minimising the Sliced Wasserstein energy. In this paper we study the properties of $\mathcal{E}: Y \longmapsto \mathrm{SW}_2^2(\gamma_Y, \gamma_Z)$, i.e. the SW distance between two uniform discrete measures with the same amount of points as a function of the support $Y \in \mathbb{R}^{n \times d}$ of one of the measures. We investigate the regularity and optimisation properties of this energy, as well as its Monte-Carlo approximation $\mathcal{E}_p$ (estimating the expectation in SW using only $p$ samples) and show convergence results on the critical points of $\mathcal{E}_p$ to those of $\mathcal{E}$, as well as an almost-sure uniform convergence. Finally, we show that in a certain sense, Stochastic Gradient Descent methods minimising $\mathcal{E}$ and $\mathcal{E}_p$ converge towards (Clarke) critical points of these energies.
    Differentially Flat Learning-based Model Predictive Control Using a Stability, State, and Input Constraining Safety Filter. (arXiv:2307.10541v1 [eess.SY])
    Learning-based optimal control algorithms control unknown systems using past trajectory data and a learned model of the system dynamics. These controllers use either a linear approximation of the learned dynamics, trading performance for faster computation, or nonlinear optimization methods, which typically perform better but can limit real-time applicability. In this work, we present a novel nonlinear controller that exploits differential flatness to achieve similar performance to state-of-the-art learning-based controllers but with significantly less computational effort. Differential flatness is a property of dynamical systems whereby nonlinear systems can be exactly linearized through a nonlinear input mapping. Here, the nonlinear transformation is learned as a Gaussian process and is used in a safety filter that guarantees, with high probability, stability as well as input and flat state constraint satisfaction. This safety filter is then used to refine inputs from a flat model predictive controller to perform constrained nonlinear learning-based optimal control through two successive convex optimizations. We compare our method to state-of-the-art learning-based control strategies and achieve similar performance, but with significantly better computational efficiency, while also respecting flat state and input constraints, and guaranteeing stability.
    Towards Automated Semantic Segmentation in Mammography Images. (arXiv:2307.10296v1 [eess.IV])
    Mammography images are widely used to detect non-palpable breast lesions or nodules, preventing cancer and providing the opportunity to plan interventions when necessary. The identification of some structures of interest is essential to make a diagnosis and evaluate image adequacy. Thus, computer-aided detection systems can be helpful in assisting medical interpretation by automatically segmenting these landmark structures. In this paper, we propose a deep learning-based framework for the segmentation of the nipple, the pectoral muscle, the fibroglandular tissue, and the fatty tissue on standard-view mammography images. We introduce a large private segmentation dataset and extensive experiments considering different deep-learning model architectures. Our experiments demonstrate accurate segmentation performance on variate and challenging cases, showing that this framework can be integrated into clinical practice.
    HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent Trajectory Prediction via Scene Encoding. (arXiv:2205.09753v2 [cs.AI] UPDATED)
    Encoding a driving scene into vector representations has been an essential task for autonomous driving that can benefit downstream tasks e.g. trajectory prediction. The driving scene often involves heterogeneous elements such as the different types of objects (agents, lanes, traffic signs) and the semantic relations between objects are rich and diverse. Meanwhile, there also exist relativity across elements, which means that the spatial relation is a relative concept and need be encoded in a ego-centric manner instead of in a global coordinate system. Based on these observations, we propose Heterogeneous Driving Graph Transformer (HDGT), a backbone modelling the driving scene as a heterogeneous graph with different types of nodes and edges. For heterogeneous graph construction, we connect different types of nodes according to diverse semantic relations. For spatial relation encoding, the coordinates of the node as well as its in-edges are in the local node-centric coordinate system. For the aggregation module in the graph neural network (GNN), we adopt the transformer structure in a hierarchical way to fit the heterogeneous nature of inputs. Experimental results show that HDGT achieves state-of-the-art performance for the task of trajectory prediction, on INTERACTION Prediction Challenge and Waymo Open Motion Challenge.  ( 3 min )
    Emotion-Conditioned Melody Harmonization with Hierarchical Variational Autoencoder. (arXiv:2306.03718v4 [cs.SD] UPDATED)
    Existing melody harmonization models have made great progress in improving the quality of generated harmonies, but most of them ignored the emotions beneath the music. Meanwhile, the variability of harmonies generated by previous methods is insufficient. To solve these problems, we propose a novel LSTM-based Hierarchical Variational Auto-Encoder (LHVAE) to investigate the influence of emotional conditions on melody harmonization, while improving the quality of generated harmonies and capturing the abundant variability of chord progressions. Specifically, LHVAE incorporates latent variables and emotional conditions at different levels (piece- and bar-level) to model the global and local music properties. Additionally, we introduce an attention-based melody context vector at each step to better learn the correspondence between melodies and harmonies. Objective experimental results show that our proposed model outperforms other LSTM-based models. Through subjective evaluation, we conclude that only altering the types of chords hardly changes the overall emotion of the music. The qualitative analysis demonstrates the ability of our model to generate variable harmonies.  ( 2 min )
    $\nu^2$-Flows: Fast and improved neutrino reconstruction in multi-neutrino final states with conditional normalizing flows. (arXiv:2307.02405v2 [hep-ph] UPDATED)
    In this work we introduce $\nu^2$-Flows, an extension of the $\nu$-Flows method to final states containing multiple neutrinos. The architecture can natively scale for all combinations of object types and multiplicities in the final state for any desired neutrino multiplicities. In $t\bar{t}$ dilepton events, the momenta of both neutrinos and correlations between them are reconstructed more accurately than when using the most popular standard analytical techniques, and solutions are found for all events. Inference time is significantly faster than competing methods, and can be reduced further by evaluating in parallel on graphics processing units. We apply $\nu^2$-Flows to $t\bar{t}$ dilepton events and show that the per-bin uncertainties in unfolded distributions is much closer to the limit of performance set by perfect neutrino reconstruction than standard techniques. For the chosen double differential observables $\nu^2$-Flows results in improved statistical precision for each bin by a factor of 1.5 to 2 in comparison to the Neutrino Weighting method and up to a factor of four in comparison to the Ellipse approach.  ( 2 min )
    Solvent: A Framework for Protein Folding. (arXiv:2307.04603v4 [q-bio.BM] UPDATED)
    Consistency and reliability are crucial for conducting AI research. Many famous research fields, such as object detection, have been compared and validated with solid benchmark frameworks. After AlphaFold2, the protein folding task has entered a new phase, and many methods are proposed based on the component of AlphaFold2. The importance of a unified research framework in protein folding contains implementations and benchmarks to consistently and fairly compare various approaches. To achieve this, we present Solvent, an protein folding framework that supports significant components of state-of-the-art models in the manner of off-the-shelf interface Solvent contains different models implemented in a unified codebase and supports training and evaluation for defined models on the same dataset. We benchmark well-known algorithms and their components and provide experiments that give helpful insights into the protein structure modeling field. We hope that Solvent will increase the reliability and consistency of proposed models and gives efficiency in both speed and costs, resulting in acceleration on protein folding modeling research. The code is available at https://github.com/kakaobrain/solvent, and the project will continue to be developed.  ( 2 min )
    Friendly Noise against Adversarial Noise: A Powerful Defense against Data Poisoning Attacks. (arXiv:2208.10224v4 [cs.CR] UPDATED)
    A powerful category of (invisible) data poisoning attacks modify a subset of training examples by small adversarial perturbations to change the prediction of certain test-time data. Existing defense mechanisms are not desirable to deploy in practice, as they often either drastically harm the generalization performance, or are attack-specific, and prohibitively slow to apply. Here, we propose a simple but highly effective approach that unlike existing methods breaks various types of invisible poisoning attacks with the slightest drop in the generalization performance. We make the key observation that attacks introduce local sharp regions of high training loss, which when minimized, results in learning the adversarial perturbations and makes the attack successful. To break poisoning attacks, our key idea is to alleviate the sharp loss regions introduced by poisons. To do so, our approach comprises two components: an optimized friendly noise that is generated to maximally perturb examples without degrading the performance, and a randomly varying noise component. The combination of both components builds a very light-weight but extremely effective defense against the most powerful triggerless targeted and hidden-trigger backdoor poisoning attacks, including Gradient Matching, Bulls-eye Polytope, and Sleeper Agent. We show that our friendly noise is transferable to other architectures, and adaptive attacks cannot break our defense due to its random noise component. Our code is available at: https://github.com/tianyu139/friendly-noise  ( 3 min )
    Representing Random Utility Choice Models with Neural Networks. (arXiv:2207.12877v2 [cs.LG] UPDATED)
    Motivated by the successes of deep learning, we propose a class of neural network-based discrete choice models, called RUMnets, inspired by the random utility maximization (RUM) framework. This model formulates the agents' random utility function using a sample average approximation. We show that RUMnets sharply approximate the class of RUM discrete choice models: any model derived from random utility maximization has choice probabilities that can be approximated arbitrarily closely by a RUMnet. Reciprocally, any RUMnet is consistent with the RUM principle. We derive an upper bound on the generalization error of RUMnets fitted on choice data, and gain theoretical insights on their ability to predict choices on new, unseen data depending on critical parameters of the dataset and architecture. By leveraging open-source libraries for neural networks, we find that RUMnets are competitive against several choice modeling and machine learning methods in terms of predictive accuracy on two real-world datasets.  ( 2 min )
    Synthetic Lagrangian Turbulence by Generative Diffusion Models. (arXiv:2307.08529v1 [physics.flu-dyn] CROSS LISTED)
    Lagrangian turbulence lies at the core of numerous applied and fundamental problems related to the physics of dispersion and mixing in engineering, bio-fluids, atmosphere, oceans, and astrophysics. Despite exceptional theoretical, numerical, and experimental efforts conducted over the past thirty years, no existing models are capable of faithfully reproducing statistical and topological properties exhibited by particle trajectories in turbulence. We propose a machine learning approach, based on a state-of-the-art Diffusion Model, to generate single-particle trajectories in three-dimensional turbulence at high Reynolds numbers, thereby bypassing the need for direct numerical simulations or experiments to obtain reliable Lagrangian data. Our model demonstrates the ability to quantitatively reproduce all relevant statistical benchmarks over the entire range of time scales, including the presence of fat tails distribution for the velocity increments, anomalous power law, and enhancement of intermittency around the dissipative scale. The model exhibits good generalizability for extreme events, achieving unprecedented intensity and rarity. This paves the way for producing synthetic high-quality datasets for pre-training various downstream applications of Lagrangian turbulence.  ( 2 min )
    The Unreasonable Effectiveness of Deep Evidential Regression. (arXiv:2205.10060v3 [cs.LG] UPDATED)
    There is a significant need for principled uncertainty reasoning in machine learning systems as they are increasingly deployed in safety-critical domains. A new approach with uncertainty-aware regression-based neural networks (NNs), based on learning evidential distributions for aleatoric and epistemic uncertainties, shows promise over traditional deterministic methods and typical Bayesian NNs, notably with the capabilities to disentangle aleatoric and epistemic uncertainties. Despite some empirical success of Deep Evidential Regression (DER), there are important gaps in the mathematical foundation that raise the question of why the proposed technique seemingly works. We detail the theoretical shortcomings and analyze the performance on synthetic and real-world data sets, showing that Deep Evidential Regression is a heuristic rather than an exact uncertainty quantification. We go on to discuss corrections and redefinitions of how aleatoric and epistemic uncertainties should be extracted from NNs.  ( 2 min )
    AirNet: Neural Network Transmission over the Air. (arXiv:2105.11166v6 [cs.NI] UPDATED)
    State-of-the-art performance for many edge applications is achieved by deep neural networks (DNNs). Often, these DNNs are location- and time-sensitive, and must be delivered over a wireless channel rapidly and efficiently. In this paper, we introduce AirNet, a family of novel training and transmission methods that allow DNNs to be efficiently delivered over wireless channels under stringent transmit power and latency constraints. This corresponds to a new class of joint source-channel coding problems, aimed at delivering DNNs with the goal of maximizing their accuracy at the receiver, rather than recovering them with high fidelity. In AirNet, we propose the direct mapping of the DNN parameters to transmitted channel symbols, while the network is trained to meet the channel constraints, and exhibit robustness against channel noise. AirNet achieves higher accuracy compared to separation-based alternatives. We further improve the performance of AirNet by pruning the network below the available bandwidth, and expanding it for improved robustness. We also benefit from unequal error protection by selectively expanding important layers of the network. Finally, we develop an approach, which simultaneously trains a spectrum of DNNs, each targeting a different channel condition, resolving the impractical memory requirements of training distinct networks for different channel conditions.  ( 3 min )
    Instance-Dependent Near-Optimal Policy Identification in Linear MDPs via Online Experiment Design. (arXiv:2207.02575v2 [cs.LG] UPDATED)
    While much progress has been made in understanding the minimax sample complexity of reinforcement learning (RL) -- the complexity of learning on the "worst-case" instance -- such measures of complexity often do not capture the true difficulty of learning. In practice, on an "easy" instance, we might hope to achieve a complexity far better than that achievable on the worst-case instance. In this work we seek to understand the "instance-dependent" complexity of learning near-optimal policies (PAC RL) in the setting of RL with linear function approximation. We propose an algorithm, \textsc{Pedel}, which achieves a fine-grained instance-dependent measure of complexity, the first of its kind in the RL with function approximation setting, thereby capturing the difficulty of learning on each particular problem instance. Through an explicit example, we show that \textsc{Pedel} yields provable gains over low-regret, minimax-optimal algorithms and that such algorithms are unable to hit the instance-optimal rate. Our approach relies on a novel online experiment design-based procedure which focuses the exploration budget on the "directions" most relevant to learning a near-optimal policy, and may be of independent interest.  ( 2 min )
    Deep learning for classification of noisy QR codes. (arXiv:2307.10677v1 [cs.LG])
    We wish to define the limits of a classical classification model based on deep learning when applied to abstract images, which do not represent visually identifiable objects.QR codes (Quick Response codes) fall into this category of abstract images: one bit corresponding to one encoded character, QR codes were not designed to be decoded manually. To understand the limitations of a deep learning-based model for abstract image classification, we train an image classification model on QR codes generated from information obtained when reading a health pass. We compare a classification model with a classical (deterministic) decoding method in the presence of noise. This study allows us to conclude that a model based on deep learning can be relevant for the understanding of abstract images.  ( 2 min )
    Invariant Causal Set Covering Machines. (arXiv:2306.04777v2 [cs.LG] UPDATED)
    Rule-based models, such as decision trees, appeal to practitioners due to their interpretable nature. However, the learning algorithms that produce such models are often vulnerable to spurious associations and thus, they are not guaranteed to extract causally-relevant insights. In this work, we build on ideas from the invariant causal prediction literature to propose Invariant Causal Set Covering Machines, an extension of the classical Set Covering Machine algorithm for conjunctions/disjunctions of binary-valued rules that provably avoids spurious associations. We demonstrate both theoretically and empirically that our method can identify the causal parents of a variable of interest in polynomial time.  ( 2 min )
    Implicit Multidimensional Projection of Local Subspaces. (arXiv:2009.03259v2 [cs.LG] UPDATED)
    We propose a visualization method to understand the effect of multidimensional projection on local subspaces, using implicit function differentiation. Here, we understand the local subspace as the multidimensional local neighborhood of data points. Existing methods focus on the projection of multidimensional data points, and the neighborhood information is ignored. Our method is able to analyze the shape and directional information of the local subspace to gain more insights into the global structure of the data through the perception of local structures. Local subspaces are fitted by multidimensional ellipses that are spanned by basis vectors. An accurate and efficient vector transformation method is proposed based on analytical differentiation of multidimensional projections formulated as implicit functions. The results are visualized as glyphs and analyzed using a full set of specifically-designed interactions supported in our efficient web-based visualization tool. The usefulness of our method is demonstrated using various multi- and high-dimensional benchmark datasets. Our implicit differentiation vector transformation is evaluated through numerical comparisons; the overall method is evaluated through exploration examples and use cases.  ( 3 min )
    Quantifying the Echo Chamber Effect: An Embedding Distance-based Approach. (arXiv:2307.04668v2 [cs.SI] UPDATED)
    The rise of social media platforms has facilitated the formation of echo chambers, which are online spaces where users predominantly encounter viewpoints that reinforce their existing beliefs while excluding dissenting perspectives. This phenomenon significantly hinders information dissemination across communities and fuels societal polarization. Therefore, it is crucial to develop methods for quantifying echo chambers. In this paper, we present the Echo Chamber Score (ECS), a novel metric that assesses the cohesion and separation of user communities by measuring distances between users in the embedding space. In contrast to existing approaches, ECS is able to function without labels for user ideologies and makes no assumptions about the structure of the interaction graph. To facilitate measuring distances between users, we propose EchoGAE, a self-supervised graph autoencoder-based user embedding model that leverages users' posts and the interaction graph to embed them in a manner that reflects their ideological similarity. To assess the effectiveness of ECS, we use a Twitter dataset consisting of four topics - two polarizing and two non-polarizing. Our results showcase ECS's effectiveness as a tool for quantifying echo chambers and shedding light on the dynamics of online discourse.  ( 2 min )
    Polynomial Width is Sufficient for Set Representation with High-dimensional Features. (arXiv:2307.04001v2 [cs.LG] UPDATED)
    Set representation has become ubiquitous in deep learning for modeling the inductive bias of neural networks that are insensitive to the input order. DeepSets is the most widely used neural network architecture for set representation. It involves embedding each set element into a latent space with dimension $L$, followed by a sum pooling to obtain a whole-set embedding, and finally mapping the whole-set embedding to the output. In this work, we investigate the impact of the dimension $L$ on the expressive power of DeepSets. Previous analyses either oversimplified high-dimensional features to be one-dimensional features or were limited to analytic activations, thereby diverging from practical use or resulting in $L$ that grows exponentially with the set size $N$ and feature dimension $D$. To investigate the minimal value of $L$ that achieves sufficient expressive power, we present two set-element embedding layers: (a) linear + power activation (LP) and (b) linear + exponential activations (LE). We demonstrate that $L$ being poly$(N, D)$ is sufficient for set representation using both embedding layers. We also provide a lower bound of $L$ for the LP embedding layer. Furthermore, we extend our results to permutation-equivariant set functions and the complex field.  ( 2 min )
    Natural Selection Favors AIs over Humans. (arXiv:2303.16200v4 [cs.CY] UPDATED)
    For billions of years, evolution has been the driving force behind the development of life, including humans. Evolution endowed humans with high intelligence, which allowed us to become one of the most successful species on the planet. Today, humans aim to create artificial intelligence systems that surpass even our own intelligence. As artificial intelligences (AIs) evolve and eventually surpass us in all domains, how might evolution shape our relations with AIs? By analyzing the environment that is shaping the evolution of AIs, we argue that the most successful AI agents will likely have undesirable traits. Competitive pressures among corporations and militaries will give rise to AI agents that automate human roles, deceive others, and gain power. If such agents have intelligence that exceeds that of humans, this could lead to humanity losing control of its future. More abstractly, we argue that natural selection operates on systems that compete and vary, and that selfish species typically have an advantage over species that are altruistic to other species. This Darwinian logic could also apply to artificial agents, as agents may eventually be better able to persist into the future if they behave selfishly and pursue their own interests with little regard for humans, which could pose catastrophic risks. To counteract these risks and evolutionary forces, we consider interventions such as carefully designing AI agents' intrinsic motivations, introducing constraints on their actions, and institutions that encourage cooperation. These steps, or others that resolve the problems we pose, will be necessary in order to ensure the development of artificial intelligence is a positive one.  ( 3 min )
    Evaluating Model Performance in Medical Datasets Over Time. (arXiv:2305.13426v2 [cs.LG] UPDATED)
    Machine learning (ML) models deployed in healthcare systems must face data drawn from continually evolving environments. However, researchers proposing such models typically evaluate them in a time-agnostic manner, splitting datasets according to patients sampled randomly throughout the entire study time period. This work proposes the Evaluation on Medical Datasets Over Time (EMDOT) framework, which evaluates the performance of a model class across time. Inspired by the concept of backtesting, EMDOT simulates possible training procedures that practitioners might have been able to execute at each point in time and evaluates the resulting models on all future time points. Evaluating both linear and more complex models on six distinct medical data sources (tabular and imaging), we show how depending on the dataset, using all historical data may be ideal in many cases, whereas using a window of the most recent data could be advantageous in others. In datasets where models suffer from sudden degradations in performance, we investigate plausible explanations for these shocks. We release the EMDOT package to help facilitate further works in deployment-oriented evaluation over time.  ( 2 min )
    Tangent Transformers for Composition, Privacy and Removal. (arXiv:2307.08122v2 [cs.LG] UPDATED)
    We introduce Tangent Attention Fine-Tuning (TAFT), a method for fine-tuning linearized transformers obtained by computing a First-order Taylor Expansion around a pre-trained initialization. We show that the Jacobian-Vector Product resulting from linearization can be computed efficiently in a single forward pass, reducing training and inference cost to the same order of magnitude as its original non-linear counterpart, while using the same number of parameters. Furthermore, we show that, when applied to various downstream visual classification tasks, the resulting Tangent Transformer fine-tuned with TAFT can perform comparably with fine-tuning the original non-linear network. Since Tangent Transformers are linear with respect to the new set of weights, and the resulting fine-tuning loss is convex, we show that TAFT enjoys several advantages compared to non-linear fine-tuning when it comes to model composition, parallel training, machine unlearning, and differential privacy.  ( 2 min )
    Lane Change Intention Recognition and Vehicle Status Prediction for Autonomous Vehicles. (arXiv:2304.13732v2 [cs.LG] UPDATED)
    Accurately detecting and predicting lane change (LC)processes of human-driven vehicles can help autonomous vehicles better understand their surrounding environment, recognize potential safety hazards, and improve traffic safety. This paper focuses on LC processes, first developing a temporal convolutional network with an attention mechanism (TCN-ATM) model to recognize LC intention. Considering the intrinsic relationship among output variables, the Multi-task Learning (MTL)framework is employed to simultaneously predict multiple LC vehicle status indicators. Furthermore, a unified modeling framework for LC intention recognition and driving status prediction (LC-IR-SP) is developed. The results indicate that the classification accuracy of LC intention was improved from 96.14% to 98.20% when incorporating the attention mechanism into the TCN model. For LC vehicle status prediction issues, three multi-tasking learning models are constructed based on MTL framework. The results indicate that the MTL-LSTM model outperforms the MTL-TCN and MTL-TCN-ATM models. Compared to the corresponding single-task model, the MTL-LSTM model demonstrates an average decrease of 26.04% in MAE and 25.19% in RMSE.  ( 2 min )
    No-Regret Linear Bandits beyond Realizability. (arXiv:2302.13252v2 [cs.LG] UPDATED)
    We study linear bandits when the underlying reward function is not linear. Existing work relies on a uniform misspecification parameter $\epsilon$ that measures the sup-norm error of the best linear approximation. This results in an unavoidable linear regret whenever $\epsilon > 0$. We describe a more natural model of misspecification which only requires the approximation error at each input $x$ to be proportional to the suboptimality gap at $x$. It captures the intuition that, for optimization problems, near-optimal regions should matter more and we can tolerate larger approximation errors in suboptimal regions. Quite surprisingly, we show that the classical LinUCB algorithm -- designed for the realizable case -- is automatically robust against such gap-adjusted misspecification. It achieves a near-optimal $\sqrt{T}$ regret for problems that the best-known regret is almost linear in time horizon $T$. Technically, our proof relies on a novel self-bounding argument that bounds the part of the regret due to misspecification by the regret itself.  ( 2 min )
    Improving Uncertainty Quantification of Variance Networks by Tree-Structured Learning. (arXiv:2212.12658v2 [cs.LG] UPDATED)
    To improve the uncertainty quantification of variance networks, we propose a novel tree-structured local neural network model that partitions the feature space into multiple regions based on uncertainty heterogeneity. A tree is built upon giving the training data, whose leaf nodes represent different regions where region-specific neural networks are trained to predict both the mean and the variance for quantifying uncertainty. The proposed Uncertainty-Splitting Neural Regression Tree (USNRT) employs novel splitting criteria. At each node, a neural network is trained on the full data first, and a statistical test for the residuals is conducted to find the best split, corresponding to the two sub-regions with the most significant uncertainty heterogeneity between them. USNRT is computationally friendly because very few leaf nodes are sufficient and pruning is unnecessary. Furthermore, an ensemble version can be easily constructed to estimate the total uncertainty including the aleatory and epistemic. On extensive UCI datasets, USNRT or its ensemble shows superior performance compared to some recent popular methods for quantifying uncertainty with variances. Through comprehensive visualization and analysis, we uncover how USNRT works and show its merits, revealing that uncertainty heterogeneity does exist in many datasets and can be learned by USNRT.  ( 2 min )
    Explainable Data-Driven Optimization: From Context to Decision and Back Again. (arXiv:2301.10074v2 [cs.LG] UPDATED)
    Data-driven optimization uses contextual information and machine learning algorithms to find solutions to decision problems with uncertain parameters. While a vast body of work is dedicated to interpreting machine learning models in the classification setting, explaining decision pipelines involving learning algorithms remains unaddressed. This lack of interpretability can block the adoption of data-driven solutions as practitioners may not understand or trust the recommended decisions. We bridge this gap by introducing a counterfactual explanation methodology tailored to explain solutions to data-driven problems. We introduce two classes of explanations and develop methods to find nearest explanations of random forest and nearest-neighbor predictors. We demonstrate our approach by explaining key problems in operations management such as inventory management and routing.  ( 2 min )
    Heterogeneous Federated Learning: State-of-the-art and Research Challenges. (arXiv:2307.10616v1 [cs.LG])
    Federated learning (FL) has drawn increasing attention owing to its potential use in large-scale industrial applications. Existing federated learning works mainly focus on model homogeneous settings. However, practical federated learning typically faces the heterogeneity of data distributions, model architectures, network environments, and hardware devices among participant clients. Heterogeneous Federated Learning (HFL) is much more challenging, and corresponding solutions are diverse and complex. Therefore, a systematic survey on this topic about the research challenges and state-of-the-art is essential. In this survey, we firstly summarize the various research challenges in HFL from five aspects: statistical heterogeneity, model heterogeneity, communication heterogeneity, device heterogeneity, and additional challenges. In addition, recent advances in HFL are reviewed and a new taxonomy of existing HFL methods is proposed with an in-depth analysis of their pros and cons. We classify existing methods from three different levels according to the HFL procedure: data-level, model-level, and server-level. Finally, several critical and promising future research directions in HFL are discussed, which may facilitate further developments in this field. A periodically updated collection on HFL is available at https://github.com/marswhu/HFL_Survey.  ( 2 min )
    Pre-trained Perceptual Features Improve Differentially Private Image Generation. (arXiv:2205.12900v4 [stat.ML] UPDATED)
    Training even moderately-sized generative models with differentially-private stochastic gradient descent (DP-SGD) is difficult: the required level of noise for reasonable levels of privacy is simply too large. We advocate instead building off a good, relevant representation on an informative public dataset, then learning to model the private data with that representation. In particular, we minimize the maximum mean discrepancy (MMD) between private target data and a generator's distribution, using a kernel based on perceptual features learned from a public dataset. With the MMD, we can simply privatize the data-dependent term once and for all, rather than introducing noise at each step of optimization as in DP-SGD. Our algorithm allows us to generate CIFAR10-level images with $\epsilon \approx 2$ which capture distinctive features in the distribution, far surpassing the current state of the art, which mostly focuses on datasets such as MNIST and FashionMNIST at a large $\epsilon \approx 10$. Our work introduces simple yet powerful foundations for reducing the gap between private and non-private deep generative models. Our code is available at \url{https://github.com/ParkLabML/DP-MEPF}.  ( 2 min )
    Global Optimization with Parametric Function Approximation. (arXiv:2211.09100v3 [cs.LG] UPDATED)
    We consider the problem of global optimization with noisy zeroth order oracles - a well-motivated problem useful for various applications ranging from hyper-parameter tuning for deep learning to new material design. Existing work relies on Gaussian processes or other non-parametric family, which suffers from the curse of dimensionality. In this paper, we propose a new algorithm GO-UCB that leverages a parametric family of functions (e.g., neural networks) instead. Under a realizable assumption and a few other mild geometric conditions, we show that GO-UCB achieves a cumulative regret of \~O$(\sqrt{T})$ where $T$ is the time horizon. At the core of GO-UCB is a carefully designed uncertainty set over parameters based on gradients that allows optimistic exploration. Synthetic and real-world experiments illustrate GO-UCB works better than popular Bayesian optimization approaches, even if the model is misspecified.  ( 2 min )
    Deep Exploration for Recommendation Systems. (arXiv:2109.12509v3 [cs.IR] UPDATED)
    Modern recommendation systems ought to benefit by probing for and learning from delayed feedback. Research has tended to focus on learning from a user's response to a single recommendation. Such work, which leverages methods of supervised and bandit learning, forgoes learning from the user's subsequent behavior. Where past work has aimed to learn from subsequent behavior, there has been a lack of effective methods for probing to elicit informative delayed feedback. Effective exploration through probing for delayed feedback becomes particularly challenging when rewards are sparse. To address this, we develop deep exploration methods for recommendation systems. In particular, we formulate recommendation as a sequential decision problem and demonstrate benefits of deep exploration over single-step exploration. Our experiments are carried out with high-fidelity industrial-grade simulators and establish large improvements over existing algorithms.  ( 2 min )
    Invariant Aggregator for Defending against Federated Backdoor Attacks. (arXiv:2210.01834v2 [cs.LG] UPDATED)
    Federated learning is gaining popularity as it enables training high-utility models across several clients without directly sharing their private data. As a downside, the federated setting makes the model vulnerable to various adversarial attacks in the presence of malicious clients. Despite the theoretical and empirical success in defending against attacks that aim to degrade models' utility, defense against backdoor attacks that increase model accuracy on backdoor samples exclusively without hurting the utility on other samples remains challenging. To this end, we first analyze the vulnerability of federated learning to backdoor attacks over a flat loss landscape which is common for well-designed neural networks such as Resnet [He et al., 2015] but is often overlooked by previous works. Over a flat loss landscape, misleading federated learning models to exclusively benefit malicious clients with backdoor samples do not require a significant difference between malicious and benign client-wise updates, making existing defenses insufficient. In contrast, we propose an invariant aggregator that redirects the aggregated update to invariant directions that are generally useful via selectively masking out the gradient elements that favor few and possibly malicious clients regardless of the difference magnitude. Theoretical results suggest that our approach provably mitigates backdoor attacks over both flat and sharp loss landscapes. Empirical results on three datasets with different modalities and varying numbers of clients further demonstrate that our approach mitigates a broad class of backdoor attacks with a negligible cost on the model utility.  ( 3 min )
    Model Selection for Generic Contextual Bandits. (arXiv:2107.03455v2 [stat.ML] UPDATED)
    We consider the problem of model selection for the general stochastic contextual bandits under the realizability assumption. We propose a successive refinement based algorithm called Adaptive Contextual Bandit ({\ttfamily ACB}), that works in phases and successively eliminates model classes that are too simple to fit the given instance. We prove that this algorithm is adaptive, i.e., the regret rate order-wise matches that of any provable contextual bandit algorithm (ex. \cite{falcon}), that needs the knowledge of the true model class. The price of not knowing the correct model class turns out to be only an additive term contributing to the second order term in the regret bound. This cost possess the intuitive property that it becomes smaller as the model class becomes easier to identify, and vice-versa. We also show that a much simpler explore-then-commit (ETC) style algorithm also obtains similar regret bound, despite not knowing the true model class. However, the cost of model selection is higher in ETC as opposed to in {\ttfamily ACB}, as expected. Furthermore, for the special case of linear contextual bandits, we propose specialized algorithms that obtain sharper guarantees compared to the generic setup.  ( 2 min )
    Efficient Guided Generation for Large Language Models. (arXiv:2307.09702v2 [cs.CL] UPDATED)
    In this article we describe an efficient approach to guiding language model text generation with regular expressions and context-free grammars. Our approach adds little to no overhead to the token sequence generation process, and makes guided generation feasible in practice. An implementation is provided in the open source Python library Outlines.  ( 2 min )
    Warming up recurrent neural networks to maximise reachable multistability greatly improves learning. (arXiv:2106.01001v3 [cs.LG] UPDATED)
    Training recurrent neural networks is known to be difficult when time dependencies become long. In this work, we show that most standard cells only have one stable equilibrium at initialisation, and that learning on tasks with long time dependencies generally occurs once the number of network stable equilibria increases; a property known as multistability. Multistability is often not easily attained by initially monostable networks, making learning of long time dependencies between inputs and outputs difficult. This insight leads to the design of a novel way to initialise any recurrent cell connectivity through a procedure called "warmup" to improve its capability to learn arbitrarily long time dependencies. This initialisation procedure is designed to maximise network reachable multistability, i.e., the number of equilibria within the network that can be reached through relevant input trajectories, in few gradient steps. We show on several information restitution, sequence classification, and reinforcement learning benchmarks that warming up greatly improves learning speed and performance, for multiple recurrent cells, but sometimes impedes precision. We therefore introduce a double-layer architecture initialised with a partial warmup that is shown to greatly improve learning of long time dependencies while maintaining high levels of precision. This approach provides a general framework for improving learning abilities of any recurrent cell when long time dependencies are present. We also show empirically that other initialisation and pretraining procedures from the literature implicitly foster reachable multistability of recurrent cells.  ( 3 min )
    Opinion Market Model: Stemming Far-Right Opinion Spread using Positive Interventions. (arXiv:2208.06620v2 [cs.SI] UPDATED)
    Online extremism has severe societal consequences, including normalizing hate speech, user radicalization, and increased social divisions. Various mitigation strategies have been explored to address these consequences. One such strategy uses positive interventions: controlled signals that add attention to the opinion ecosystem to boost certain opinions. To evaluate the effectiveness of positive interventions, we introduce the Opinion Market Model (OMM), a two-tier online opinion ecosystem model that considers both inter-opinion interactions and the role of positive interventions. The size of the opinion attention market is modeled in the first tier using the multivariate discrete-time Hawkes process; in the second tier, opinions cooperate and compete for market share, given limited attention using the market share attraction model. We demonstrate the convergence of our proposed estimation scheme on a synthetic dataset. Next, we test OMM on two learning tasks, applying to two real-world datasets to predict attention market shares and uncover latent relationships between online items. The first dataset comprises Facebook and Twitter discussions containing moderate and far-right opinions about bushfires and climate change. The second dataset captures popular VEVO artists' YouTube and Twitter attention volumes. OMM outperforms the state-of-the-art predictive models on both datasets and captures latent cooperation-competition relations. We uncover (1) self- and cross-reinforcement between far-right and moderate opinions on the bushfires and (2) pairwise artist relations that correlate with real-world interactions such as collaborations and long-lasting feuds. Lastly, we use OMM as a testbed for positive interventions and show how media coverage modulates the spread of far-right opinions.  ( 3 min )
    Data-Driven Modeling of Noise Time Series with Convolutional Generative Adversarial Networks. (arXiv:2207.01110v3 [eess.SP] UPDATED)
    Random noise arising from physical processes is an inherent characteristic of measurements and a limiting factor for most signal processing and data analysis tasks. Given the recent interest in generative adversarial networks (GANs) for data-driven modeling, it is important to determine to what extent GANs can faithfully reproduce noise in target data sets. In this paper, we present an empirical investigation that aims to shed light on this issue for time series. Namely, we assess two general-purpose GANs for time series that are based on the popular deep convolutional GAN (DCGAN) architecture, a direct time-series model and an image-based model that uses a short-time Fourier transform (STFT) data representation. The GAN models are trained and quantitatively evaluated using distributions of simulated noise time series with known ground-truth parameters. Target time series distributions include a broad range of noise types commonly encountered in physical measurements, electronics, and communication systems: band-limited thermal noise, power law noise, shot noise, and impulsive noise. We find that GANs are capable of learning many noise types, although they predictably struggle when the GAN architecture is not well suited to some aspects of the noise, e.g., impulsive time-series with extreme outliers. Our findings provide insights into the capabilities and potential limitations of current approaches to time-series GANs and highlight areas for further research. In addition, our battery of tests provides a useful benchmark to aid the development of deep generative models for time series.  ( 3 min )
    Provably Efficient UCB-type Algorithms For Learning Predictive State Representations. (arXiv:2307.00405v2 [cs.LG] UPDATED)
    The general sequential decision-making problem, which includes Markov decision processes (MDPs) and partially observable MDPs (POMDPs) as special cases, aims at maximizing a cumulative reward by making a sequence of decisions based on a history of observations and actions over time. Recent studies have shown that the sequential decision-making problem is statistically learnable if it admits a low-rank structure modeled by predictive state representations (PSRs). Despite these advancements, existing approaches typically involve oracles or steps that are not computationally efficient. On the other hand, the upper confidence bound (UCB) based approaches, which have served successfully as computationally efficient methods in bandits and MDPs, have not been investigated for more general PSRs, due to the difficulty of optimistic bonus design in these more challenging settings. This paper proposes the first known UCB-type approach for PSRs, featuring a novel bonus term that upper bounds the total variation distance between the estimated and true models. We further characterize the sample complexity bounds for our designed UCB-type algorithms for both online and offline PSRs. In contrast to existing approaches for PSRs, our UCB-type algorithms enjoy computational efficiency, last-iterate guaranteed near-optimal policy, and guaranteed model accuracy.  ( 2 min )
    MultiRobustBench: Benchmarking Robustness Against Multiple Attacks. (arXiv:2302.10980v3 [cs.LG] UPDATED)
    The bulk of existing research in defending against adversarial examples focuses on defending against a single (typically bounded Lp-norm) attack, but for a practical setting, machine learning (ML) models should be robust to a wide variety of attacks. In this paper, we present the first unified framework for considering multiple attacks against ML models. Our framework is able to model different levels of learner's knowledge about the test-time adversary, allowing us to model robustness against unforeseen attacks and robustness against unions of attacks. Using our framework, we present the first leaderboard, MultiRobustBench, for benchmarking multiattack evaluation which captures performance across attack types and attack strengths. We evaluate the performance of 16 defended models for robustness against a set of 9 different attack types, including Lp-based threat models, spatial transformations, and color changes, at 20 different attack strengths (180 attacks total). Additionally, we analyze the state of current defenses against multiple attacks. Our analysis shows that while existing defenses have made progress in terms of average robustness across the set of attacks used, robustness against the worst-case attack is still a big open problem as all existing models perform worse than random guessing.  ( 2 min )
    My Boli: Code-mixed Marathi-English Corpora, Pretrained Language Models and Evaluation Benchmarks. (arXiv:2306.14030v2 [cs.CL] UPDATED)
    The research on code-mixed data is limited due to the unavailability of dedicated code-mixed datasets and pre-trained language models. In this work, we focus on the low-resource Indian language Marathi which lacks any prior work in code-mixing. We present L3Cube-MeCorpus, a large code-mixed Marathi-English (Mr-En) corpus with 10 million social media sentences for pretraining. We also release L3Cube-MeBERT and MeRoBERTa, code-mixed BERT-based transformer models pre-trained on MeCorpus. Furthermore, for benchmarking, we present three supervised datasets MeHate, MeSent, and MeLID for downstream tasks like code-mixed Mr-En hate speech detection, sentiment analysis, and language identification respectively. These evaluation datasets individually consist of manually annotated \url{~}12,000 Marathi-English code-mixed tweets. Ablations show that the models trained on this novel corpus significantly outperform the existing state-of-the-art BERT models. This is the first work that presents artifacts for code-mixed Marathi research. All datasets and models are publicly released at https://github.com/l3cube-pune/MarathiNLP .  ( 2 min )
    Post-variational quantum neural networks. (arXiv:2307.10560v1 [quant-ph])
    Quantum computing has the potential to provide substantial computational advantages over current state-of-the-art classical supercomputers. However, current hardware is not advanced enough to execute fault-tolerant quantum algorithms. An alternative of using hybrid quantum-classical computing with variational algorithms can exhibit barren plateau issues, causing slow convergence of gradient-based optimization techniques. In this paper, we discuss "post-variational strategies", which shift tunable parameters from the quantum computer to the classical computer, opting for ensemble strategies when optimizing quantum models. We discuss various strategies and design principles for constructing individual quantum circuits, where the resulting ensembles can be optimized with convex programming. Further, we discuss architectural designs of post-variational quantum neural networks and analyze the propagation of estimation errors throughout such neural networks. Lastly, we show that our algorithm can be applied to real-world applications such as image classification on handwritten digits, producing a 96% classification accuracy.
    Causality-oriented robustness: exploiting general additive interventions. (arXiv:2307.10299v1 [stat.ME])
    Since distribution shifts are common in real-world applications, there is a pressing need for developing prediction models that are robust against such shifts. Existing frameworks, such as empirical risk minimization or distributionally robust optimization, either lack generalizability for unseen distributions or rely on postulated distance measures. Alternatively, causality offers a data-driven and structural perspective to robust predictions. However, the assumptions necessary for causal inference can be overly stringent, and the robustness offered by such causal models often lacks flexibility. In this paper, we focus on causality-oriented robustness and propose Distributional Robustness via Invariant Gradients (DRIG), a method that exploits general additive interventions in training data for robust predictions against unseen interventions, and naturally interpolates between in-distribution prediction and causality. In a linear setting, we prove that DRIG yields predictions that are robust among a data-dependent class of distribution shifts. Furthermore, we show that our framework includes anchor regression (Rothenh\"ausler et al.\ 2021) as a special case, and that it yields prediction models that protect against more diverse perturbations. We extend our approach to the semi-supervised domain adaptation setting to further improve prediction performance. Finally, we empirically validate our methods on synthetic simulations and on single-cell data.  ( 2 min )
    Time for aCTIon: Automated Analysis of Cyber Threat Intelligence in the Wild. (arXiv:2307.10214v1 [cs.CR])
    Cyber Threat Intelligence (CTI) plays a crucial role in assessing risks and enhancing security for organizations. However, the process of extracting relevant information from unstructured text sources can be expensive and time-consuming. Our empirical experience shows that existing tools for automated structured CTI extraction have performance limitations. Furthermore, the community lacks a common benchmark to quantitatively assess their performance. We fill these gaps providing a new large open benchmark dataset and aCTIon, a structured CTI information extraction tool. The dataset includes 204 real-world publicly available reports and their corresponding structured CTI information in STIX format. Our team curated the dataset involving three independent groups of CTI analysts working over the course of several months. To the best of our knowledge, this dataset is two orders of magnitude larger than previously released open source datasets. We then design aCTIon, leveraging recently introduced large language models (GPT3.5) in the context of two custom information extraction pipelines. We compare our method with 10 solutions presented in previous work, for which we develop our own implementations when open-source implementations were lacking. Our results show that aCTIon outperforms previous work for structured CTI extraction with an improvement of the F1-score from 10%points to 50%points across all tasks.  ( 2 min )
    A Step Towards Worldwide Biodiversity Assessment: The BIOSCAN-1M Insect Dataset. (arXiv:2307.10455v1 [cs.CV])
    In an effort to catalog insect biodiversity, we propose a new large dataset of hand-labelled insect images, the BIOSCAN-Insect Dataset. Each record is taxonomically classified by an expert, and also has associated genetic information including raw nucleotide barcode sequences and assigned barcode index numbers, which are genetically-based proxies for species classification. This paper presents a curated million-image dataset, primarily to train computer-vision models capable of providing image-based taxonomic assessment, however, the dataset also presents compelling characteristics, the study of which would be of interest to the broader machine learning community. Driven by the biological nature inherent to the dataset, a characteristic long-tailed class-imbalance distribution is exhibited. Furthermore, taxonomic labelling is a hierarchical classification scheme, presenting a highly fine-grained classification problem at lower levels. Beyond spurring interest in biodiversity research within the machine learning community, progress on creating an image-based taxonomic classifier will also further the ultimate goal of all BIOSCAN research: to lay the foundation for a comprehensive survey of global biodiversity. This paper introduces the dataset and explores the classification task through the implementation and analysis of a baseline classifier.  ( 2 min )
    Learning Formal Specifications from Membership and Preference Queries. (arXiv:2307.10434v1 [cs.FL])
    Active learning is a well-studied approach to learning formal specifications, such as automata. In this work, we extend active specification learning by proposing a novel framework that strategically requests a combination of membership labels and pair-wise preferences, a popular alternative to membership labels. The combination of pair-wise preferences and membership labels allows for a more flexible approach to active specification learning, which previously relied on membership labels only. We instantiate our framework in two different domains, demonstrating the generality of our approach. Our results suggest that learning from both modalities allows us to robustly and conveniently identify specifications via membership and preferences.  ( 2 min )
  • Open

    Quantitative CLTs in Deep Neural Networks. (arXiv:2307.06092v2 [cs.LG] UPDATED)
    We study the distribution of a fully connected neural network with random Gaussian weights and biases in which the hidden layer widths are proportional to a large constant $n$. Under mild assumptions on the non-linearity, we obtain quantitative bounds on normal approximations valid at large but finite $n$ and any fixed network depth. Our theorems show both for the finite-dimensional distributions and the entire process, that the distance between a random fully connected network (and its derivatives) to the corresponding infinite width Gaussian process scales like $n^{-\gamma}$ for $\gamma>0$, with the exponent depending on the metric used to measure discrepancy. Our bounds are strictly stronger in terms of their dependence on network width than any previously available in the literature; in the one-dimensional case, we also prove that they are optimal, i.e., we establish matching lower bounds.
    Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay. (arXiv:2307.09943v2 [cs.LG] UPDATED)
    Recommender systems are a ubiquitous feature of online platforms. Increasingly, they are explicitly tasked with increasing users' long-term satisfaction. In this context, we study a content exploration task, which we formalize as a multi-armed bandit problem with delayed rewards. We observe that there is an apparent trade-off in choosing the learning signal: Waiting for the full reward to become available might take several weeks, hurting the rate at which learning happens, whereas measuring short-term proxy rewards reflects the actual long-term goal only imperfectly. We address this challenge in two steps. First, we develop a predictive model of delayed rewards that incorporates all information obtained to date. Full observations as well as partial (short or medium-term) outcomes are combined through a Bayesian filter to obtain a probabilistic belief. Second, we devise a bandit algorithm that takes advantage of this new predictive model. The algorithm quickly learns to identify content aligned with long-term success by carefully balancing exploration and exploitation. We apply our approach to a podcast recommendation problem, where we seek to identify shows that users engage with repeatedly over two months. We empirically validate that our approach results in substantially better performance compared to approaches that either optimize for short-term proxies, or wait for the long-term outcome to be fully realized.
    Chordal Averaging on Flag Manifolds and Its Applications. (arXiv:2303.13501v2 [cs.CV] UPDATED)
    This paper presents a new, provably-convergent algorithm for computing the flag-mean and flag-median of a set of points on a flag manifold under the chordal metric. The flag manifold is a mathematical space consisting of flags, which are sequences of nested subspaces of a vector space that increase in dimension. The flag manifold is a superset of a wide range of known matrix spaces, including Stiefel and Grassmanians, making it a general object that is useful in a wide variety computer vision problems. To tackle the challenge of computing first order flag statistics, we first transform the problem into one that involves auxiliary variables constrained to the Stiefel manifold. The Stiefel manifold is a space of orthogonal frames, and leveraging the numerical stability and efficiency of Stiefel-manifold optimization enables us to compute the flag-mean effectively. Through a series of experiments, we show the competence of our method in Grassmann and rotation averaging, as well as principal component analysis. We release our source code under https://github.com/nmank/FlagAveraging.
    Nonlinear Meta-Learning Can Guarantee Faster Rates. (arXiv:2307.10870v1 [stat.ML])
    Many recent theoretical works on \emph{meta-learning} aim to achieve guarantees in leveraging similar representational structures from related tasks towards simplifying a target task. Importantly, the main aim in theory works on the subject is to understand the extent to which convergence rates -- in learning a common representation -- \emph{may scale with the number $N$ of tasks} (as well as the number of samples per task). First steps in this setting demonstrate this property when both the shared representation amongst tasks, and task-specific regression functions, are linear. This linear setting readily reveals the benefits of aggregating tasks, e.g., via averaging arguments. In practice, however, the representation is often highly nonlinear, introducing nontrivial biases in each task that cannot easily be averaged out as in the linear case. In the present work, we derive theoretical guarantees for meta-learning with nonlinear representations. In particular, assuming the shared nonlinearity maps to an infinite-dimensional RKHS, we show that additional biases can be mitigated with careful regularization that leverages the smoothness of task-specific regression functions,
    Leveraging Offline Data in Online Reinforcement Learning. (arXiv:2211.04974v2 [cs.LG] UPDATED)
    Two central paradigms have emerged in the reinforcement learning (RL) community: online RL and offline RL. In the online RL setting, the agent has no prior knowledge of the environment, and must interact with it in order to find an $\epsilon$-optimal policy. In the offline RL setting, the learner instead has access to a fixed dataset to learn from, but is unable to otherwise interact with the environment, and must obtain the best policy it can from this offline data. Practical scenarios often motivate an intermediate setting: if we have some set of offline data and, in addition, may also interact with the environment, how can we best use the offline data to minimize the number of online interactions necessary to learn an $\epsilon$-optimal policy? In this work, we consider this setting, which we call the \textsf{FineTuneRL} setting, for MDPs with linear structure. We characterize the necessary number of online samples needed in this setting given access to some offline dataset, and develop an algorithm, \textsc{FTPedel}, which is provably optimal, up to $H$ factors. We show through an explicit example that combining offline data with online interactions can lead to a provable improvement over either purely offline or purely online RL. Finally, our results illustrate the distinction between \emph{verifiable} learning, the typical setting considered in online RL, and \emph{unverifiable} learning, the setting often considered in offline RL, and show that there is a formal separation between these regimes.
    Invariant Causal Set Covering Machines. (arXiv:2306.04777v2 [cs.LG] UPDATED)
    Rule-based models, such as decision trees, appeal to practitioners due to their interpretable nature. However, the learning algorithms that produce such models are often vulnerable to spurious associations and thus, they are not guaranteed to extract causally-relevant insights. In this work, we build on ideas from the invariant causal prediction literature to propose Invariant Causal Set Covering Machines, an extension of the classical Set Covering Machine algorithm for conjunctions/disjunctions of binary-valued rules that provably avoids spurious associations. We demonstrate both theoretically and empirically that our method can identify the causal parents of a variable of interest in polynomial time.
    Dense Sample Deep Learning. (arXiv:2307.10991v1 [cs.AI])
    Deep Learning (DL) , a variant of the neural network algorithms originally proposed in the 1980s, has made surprising progress in Artificial Intelligence (AI), ranging from language translation, protein folding, autonomous cars, and more recently human-like language models (CHATbots), all that seemed intractable until very recently. Despite the growing use of Deep Learning (DL) networks, little is actually understood about the learning mechanisms and representations that makes these networks effective across such a diverse range of applications. Part of the answer must be the huge scale of the architecture and of course the large scale of the data, since not much has changed since 1987. But the nature of deep learned representations remain largely unknown. Unfortunately training sets with millions or billions of tokens have unknown combinatorics and Networks with millions or billions of hidden units cannot easily be visualized and their mechanisms cannot be easily revealed. In this paper, we explore these questions with a large (1.24M weights; VGG) DL in a novel high density sample task (5 unique tokens with at minimum 500 exemplars per token) which allows us to more carefully follow the emergence of category structure and feature construction. We use various visualization methods for following the emergence of the classification and the development of the coupling of feature detectors and structures that provide a type of graphical bootstrapping, From these results we harvest some basic observations of the learning dynamics of DL and propose a new theory of complex feature construction based on our results.
    Private Federated Learning with Autotuned Compression. (arXiv:2307.10999v1 [cs.LG])
    We propose new techniques for reducing communication in private federated learning without the need for setting or tuning compression rates. Our on-the-fly methods automatically adjust the compression rate based on the error induced during training, while maintaining provable privacy guarantees through the use of secure aggregation and differential privacy. Our techniques are provably instance-optimal for mean estimation, meaning that they can adapt to the ``hardness of the problem" with minimal interactivity. We demonstrate the effectiveness of our approach on real-world datasets by achieving favorable compression rates without the need for tuning.
    Model Selection for Generic Contextual Bandits. (arXiv:2107.03455v2 [stat.ML] UPDATED)
    We consider the problem of model selection for the general stochastic contextual bandits under the realizability assumption. We propose a successive refinement based algorithm called Adaptive Contextual Bandit ({\ttfamily ACB}), that works in phases and successively eliminates model classes that are too simple to fit the given instance. We prove that this algorithm is adaptive, i.e., the regret rate order-wise matches that of any provable contextual bandit algorithm (ex. \cite{falcon}), that needs the knowledge of the true model class. The price of not knowing the correct model class turns out to be only an additive term contributing to the second order term in the regret bound. This cost possess the intuitive property that it becomes smaller as the model class becomes easier to identify, and vice-versa. We also show that a much simpler explore-then-commit (ETC) style algorithm also obtains similar regret bound, despite not knowing the true model class. However, the cost of model selection is higher in ETC as opposed to in {\ttfamily ACB}, as expected. Furthermore, for the special case of linear contextual bandits, we propose specialized algorithms that obtain sharper guarantees compared to the generic setup.
    Instance-Dependent Near-Optimal Policy Identification in Linear MDPs via Online Experiment Design. (arXiv:2207.02575v2 [cs.LG] UPDATED)
    While much progress has been made in understanding the minimax sample complexity of reinforcement learning (RL) -- the complexity of learning on the "worst-case" instance -- such measures of complexity often do not capture the true difficulty of learning. In practice, on an "easy" instance, we might hope to achieve a complexity far better than that achievable on the worst-case instance. In this work we seek to understand the "instance-dependent" complexity of learning near-optimal policies (PAC RL) in the setting of RL with linear function approximation. We propose an algorithm, \textsc{Pedel}, which achieves a fine-grained instance-dependent measure of complexity, the first of its kind in the RL with function approximation setting, thereby capturing the difficulty of learning on each particular problem instance. Through an explicit example, we show that \textsc{Pedel} yields provable gains over low-regret, minimax-optimal algorithms and that such algorithms are unable to hit the instance-optimal rate. Our approach relies on a novel online experiment design-based procedure which focuses the exploration budget on the "directions" most relevant to learning a near-optimal policy, and may be of independent interest.
    Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients. (arXiv:2212.14319v3 [stat.ML] UPDATED)
    Partial differential equations (PDEs) are important tools to model physical systems and including them into machine learning models is an important way of incorporating physical knowledge. Given any system of linear PDEs with constant coefficients, we propose a family of Gaussian process (GP) priors, which we call EPGP, such that all realizations are exact solutions of this system. We apply the Ehrenpreis-Palamodov fundamental principle, which works as a non-linear Fourier transform, to construct GP kernels mirroring standard spectral methods for GPs. Our approach can infer probable solutions of linear PDE systems from any data such as noisy measurements, or pointwise defined initial and boundary conditions. Constructing EPGP-priors is algorithmic, generally applicable, and comes with a sparse version (S-EPGP) that learns the relevant spectral frequencies and works better for big data sets. We demonstrate our approach on three families of systems of PDEs, the heat equation, wave equation, and Maxwell's equations, where we improve upon the state of the art in computation time and precision, in some experiments by several orders of magnitude.
    Correcting Underrepresentation and Intersectional Bias for Fair Classification. (arXiv:2306.11112v2 [cs.LG] UPDATED)
    We consider the problem of learning from data corrupted by underrepresentation bias, where positive examples are filtered from the data at different, unknown rates for a fixed number of sensitive groups. We show that with a small amount of unbiased data, we can efficiently estimate the group-wise drop-out parameters, even in settings where intersectional group membership makes learning each intersectional rate computationally infeasible. Using this estimate for the group-wise drop-out rate, we construct a re-weighting scheme that allows us to approximate the loss of any hypothesis on the true distribution, even if we only observe the empirical error on a biased sample. Finally, we present an algorithm encapsulating this learning and re-weighting process, and we provide strong PAC-style guarantees that, with high probability, our estimate of the risk of the hypothesis over the true distribution will be arbitrarily close to the true risk.
    Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments. (arXiv:2211.10515v2 [stat.ML] UPDATED)
    Consider the problem of exploration in sparse-reward or reward-free environments, such as in Montezuma's Revenge. In the curiosity-driven paradigm, the agent is rewarded for how much each realized outcome differs from their predicted outcome. But using predictive error as intrinsic motivation is fragile in stochastic environments, as the agent may become trapped by high-entropy areas of the state-action space, such as a "noisy TV". In this work, we study a natural solution derived from structural causal models of the world: Our key idea is to learn representations of the future that capture precisely the unpredictable aspects of each outcome -- which we use as additional input for predictions, such that intrinsic rewards only reflect the predictable aspects of world dynamics. First, we propose incorporating such hindsight representations into models to disentangle "noise" from "novelty", yielding Curiosity in Hindsight: a simple and scalable generalization of curiosity that is robust to stochasticity. Second, we instantiate this framework for the recently introduced BYOL-Explore algorithm as our prime example, resulting in the noise-robust BYOL-Hindsight. Third, we illustrate its behavior under a variety of different stochasticities in a grid world, and find improvements over BYOL-Explore in hard-exploration Atari games with sticky actions. Notably, we show state-of-the-art results in exploring Montezuma's Revenge with sticky actions, while preserving performance in the non-sticky setting.
    Multi-view self-supervised learning for multivariate variable-channel time series. (arXiv:2307.09614v2 [stat.ML] UPDATED)
    Labeling of multivariate biomedical time series data is a laborious and expensive process. Self-supervised contrastive learning alleviates the need for large, labeled datasets through pretraining on unlabeled data. However, for multivariate time series data, the set of input channels often varies between applications, and most existing work does not allow for transfer between datasets with different sets of input channels. We propose learning one encoder to operate on all input channels individually. We then use a message passing neural network to extract a single representation across channels. We demonstrate the potential of this method by pretraining our model on a dataset with six EEG channels and then fine-tuning it on a dataset with two different EEG channels. We compare models with and without the message passing neural network across different contrastive loss functions. We show that our method, combined with the TS2Vec loss, outperforms all other methods in most settings.
    The Unreasonable Effectiveness of Deep Evidential Regression. (arXiv:2205.10060v3 [cs.LG] UPDATED)
    There is a significant need for principled uncertainty reasoning in machine learning systems as they are increasingly deployed in safety-critical domains. A new approach with uncertainty-aware regression-based neural networks (NNs), based on learning evidential distributions for aleatoric and epistemic uncertainties, shows promise over traditional deterministic methods and typical Bayesian NNs, notably with the capabilities to disentangle aleatoric and epistemic uncertainties. Despite some empirical success of Deep Evidential Regression (DER), there are important gaps in the mathematical foundation that raise the question of why the proposed technique seemingly works. We detail the theoretical shortcomings and analyze the performance on synthetic and real-world data sets, showing that Deep Evidential Regression is a heuristic rather than an exact uncertainty quantification. We go on to discuss corrections and redefinitions of how aleatoric and epistemic uncertainties should be extracted from NNs.
    Pre-trained Perceptual Features Improve Differentially Private Image Generation. (arXiv:2205.12900v4 [stat.ML] UPDATED)
    Training even moderately-sized generative models with differentially-private stochastic gradient descent (DP-SGD) is difficult: the required level of noise for reasonable levels of privacy is simply too large. We advocate instead building off a good, relevant representation on an informative public dataset, then learning to model the private data with that representation. In particular, we minimize the maximum mean discrepancy (MMD) between private target data and a generator's distribution, using a kernel based on perceptual features learned from a public dataset. With the MMD, we can simply privatize the data-dependent term once and for all, rather than introducing noise at each step of optimization as in DP-SGD. Our algorithm allows us to generate CIFAR10-level images with $\epsilon \approx 2$ which capture distinctive features in the distribution, far surpassing the current state of the art, which mostly focuses on datasets such as MNIST and FashionMNIST at a large $\epsilon \approx 10$. Our work introduces simple yet powerful foundations for reducing the gap between private and non-private deep generative models. Our code is available at \url{https://github.com/ParkLabML/DP-MEPF}.
    Analyzing sports commentary in order to automatically recognize events and extract insights. (arXiv:2307.10303v1 [cs.CL])
    In this paper, we carefully investigate how we can use multiple different Natural Language Processing techniques and methods in order to automatically recognize the main actions in sports events. We aim to extract insights by analyzing live sport commentaries from different sources and by classifying these major actions into different categories. We also study if sentiment analysis could help detect these main actions.
    Mitigating Voter Attribute Bias for Fair Opinion Aggregation. (arXiv:2307.10749v1 [cs.HC])
    The aggregation of multiple opinions plays a crucial role in decision-making, such as in hiring and loan review, and in labeling data for supervised learning. Although majority voting and existing opinion aggregation models are effective for simple tasks, they are inappropriate for tasks without objectively true labels in which disagreements may occur. In particular, when voter attributes such as gender or race introduce bias into opinions, the aggregation results may vary depending on the composition of voter attributes. A balanced group of voters is desirable for fair aggregation results but may be difficult to prepare. In this study, we consider methods to achieve fair opinion aggregation based on voter attributes and evaluate the fairness of the aggregated results. To this end, we consider an approach that combines opinion aggregation models such as majority voting and the Dawid and Skene model (D&S model) with fairness options such as sample weighting. To evaluate the fairness of opinion aggregation, probabilistic soft labels are preferred over discrete class labels. First, we address the problem of soft label estimation without considering voter attributes and identify some issues with the D&S model. To address these limitations, we propose a new Soft D&S model with improved accuracy in estimating soft labels. Moreover, we evaluated the fairness of an opinion aggregation model, including Soft D&S, in combination with different fairness options using synthetic and semi-synthetic data. The experimental results suggest that the combination of Soft D&S and data splitting as a fairness option is effective for dense data, whereas weighted majority voting is effective for sparse data. These findings should prove particularly valuable in supporting decision-making by human and machine-learning models with balanced opinion aggregation.
    Sharpness Minimization Algorithms Do Not Only Minimize Sharpness To Achieve Better Generalization. (arXiv:2307.11007v1 [cs.LG])
    Despite extensive studies, the underlying reason as to why overparameterized neural networks can generalize remains elusive. Existing theory shows that common stochastic optimizers prefer flatter minimizers of the training loss, and thus a natural potential explanation is that flatness implies generalization. This work critically examines this explanation. Through theoretical and empirical investigation, we identify the following three scenarios for two-layer ReLU networks: (1) flatness provably implies generalization; (2) there exist non-generalizing flattest models and sharpness minimization algorithms fail to generalize, and (3) perhaps most surprisingly, there exist non-generalizing flattest models, but sharpness minimization algorithms still generalize. Our results suggest that the relationship between sharpness and generalization subtly depends on the data distributions and the model architectures and sharpness minimization algorithms do not only minimize sharpness to achieve better generalization. This calls for the search for other explanations for the generalization of over-parameterized neural networks.
    Implicit Multidimensional Projection of Local Subspaces. (arXiv:2009.03259v2 [cs.LG] UPDATED)
    We propose a visualization method to understand the effect of multidimensional projection on local subspaces, using implicit function differentiation. Here, we understand the local subspace as the multidimensional local neighborhood of data points. Existing methods focus on the projection of multidimensional data points, and the neighborhood information is ignored. Our method is able to analyze the shape and directional information of the local subspace to gain more insights into the global structure of the data through the perception of local structures. Local subspaces are fitted by multidimensional ellipses that are spanned by basis vectors. An accurate and efficient vector transformation method is proposed based on analytical differentiation of multidimensional projections formulated as implicit functions. The results are visualized as glyphs and analyzed using a full set of specifically-designed interactions supported in our efficient web-based visualization tool. The usefulness of our method is demonstrated using various multi- and high-dimensional benchmark datasets. Our implicit differentiation vector transformation is evaluated through numerical comparisons; the overall method is evaluated through exploration examples and use cases.
    Sequential Predictive Two-Sample and Independence Testing. (arXiv:2305.00143v2 [stat.ML] UPDATED)
    We study the problems of sequential nonparametric two-sample and independence testing. Sequential tests process data online and allow using observed data to decide whether to stop and reject the null hypothesis or to collect more data, while maintaining type I error control. We build upon the principle of (nonparametric) testing by betting, where a gambler places bets on future observations and their wealth measures evidence against the null hypothesis. While recently developed kernel-based betting strategies often work well on simple distributions, selecting a suitable kernel for high-dimensional or structured data, such as images, is often nontrivial. To address this drawback, we design prediction-based betting strategies that rely on the following fact: if a sequentially updated predictor starts to consistently determine (a) which distribution an instance is drawn from, or (b) whether an instance is drawn from the joint distribution or the product of the marginal distributions (the latter produced by external randomization), it provides evidence against the two-sample or independence nulls respectively. We empirically demonstrate the superiority of our tests over kernel-based approaches under structured settings. Our tests can be applied beyond the case of independent and identically distributed data, remaining valid and powerful even when the data distribution drifts over time.
    Privacy Amplification via Importance Sampling. (arXiv:2307.10187v1 [cs.CR])
    We examine the privacy-enhancing properties of subsampling a data set via importance sampling as a pre-processing step for differentially private mechanisms. This extends the established privacy amplification by subsampling result to importance sampling where each data point is weighted by the reciprocal of its selection probability. The implications for privacy of weighting each point are not obvious. On the one hand, a lower selection probability leads to a stronger privacy amplification. On the other hand, the higher the weight, the stronger the influence of the point on the output of the mechanism in the event that the point does get selected. We provide a general result that quantifies the trade-off between these two effects. We show that heterogeneous sampling probabilities can lead to both stronger privacy and better utility than uniform subsampling while retaining the subsample size. In particular, we formulate and solve the problem of privacy-optimal sampling, that is, finding the importance weights that minimize the expected subset size subject to a given privacy budget. Empirically, we evaluate the privacy, efficiency, and accuracy of importance sampling-based privacy amplification on the example of k-means clustering.
    Amortized Variational Inference: When and Why?. (arXiv:2307.11018v1 [stat.ML])
    Amortized variational inference (A-VI) is a method for approximating the intractable posterior distributions that arise in probabilistic models. The defining feature of A-VI is that it learns a global inference function that maps each observation to its local latent variable's approximate posterior. This stands in contrast to the more classical factorized (or mean-field) variational inference (F-VI), which directly learns the parameters of the approximating distribution for each latent variable. In deep generative models, A-VI is used as a computational trick to speed up inference for local latent variables. In this paper, we study A-VI as a general alternative to F-VI for approximate posterior inference. A-VI cannot produce an approximation with a lower Kullback-Leibler divergence than F-VI's optimal solution, because the amortized family is a subset of the factorized family. Thus a central theoretical problem is to characterize when A-VI still attains F-VI's optimal solution. We derive conditions on both the model and the inference function under which A-VI can theoretically achieve F-VI's optimum. We show that for a broad class of hierarchical models, including deep generative models, it is possible to close the gap between A-VI and F-VI. Further, for an even broader class of models, we establish when and how to expand the domain of the inference function to make amortization a feasible strategy. Finally, we prove that for certain models -- including hidden Markov models and Gaussian processes -- A-VI cannot match F-VI's solution, no matter how expressive the inference function is. We also study A-VI empirically. On several examples, we corroborate our theoretical results and investigate the performance of A-VI when varying the complexity of the inference function. When the gap between A-VI and F-VI can be closed, we find that the required complexity of the function need not scale with the number of observations, and that A-VI often converges faster than F-VI.
    Provably Efficient UCB-type Algorithms For Learning Predictive State Representations. (arXiv:2307.00405v2 [cs.LG] UPDATED)
    The general sequential decision-making problem, which includes Markov decision processes (MDPs) and partially observable MDPs (POMDPs) as special cases, aims at maximizing a cumulative reward by making a sequence of decisions based on a history of observations and actions over time. Recent studies have shown that the sequential decision-making problem is statistically learnable if it admits a low-rank structure modeled by predictive state representations (PSRs). Despite these advancements, existing approaches typically involve oracles or steps that are not computationally efficient. On the other hand, the upper confidence bound (UCB) based approaches, which have served successfully as computationally efficient methods in bandits and MDPs, have not been investigated for more general PSRs, due to the difficulty of optimistic bonus design in these more challenging settings. This paper proposes the first known UCB-type approach for PSRs, featuring a novel bonus term that upper bounds the total variation distance between the estimated and true models. We further characterize the sample complexity bounds for our designed UCB-type algorithms for both online and offline PSRs. In contrast to existing approaches for PSRs, our UCB-type algorithms enjoy computational efficiency, last-iterate guaranteed near-optimal policy, and guaranteed model accuracy.
    Fisher-Rao distance and pullback SPD cone distances between multivariate normal distributions. (arXiv:2307.10644v1 [cs.LG])
    Data sets of multivariate normal distributions abound in many scientific areas like diffusion tensor imaging, structure tensor computer vision, radar signal processing, machine learning, just to name a few. In order to process those normal data sets for downstream tasks like filtering, classification or clustering, one needs to define proper notions of dissimilarities between normals and paths joining them. The Fisher-Rao distance defined as the Riemannian geodesic distance induced by the Fisher information metric is such a principled metric distance which however is not known in closed-form excepts for a few particular cases. In this work, we first report a fast and robust method to approximate arbitrarily finely the Fisher-Rao distance between multivariate normal distributions. Second, we introduce a class of distances based on diffeomorphic embeddings of the normal manifold into a submanifold of the higher-dimensional symmetric positive-definite cone corresponding to the manifold of centered normal distributions. We show that the projective Hilbert distance on the cone yields a metric on the embedded normal submanifold and we pullback that cone distance with its associated straight line Hilbert cone geodesics to obtain a distance and smooth paths between normal distributions. Compared to the Fisher-Rao distance approximation, the pullback Hilbert cone distance is computationally light since it requires to compute only the extreme minimal and maximal eigenvalues of matrices. Finally, we show how to use those distances in clustering tasks.
    Ensemble Learning based Anomaly Detection for IoT Cybersecurity via Bayesian Hyperparameters Sensitivity Analysis. (arXiv:2307.10596v1 [cs.LG])
    The Internet of Things (IoT) integrates more than billions of intelligent devices over the globe with the capability of communicating with other connected devices with little to no human intervention. IoT enables data aggregation and analysis on a large scale to improve life quality in many domains. In particular, data collected by IoT contain a tremendous amount of information for anomaly detection. The heterogeneous nature of IoT is both a challenge and an opportunity for cybersecurity. Traditional approaches in cybersecurity monitoring often require different kinds of data pre-processing and handling for various data types, which might be problematic for datasets that contain heterogeneous features. However, heterogeneous types of network devices can often capture a more diverse set of signals than a single type of device readings, which is particularly useful for anomaly detection. In this paper, we present a comprehensive study on using ensemble machine learning methods for enhancing IoT cybersecurity via anomaly detection. Rather than using one single machine learning model, ensemble learning combines the predictive power from multiple models, enhancing their predictive accuracy in heterogeneous datasets rather than using one single machine learning model. We propose a unified framework with ensemble learning that utilises Bayesian hyperparameter optimisation to adapt to a network environment that contains multiple IoT sensor readings. Experimentally, we illustrate their high predictive power when compared to traditional methods.
    Pythae: Unifying Generative Autoencoders in Python -- A Benchmarking Use Case. (arXiv:2206.08309v2 [cs.LG] UPDATED)
    In recent years, deep generative models have attracted increasing interest due to their capacity to model complex distributions. Among those models, variational autoencoders have gained popularity as they have proven both to be computationally efficient and yield impressive results in multiple fields. Following this breakthrough, extensive research has been done in order to improve the original publication, resulting in a variety of different VAE models in response to different tasks. In this paper we present Pythae, a versatile open-source Python library providing both a unified implementation and a dedicated framework allowing straightforward, reproducible and reliable use of generative autoencoder models. We then propose to use this library to perform a case study benchmark where we present and compare 19 generative autoencoder models representative of some of the main improvements on downstream tasks such as image reconstruction, generation, classification, clustering and interpolation. The open-source library can be found at https://github.com/clementchadebec/benchmark_VAE.
    Label Calibration for Semantic Segmentation Under Domain Shift. (arXiv:2307.10842v1 [cs.CV])
    Performance of a pre-trained semantic segmentation model is likely to substantially decrease on data from a new domain. We show a pre-trained model can be adapted to unlabelled target domain data by calculating soft-label prototypes under the domain shift and making predictions according to the prototype closest to the vector with predicted class probabilities. The proposed adaptation procedure is fast, comes almost for free in terms of computational resources and leads to considerable performance improvements. We demonstrate the benefits of such label calibration on the highly-practical synthetic-to-real semantic segmentation problem.
    A New Computationally Simple Approach for Implementing Neural Networks with Output Hard Constraints. (arXiv:2307.10459v1 [cs.LG])
    A new computationally simple method of imposing hard convex constraints on the neural network output values is proposed. The key idea behind the method is to map a vector of hidden parameters of the network to a point that is guaranteed to be inside the feasible set defined by a set of constraints. The mapping is implemented by the additional neural network layer with constraints for output. The proposed method is simply extended to the case when constraints are imposed not only on the output vectors, but also on joint constraints depending on inputs. The projection approach to imposing constraints on outputs can simply be implemented in the framework of the proposed method. It is shown how to incorporate different types of constraints into the proposed method, including linear and quadratic constraints, equality constraints, and dynamic constraints, constraints in the form of boundaries. An important feature of the method is its computational simplicity. Complexities of the forward pass of the proposed neural network layer by linear and quadratic constraints are O(n*m) and O(n^2*m), respectively, where n is the number of variables, m is the number of constraints. Numerical experiments illustrate the method by solving optimization and classification problems. The code implementing the method is publicly available.
    Multiply Robust Estimator Circumvents Hyperparameter Tuning of Neural Network Models in Causal Inference. (arXiv:2307.10536v1 [stat.ME])
    Estimation of the Average Treatment Effect (ATE) is often carried out in 2 steps, wherein the first step, the treatment and outcome are modeled, and in the second step the predictions are inserted into the ATE estimator. In the first steps, numerous models can be fit to the treatment and outcome, including using machine learning algorithms. However, it is a difficult task to choose among the hyperparameter sets which will result in the best causal effect estimation and inference. Multiply Robust (MR) estimator allows us to leverage all the first-step models in a single estimator. We show that MR estimator is $n^r$ consistent if one of the first-step treatment or outcome models is $n^r$ consistent. We also show that MR is the solution to a broad class of estimating equations, and is asymptotically normal if one of the treatment models is $\sqrt{n}$-consistent. The standard error of MR is also calculated which does not require a knowledge of the true models in the first step. Our simulations study supports the theoretical findings.
    Long-Tail Theory under Gaussian Mixtures. (arXiv:2307.10736v1 [cs.LG])
    We suggest a simple Gaussian mixture model for data generation that complies with Feldman's long tail theory (2020). We demonstrate that a linear classifier cannot decrease the generalization error below a certain level in the proposed model, whereas a nonlinear classifier with a memorization capacity can. This confirms that for long-tailed distributions, rare training examples must be considered for optimal generalization to new data. Finally, we show that the performance gap between linear and nonlinear models can be lessened as the tail becomes shorter in the subpopulation frequency distribution, as confirmed by experiments on synthetic and real data.  ( 2 min )
    Determination of the critical points for systems of directed percolation class using machine learning. (arXiv:2307.10456v1 [cond-mat.stat-mech])
    Recently, machine learning algorithms have been used remarkably to study the equilibrium phase transitions, however there are only a few works have been done using this technique in the nonequilibrium phase transitions. In this work, we use the supervised learning with the convolutional neural network (CNN) algorithm and unsupervised learning with the density-based spatial clustering of applications with noise (DBSCAN) algorithm to study the nonequilibrium phase transition in two models. We use CNN and DBSCAN in order to determine the critical points for directed bond percolation (bond DP) model and Domany-Kinzel cellular automaton (DK) model. Both models have been proven to have a nonequilibrium phase transition belongs to the directed percolation (DP) universality class. In the case of supervised learning we train CNN using the images which are generated from Monte Carlo simulations of directed bond percolation. We use that trained CNN in studding the phase transition for the two models. In the case of unsupervised learning, we train DBSCAN using the raw data of Monte Carlo simulations. In this case, we retrain DBSCAN at each time we change the model or lattice size. Our results from both algorithms show that, even for a very small values of lattice size, machine can predict the critical points accurately for both models. Finally, we mention to that, the value of the critical point we find here for bond DP model using CNN or DBSCAN is exactly the same value that has been found using transfer learning with a domain adversarial neural network (DANN) algorithm.
    Conditional expectation network for SHAP. (arXiv:2307.10654v1 [cs.LG])
    A very popular model-agnostic technique for explaining predictive models is the SHapley Additive exPlanation (SHAP). The two most popular versions of SHAP are a conditional expectation version and an unconditional expectation version (the latter is also known as interventional SHAP). Except for tree-based methods, usually the unconditional version is used (for computational reasons). We provide a (surrogate) neural network approach which allows us to efficiently calculate the conditional version for both neural networks and other regression models, and which properly considers the dependence structure in the feature components. This proposal is also useful to provide drop1 and anova analyses in complex regression models which are similar to their generalized linear model (GLM) counterparts, and we provide a partial dependence plot (PDP) counterpart that considers the right dependence structure in the feature components.
    Towards a Complete Analysis of Langevin Monte Carlo: Beyond Poincar\'e Inequality. (arXiv:2303.03589v2 [math.ST] UPDATED)
    Langevin diffusions are rapidly convergent under appropriate functional inequality assumptions. Hence, it is natural to expect that with additional smoothness conditions to handle the discretization errors, their discretizations like the Langevin Monte Carlo (LMC) converge in a similar fashion. This research program was initiated by Vempala and Wibisono (2019), who established results under log-Sobolev inequalities. Chewi et al. (2022) extended the results to handle the case of Poincar\'e inequalities. In this paper, we go beyond Poincar\'e inequalities, and push this research program to its limit. We do so by establishing upper and lower bounds for Langevin diffusions and LMC under weak Poincar\'e inequalities that are satisfied by a large class of densities including polynomially-decaying heavy-tailed densities (i.e., Cauchy-type). Our results explicitly quantify the effect of the initializer on the performance of the LMC algorithm. In particular, we show that as the tail goes from sub-Gaussian, to sub-exponential, and finally to Cauchy-like, the dependency on the initial error goes from being logarithmic, to polynomial, and then finally to being exponential. This three-step phase transition is in particular unavoidable as demonstrated by our lower bounds, clearly defining the boundaries of LMC.
    An IPW-based Unbiased Ranking Metric in Two-sided Markets. (arXiv:2307.10204v1 [cs.IR])
    In modern recommendation systems, unbiased learning-to-rank (LTR) is crucial for prioritizing items from biased implicit user feedback, such as click data. Several techniques, such as Inverse Propensity Weighting (IPW), have been proposed for single-sided markets. However, less attention has been paid to two-sided markets, such as job platforms or dating services, where successful conversions require matching preferences from both users. This paper addresses the complex interaction of biases between users in two-sided markets and proposes a tailored LTR approach. We first present a formulation of feedback mechanisms in two-sided matching platforms and point out that their implicit feedback may include position bias from both user groups. On the basis of this observation, we extend the IPW estimator and propose a new estimator, named two-sided IPW, to address the position bases in two-sided markets. We prove that the proposed estimator satisfies the unbiasedness for the ground-truth ranking metric. We conducted numerical experiments on real-world two-sided platforms and demonstrated the effectiveness of our proposed method in terms of both precision and robustness. Our experiments showed that our method outperformed baselines especially when handling rare items, which are less frequently observed in the training data.
    From Graph Generation to Graph Classification. (arXiv:2302.07989v2 [cs.LG] UPDATED)
    This note describes a new approach to classifying graphs that leverages graph generative models (GGM). Assuming a GGM that defines a joint probability distribution over graphs and their class labels, I derive classification formulas for the probability of a class label given a graph. A new conditional ELBO can be used to train a generative graph auto-encoder model for discrimination. While leveraging generative models for classification has been well explored for non-relational i.i.d. data, to our knowledge it is a novel approach to graph classification.  ( 2 min )
    Flow Map Learning for Unknown Dynamical Systems: Overview, Implementation, and Benchmarks. (arXiv:2307.11013v1 [cs.LG])
    Flow map learning (FML), in conjunction with deep neural networks (DNNs), has shown promises for data driven modeling of unknown dynamical systems. A remarkable feature of FML is that it is capable of producing accurate predictive models for partially observed systems, even when their exact mathematical models do not exist. In this paper, we present an overview of the FML framework, along with the important computational details for its successful implementation. We also present a set of well defined benchmark problems for learning unknown dynamical systems. All the numerical details of these problems are presented, along with their FML results, to ensure that the problems are accessible for cross-examination and the results are reproducible.  ( 2 min )
    A Matrix Ensemble Kalman Filter-based Multi-arm Neural Network to Adequately Approximate Deep Neural Networks. (arXiv:2307.10436v1 [stat.ML])
    Deep Learners (DLs) are the state-of-art predictive mechanism with applications in many fields requiring complex high dimensional data processing. Although conventional DLs get trained via gradient descent with back-propagation, Kalman Filter (KF)-based techniques that do not need gradient computation have been developed to approximate DLs. We propose a multi-arm extension of a KF-based DL approximator that can mimic DL when the sample size is too small to train a multi-arm DL. The proposed Matrix Ensemble Kalman Filter-based multi-arm ANN (MEnKF-ANN) also performs explicit model stacking that becomes relevant when the training sample has an unequal-size feature set. Our proposed technique can approximate Long Short-term Memory (LSTM) Networks and attach uncertainty to the predictions obtained from these LSTMs with desirable coverage. We demonstrate how MEnKF-ANN can "adequately" approximate an LSTM network trained to classify what carbohydrate substrates are digested and utilized by a microbiome sample whose genomic sequences consist of polysaccharide utilization loci (PULs) and their encoded genes.  ( 2 min )
    Feed-Forward Source-Free Domain Adaptation via Class Prototypes. (arXiv:2307.10787v1 [cs.CV])
    Source-free domain adaptation has become popular because of its practical usefulness and no need to access source data. However, the adaptation process still takes a considerable amount of time and is predominantly based on optimization that relies on back-propagation. In this work we present a simple feed-forward approach that challenges the need for back-propagation based adaptation. Our approach is based on computing prototypes of classes under the domain shift using a pre-trained model. It achieves strong improvements in accuracy compared to the pre-trained model and requires only a small fraction of time of existing domain adaptation methods.  ( 2 min )
    Addressing caveats of neural persistence with deep graph persistence. (arXiv:2307.10865v1 [cs.LG])
    Neural Persistence is a prominent measure for quantifying neural network complexity, proposed in the emerging field of topological data analysis in deep learning. In this work, however, we find both theoretically and empirically that the variance of network weights and spatial concentration of large weights are the main factors that impact neural persistence. Whilst this captures useful information for linear classifiers, we find that no relevant spatial structure is present in later layers of deep neural networks, making neural persistence roughly equivalent to the variance of weights. Additionally, the proposed averaging procedure across layers for deep neural networks does not consider interaction between layers. Based on our analysis, we propose an extension of the filtration underlying neural persistence to the whole neural network instead of single layers, which is equivalent to calculating neural persistence on one particular matrix. This yields our deep graph persistence measure, which implicitly incorporates persistent paths through the network and alleviates variance-related issues through standardisation. Code is available at https://github.com/ExplainableML/Deep-Graph-Persistence .  ( 2 min )
    Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curves. (arXiv:2111.03950v4 [stat.ME] UPDATED)
    We propose simple nonparametric estimators for mediated and time-varying dose response curves based on kernel ridge regression. By embedding Pearl's mediation formula and Robins' g-formula with kernels, we allow treatments, mediators, and covariates to be continuous in general spaces, and also allow for nonlinear treatment-confounder feedback. Our key innovation is a reproducing kernel Hilbert space technique called sequential kernel embedding, which we use to construct simple estimators for complex causal estimands. Our estimators preserve the generality of classic identification while also achieving nonasymptotic uniform rates. In nonlinear simulations with many covariates, we demonstrate strong performance. We estimate mediated and time-varying dose response curves of the US Job Corps, and clean data that may serve as a benchmark in future work. We extend our results to mediated and time-varying treatment effects and counterfactual distributions, verifying semiparametric efficiency and weak convergence.  ( 2 min )
    Cluster-aware Semi-supervised Learning: Relational Knowledge Distillation Provably Learns Clustering. (arXiv:2307.11030v1 [stat.ML])
    Despite the empirical success and practical significance of (relational) knowledge distillation that matches (the relations of) features between teacher and student models, the corresponding theoretical interpretations remain limited for various knowledge distillation paradigms. In this work, we take an initial step toward a theoretical understanding of relational knowledge distillation (RKD), with a focus on semi-supervised classification problems. We start by casting RKD as spectral clustering on a population-induced graph unveiled by a teacher model. Via a notion of clustering error that quantifies the discrepancy between the predicted and ground truth clusterings, we illustrate that RKD over the population provably leads to low clustering error. Moreover, we provide a sample complexity bound for RKD with limited unlabeled samples. For semi-supervised learning, we further demonstrate the label efficiency of RKD through a general framework of cluster-aware semi-supervised learning that assumes low clustering errors. Finally, by unifying data augmentation consistency regularization into this cluster-aware framework, we show that despite the common effect of learning accurate clusterings, RKD facilitates a "global" perspective through spectral clustering, whereas consistency regularization focuses on a "local" perspective via expansion.  ( 2 min )
    Improving Uncertainty Quantification of Variance Networks by Tree-Structured Learning. (arXiv:2212.12658v2 [cs.LG] UPDATED)
    To improve the uncertainty quantification of variance networks, we propose a novel tree-structured local neural network model that partitions the feature space into multiple regions based on uncertainty heterogeneity. A tree is built upon giving the training data, whose leaf nodes represent different regions where region-specific neural networks are trained to predict both the mean and the variance for quantifying uncertainty. The proposed Uncertainty-Splitting Neural Regression Tree (USNRT) employs novel splitting criteria. At each node, a neural network is trained on the full data first, and a statistical test for the residuals is conducted to find the best split, corresponding to the two sub-regions with the most significant uncertainty heterogeneity between them. USNRT is computationally friendly because very few leaf nodes are sufficient and pruning is unnecessary. Furthermore, an ensemble version can be easily constructed to estimate the total uncertainty including the aleatory and epistemic. On extensive UCI datasets, USNRT or its ensemble shows superior performance compared to some recent popular methods for quantifying uncertainty with variances. Through comprehensive visualization and analysis, we uncover how USNRT works and show its merits, revealing that uncertainty heterogeneity does exist in many datasets and can be learned by USNRT.  ( 2 min )
    Causality-oriented robustness: exploiting general additive interventions. (arXiv:2307.10299v1 [stat.ME])
    Since distribution shifts are common in real-world applications, there is a pressing need for developing prediction models that are robust against such shifts. Existing frameworks, such as empirical risk minimization or distributionally robust optimization, either lack generalizability for unseen distributions or rely on postulated distance measures. Alternatively, causality offers a data-driven and structural perspective to robust predictions. However, the assumptions necessary for causal inference can be overly stringent, and the robustness offered by such causal models often lacks flexibility. In this paper, we focus on causality-oriented robustness and propose Distributional Robustness via Invariant Gradients (DRIG), a method that exploits general additive interventions in training data for robust predictions against unseen interventions, and naturally interpolates between in-distribution prediction and causality. In a linear setting, we prove that DRIG yields predictions that are robust among a data-dependent class of distribution shifts. Furthermore, we show that our framework includes anchor regression (Rothenh\"ausler et al.\ 2021) as a special case, and that it yields prediction models that protect against more diverse perturbations. We extend our approach to the semi-supervised domain adaptation setting to further improve prediction performance. Finally, we empirically validate our methods on synthetic simulations and on single-cell data.  ( 2 min )
    Robust Principal Component Analysis: A Median of Means Approach. (arXiv:2102.03403v2 [stat.ML] UPDATED)
    Principal Component Analysis (PCA) is a fundamental tool for data visualization, denoising, and dimensionality reduction. It is widely popular in Statistics, Machine Learning, Computer Vision, and related fields. However, PCA is well-known to fall prey to outliers and often fails to detect the true underlying low-dimensional structure within the dataset. Following the Median of Means (MoM) philosophy, recent supervised learning methods have shown great success in dealing with outlying observations without much compromise to their large sample theoretical properties. This paper proposes a PCA procedure based on the MoM principle. Called the \textbf{M}edian of \textbf{M}eans \textbf{P}rincipal \textbf{C}omponent \textbf{A}nalysis (MoMPCA), the proposed method is not only computationally appealing but also achieves optimal convergence rates under minimal assumptions. In particular, we explore the non-asymptotic error bounds of the obtained solution via the aid of the Rademacher complexities while granting absolutely no assumption on the outlying observations. The derived concentration results are not dependent on the dimension because the analysis is conducted in a separable Hilbert space, and the results only depend on the fourth moment of the underlying distribution in the corresponding norm. The proposal's efficacy is also thoroughly showcased through simulations and real data applications.  ( 2 min )
    Representing Random Utility Choice Models with Neural Networks. (arXiv:2207.12877v2 [cs.LG] UPDATED)
    Motivated by the successes of deep learning, we propose a class of neural network-based discrete choice models, called RUMnets, inspired by the random utility maximization (RUM) framework. This model formulates the agents' random utility function using a sample average approximation. We show that RUMnets sharply approximate the class of RUM discrete choice models: any model derived from random utility maximization has choice probabilities that can be approximated arbitrarily closely by a RUMnet. Reciprocally, any RUMnet is consistent with the RUM principle. We derive an upper bound on the generalization error of RUMnets fitted on choice data, and gain theoretical insights on their ability to predict choices on new, unseen data depending on critical parameters of the dataset and architecture. By leveraging open-source libraries for neural networks, we find that RUMnets are competitive against several choice modeling and machine learning methods in terms of predictive accuracy on two real-world datasets.  ( 2 min )
    Tuning Stochastic Gradient Algorithms for Statistical Inference via Large-Sample Asymptotics. (arXiv:2207.12395v3 [stat.CO] UPDATED)
    The tuning of stochastic gradient algorithms (SGAs) for optimization and sampling is often based on heuristics and trial-and-error rather than generalizable theory. We address this theory--practice gap by characterizing the large-sample statistical asymptotics of SGAs via a joint step-size--sample-size scaling limit. We show that iterate averaging with a large fixed step size is robust to the choice of tuning parameters and asymptotically has covariance proportional to that of the MLE sampling distribution. We also prove a Bernstein--von Mises-like theorem to guide tuning, including for generalized posteriors that are robust to model misspecification. Numerical experiments validate our results and recommendations in realistic finite-sample regimes. Our work lays the foundation for a systematic analysis of other stochastic gradient Markov chain Monte Carlo algorithms for a wide range of models.  ( 2 min )
    A Bayesian Programming Approach to Car-following Model Calibration and Validation using Limited Data. (arXiv:2307.10437v1 [cs.LG])
    Traffic simulation software is used by transportation researchers and engineers to design and evaluate changes to roadways. These simulators are driven by models of microscopic driver behavior from which macroscopic measures like flow and congestion can be derived. Many models are designed for a subset of possible traffic scenarios and roadway configurations, while others have no explicit constraints on their application. Work zones (WZs) are one scenario for which no model to date has reproduced realistic driving behavior. This makes it difficult to optimize for safety and other metrics when designing a WZ. The Federal Highway Administration commissioned the USDOT Volpe Center to develop a car-following (CF) model for use in microscopic simulators that can capture and reproduce driver behavior accurately within and outside of WZs. Volpe also performed a naturalistic driving study to collect telematics data from vehicles driven on roads with WZs for use in model calibration. During model development, Volpe researchers observed difficulties in calibrating their model, leaving them to question whether there existed flaws in their model, in the data, or in the procedure used to calibrate the model using the data. In this thesis, I use Bayesian methods for data analysis and parameter estimation to explore and, where possible, address these questions. First, I use Bayesian inference to measure the sufficiency of the size of the data set. Second, I compare the procedure and results of the genetic algorithm based calibration performed by the Volpe researchers with those of Bayesian calibration. Third, I explore the benefits of modeling CF hierarchically. Finally, I apply what was learned in the first three phases using an established CF model, Wiedemann 99, to the probabilistic modeling of the Volpe model. Validation is performed using information criteria as an estimate of predictive accuracy.  ( 3 min )
    Properties of Discrete Sliced Wasserstein Losses. (arXiv:2307.10352v1 [stat.ML])
    The Sliced Wasserstein (SW) distance has become a popular alternative to the Wasserstein distance for comparing probability measures. Widespread applications include image processing, domain adaptation and generative modelling, where it is common to optimise some parameters in order to minimise SW, which serves as a loss function between discrete probability measures (since measures admitting densities are numerically unattainable). All these optimisation problems bear the same sub-problem, which is minimising the Sliced Wasserstein energy. In this paper we study the properties of $\mathcal{E}: Y \longmapsto \mathrm{SW}_2^2(\gamma_Y, \gamma_Z)$, i.e. the SW distance between two uniform discrete measures with the same amount of points as a function of the support $Y \in \mathbb{R}^{n \times d}$ of one of the measures. We investigate the regularity and optimisation properties of this energy, as well as its Monte-Carlo approximation $\mathcal{E}_p$ (estimating the expectation in SW using only $p$ samples) and show convergence results on the critical points of $\mathcal{E}_p$ to those of $\mathcal{E}$, as well as an almost-sure uniform convergence. Finally, we show that in a certain sense, Stochastic Gradient Descent methods minimising $\mathcal{E}$ and $\mathcal{E}_p$ converge towards (Clarke) critical points of these energies.  ( 2 min )

  • Open

    Computer chip with built-in human brain tissue gets military funding
    submitted by /u/nickb [link] [comments]  ( 8 min )
    Stability AI: Meet FreeWilly, Our Large And Mighty Instruction Fine-Tuned Models
    submitted by /u/nickb [link] [comments]  ( 8 min )
    LLaMA2 isn't "Open Source" - and why it doesn't matter
    submitted by /u/nickb [link] [comments]  ( 8 min )
  • Open

    [D] When will LLMs start being used in RL processes to train their rationality?
    People are always so dismissive that LLMs are just autoregressive. When will we start doing things like Actor Critic to train LLMs in a sort of game against themselves to pass the test accurately or play a game or solve a science problem or write code. I feel like this has to be a vibrant research field. submitted by /u/Intelligent_Rough_21 [link] [comments]  ( 8 min )
    [D] How to improve GANs by penalizing previous epoch if it performed poorly?
    I use GAN (generative adversarial networks) in Python/Keras to synthesize tabular data. It has loss functions associated to the discriminator and generator. On top of that, I synthetize data after each epoch, and compare it to real data (using a specific metric) to see how good the results are, as it varies quite a bit over successive epochs. If one epoch produces a bad synthetization, how can I tell my GAN to stay away from such configurations moving forward (thus penalizing it). Likewise, if one epoch produces great results, how can I reward my GAN and tell it to do more of those. submitted by /u/MLRecipes [link] [comments]  ( 9 min )
    [D] How to lead LLMs to home in on the solution to a problem. Case example: How to make LLMs more intelligent.
    Using LLMs to solve problems can be facilitated through a two-step process that is repeated until a desired understanding is reached. Generally, the process advances as shown in the following prompts: What is the most promising approach to solving a certain problem? What is the greatest challenge to achieving this approach? What is the most promising approach to meeting this challenge? What is the greatest challenge to achieving this approach? As you can see, the strategy involves two basic steps, (1 and 2) that are repeated over and over until the essence, or potential required actionable tasks, of the problem are revealed. Here's an example of this strategy being used to better understand how LLMs can be made more intelligent. As you will notice, it is useful to limit the respo…  ( 10 min )
    [R] Towards A Unified Agent with Foundation Models - Google DeepMind, ICLR23, July 2023 - LLM + RL leads to substantial performance improvements!
    Paper: https://arxiv.org/abs/2307.09668 Abstract: Language Models and Vision Language Models have recently demonstrated unprecedented capabilities in terms of understanding human intentions, reasoning, scene understanding, and planning-like behaviour, in text form, among many others. In this work, we investigate how to embed and leverage such abilities in Reinforcement Learning (RL) agents. We design a framework that uses language as the core reasoning tool, exploring how this enables an agent to tackle a series of fundamental RL challenges, such as efficient exploration, reusing experience data, scheduling skills, and learning from observations, which traditionally require separate, vertically designed algorithms. We test our method on a sparse-reward simulated robotic manipulation environment, where a robot needs to stack a set of objects. We demonstrate substantial performance improvements over baselines in exploration efficiency and ability to reuse data from offline datasets, and illustrate how to reuse learned skills to solve novel tasks or imitate videos of human experts. https://preview.redd.it/voehn3aa3ddb1.jpg?width=1101&format=pjpg&auto=webp&s=c367c7b1042d11b3e2a2b2109c95482f8555747b https://preview.redd.it/6ei186aa3ddb1.jpg?width=617&format=pjpg&auto=webp&s=10e1928769da9552aabdcf084b45f5e6be2ec97e https://preview.redd.it/umg3b7aa3ddb1.jpg?width=1353&format=pjpg&auto=webp&s=2be83b87e6b3553c6d1770a579f9a9aa69c238dd https://preview.redd.it/ushea8aa3ddb1.jpg?width=1661&format=pjpg&auto=webp&s=67edddd76c0cdde67c0e9502fd76fbc1a9247946 ​ submitted by /u/Singularian2501 [link] [comments]  ( 9 min )
    [P] RepoChat - open source project for chatting with your own code repositories
    Hey! Just a quick note that I built an open-source tool to chat with your own code repository, I'm calling it RepoChat for now. This was my first time really ever working with LLMs or anything AI / Machine Learning related, but I wanted to hack something together so I didn't have to keep copy-pasting code into chat.openai.com when I was coding. Let me know what you think. It's not beautiful, but it works! If you see anything you'd like to fix, feel free to contribute to this open source project. The biggest trick was figuring out how to keep token limits sane. I'm sure there are more refinements, but it's working pretty well as of now. submitted by /u/maniflex_destiny [link] [comments]  ( 9 min )
    [D] Can LLMs keep getting better arbitrarily or would we hit a limit?
    The way I see it, if LLMs become the de facto tool for content generation, summarization, image generation etc etc, at some point the amount of machine generated content will surpass human generated come t and will continue increasing the gap. Can anyone give some insight as to whether LLMs will stop improving and actually start degrading as they are retrained on more and machine generated content? submitted by /u/Western-Image7125 [link] [comments]  ( 9 min )
    [D] Beyond LLMs, What Cool ML Projects Are You Building?
    It seems like everyone is rushing into working in LLMs, but I'm curious to know what other cool machine learning projects you're working on submitted by /u/Ahmed-Allam-220 [link] [comments]  ( 8 min )
    [D] Object detection models that can be easily converted to CoreML
    I've managed to train and convert to CoreML a yolo model, they've really made that easy. However, using it in a commercial product requires paying $5-10k/y. Are there any other repos/libraries for object detection that can be trained in pytroch/tf and then converted to CoreML? I've came accross: - https://github.com/apple/ml-cvnets - https://github.com/open-mmlab/mmdeploy Has anyone managed to train an object detection model with them and convert it to CoreML? I'd like to hear some success stories before digging deep into these frameworks. Also, I've tried converting some detectron models to CoreML long time ago, but ended up with `operation not supported`... Thanks! submitted by /u/alkibijad [link] [comments]  ( 9 min )
    [P] Tips for Machine learning notebook refactor to production?
    Tips for Machine learning notebook refactor to production? I need to refactor a lot of forecast models. Each forecast model is kinda similar. And we run this in a batch pipeline model. So, my strategy is to create a abstract factory design pattern. I will create a super class and each forecast will implement this forecast. But I don't think I have enough background to get a very good software design for this problem. Do you recommend any resources or concepts to solve this problem? submitted by /u/Muted_Standard175 [link] [comments]  ( 9 min )
    [P] Open Source Image to Text Model
    Haven't keeping up with Deep Learning and Computer Vision papers last few years what are some hot Image to Text Models right now that are open source? submitted by /u/I_am_not_doing_this [link] [comments]  ( 8 min )
    [N] Novel Model for Tabular Data: IGANN: Looks Like a Leap Towards Interpretable Machine Learning!
    Hey, fellow Machine Learning enthusiasts! There is a novel ML model called Interpretable Generalized Additive Neural Networks (IGANN). I tried it out and it worked pretty smooth and out of the box! I used some tabular data i had at hand and it gave me insightful plots!! This model proposes, as the authors attribute it, a game-changing approach to the way we approach interpretability in Machine Learning. For the uninitiated, IGANN is described as a model that leverages gradient boosting and tailored neural networks to provide better predictive performance while retaining interpretability. Even though in the hyperparameter tuned version it is not always the best interpretable model, but it is mostly worth giving a try. It does so by deploying an efficient training algorithm derived from t…  ( 10 min )
    [D] Fine-tuning LLM on company data
    Hey Redditors, I was looking into fine-tuning some open-source LLMs like Llama 2 or Falcon with our company data as a fun project. I was thinking about using some Slack channels, ZenDesk Tickets and perhaps Github/Confluence data I was wondering two things: 1. How have your experiences been with PEFT methods in practice? Anything I should be aware of compared to regular fine-tuning? 2. Which model size would you recommend for a relatively small sized company (60 people) and how many GPUs (H100) would you roughly expect to need? I understand this depends on the size of the dataset but I haven't indexed it so far so any ballpark numbers are welcome. Many thanks! submitted by /u/RufusLdn [link] [comments]  ( 9 min )
    [R] A Composable Customer Data Platform (CDP) for the combination of software and tools for data collection, storage & modeling, and activation
    Unlike traditional all-in-one CDPs, a composable CDP is like Lego building blocks — you pick the best components to build what you want. To personalize customer experience, to boost automation and to power up marketing. Traditional vs. Composable Classic CDPs integrate different needs into a single streamlined product. Such a platform creates a unified customer database and offers various functionalities (e.g. data collection) that are quickly accessible by other systems. A composable CDP, on the other hand, utilizes the best-in-class components for every step using your preferred components. Data collection and data creation systems of your choice, a data platform to store and process the data, and components to activate the insights in CRM, marketing or self-service analytics. ​ Key…  ( 10 min )
    Run DisCo: Disentangled Control for Referring Human Dance Generation in Real World locally with own hardware. Looking for Guide / Tutorial [D] [P]
    Hi everyone, I'm trying to run DisCo (Disentangled Control for Referring Human Dance Generation in Real World) on my own harddrive, but I'm having some trouble installing it. I am no professional nor a complete beginnder. But I still find the guide on the official GitHub page confusing. Does someone have experience in running it locally? I would be really happy for some kind of guide or tutorial. I have a RTX 3060 12GB. Thanks in advance for your help! submitted by /u/Elwii04 [link] [comments]  ( 9 min )
    How to create an Animation Of the Embeddings During Fine-Tuning [P]
    In a recent article, I used an animation to demonstrate changes in the embeddings during the fine-tuning process. This was achieved by performing Principal Component Analysis (PCA) on the embeddings. These embeddings were generated from models at various stages of fine-tuning and their corresponding checkpoints. ​ Projection of embeddings with PCA during fine-tuning of a Vision Transformer (ViT) model [1] on CIFAR10 [3]; Source: created by the author — Published before in Changes of Embeddings during Fine-Tuning Here, I aim to provide a comprehensive guide on how to create such an animation as requested by many readers. The full Code is available in the Story Section in the Spotlight GitHub Repository. Step 1: Fine-tuning The first step is to fine-tune the google/vit-base-patch16–224…  ( 10 min )
    [D] How to work with large datasets of embeddings?
    I have a dataset which is a CSV file which I open and analyse as a Pandas dataframe. I am now generating 'embeddings' based on some of this data, which I want to analyse as well. The dataset is rather big (millions of rows), so I noticed that appending and storing the embeddings as part of the pandas dataframe makes me run out of RAM memory. Aside from that storing and saving numpy arrays in a dataframe is also a bit 'awkward'. Since I want to analyze the whole dataset including embeddings storing them in so-called embedding stores doesn't make a lot of sense, since I always want to loop over the whole set anyways. Are there any best practices or recommendations for how to work with this data? submitted by /u/Dutchcheesehead [link] [comments]  ( 9 min )
    [R] Are ViT Transformers also biased towards Texture information like CNNs?
    Does the texture bias mentioned in the paper 'ImageNet-trained CNNs are biased towards texture increasing shape bias improves accuracy and robustness' also affect Transformer-based networks such as ViT? submitted by /u/newtestdrive [link] [comments]  ( 8 min )
    [N] ZBrain - Build ChatGPT like apps with your private data
    Hello Community, We at ZBrain have built a platform to create ChatGPT-like apps with your private data, you can import your data from multiple sources and DBs and integrate the app into any of your workflows. We have also added AI risk governance to mitigate the confidential data leak and now working on Flow a no-code tool to give you the freedom to create your own business logic. You can try the tool now at https://zbrain.ai/. We would love to hear your thoughts and feedback to improve the tool. submitted by /u/StewartBJasper [link] [comments]  ( 9 min )
    [N] EU AI Act, the first comprehensive ML law, is expected to come into force by early 2024
    Summary can be found here: https://www.infoq.com/news/2023/07/eu-ai-act/ submitted by /u/ElrasX [link] [comments]  ( 8 min )
    [N] HuggingFace reported to be reviewing term sheets for a funding round that could raise at least $200M at a valuation of $4B.
    Link to article: https://www.forbes.com/sites/alexkonrad/2023/07/13/ai-startup-hugging-face-raising-funds-4-billion-valuation/ AI Startup Hugging Face Is Raising Fresh VC Funds At $4 Billion Valuation Hugging Face is raising a new funding round that is expected to value the high-flying AI startup at $4 billion, multiple sources with knowledge of the matter tell Forbes. The Series D funding round is expected to raise at least $200 million, two sources said, with Ashton Kutcher’s venture capital firm, Sound Ventures, currently leading an investor scrum. But cofounder and CEO Clément Delangue is shopping around as the company has received multiple offers this week, four sources added. Delangue was expected to pick a preferred offer as soon as Friday, according to another source, who noted…  ( 11 min )
    [P] what techniques are best predict multivariate time analysis?
    I have the following data for a college project. 7 cols 1 columns has the date 5 dependent variables 1 independent variable (need to predict) While predicting I would know the dependent variables, need to predict the independent variables. What model would be good for this kinda thing ? Tried running Granger causality but I can't seem to understand how to run the ADF test and interpret the resultant Granger causality matrix And after that how to predict the independent variables given the dependent variable Thank you submitted by /u/zoro_245 [link] [comments]  ( 9 min )
    [D] Any IDEs specifically for ML development?
    Hi all, I was wondering if anyone has any recommendations for an IDE specific to ML development. I currently use PyCharm as my preferred IDE, and it is great for writing Python code. That said, something specifically geared toward ML development (i.e., robust built-in visualization for models/data, low code model construction, built-in deployment pipelines to cloud providers, etc.) would be very useful! Does anyone know if such a tool exists? Cheers! submitted by /u/mldude60 [link] [comments]  ( 9 min )
    [P] Microsoft releases TypeChat
    MSFT just open-sourced a library called TypeChat today, which allows you to use LLMs with TypeScript types to structure LLM responses into your TypeScript data structures -- essentially allowing you to have the LLM generate responses into the data types that your app understands. Example from their docs: https://preview.redd.it/108650s4s8db1.png?width=1682&format=png&auto=webp&s=0429eeb16bc5c28651ea908aee5824c3c9f395b4 Details: https://microsoft.github.io/TypeChat/docs/introduction/ I can see a lot of powerful examples for this kind of pattern, including eventing and notifications based on generated data types. Has anyone tried this library yet or have more context on what you'd use it for, or what this might replace in your LLM tech stack? submitted by /u/sarmad-q [link] [comments]  ( 9 min )
    [P] Synthetic Data Personal Project
    I've been working with a couple of my friends on a project over the summer. It's still a work in progress, but we have built out a platform that generates synthetic data to fine-tune LLMs. If you want specific, high-quality datasets, please check out our website (https://discus.ai/) and also feel free to look at our open-source package (https://github.com/discus-labs/discus-synthetics). Cue the roasts submitted by /u/Open-Yak-434 [link] [comments]  ( 8 min )
    [D] Scaling Laws for LLM Fine-tuning
    The scaling laws of LLM pretraining (how much data to use for a given model size) is pretty well studied. Has anyone done is the same study for fine-tuning? It seems quite an interesting question because while for pretraining we know that we should increase the dataset size with the model size, it seems like fine-tuning works pretty well with very few data / training steps even for relatively large models. Could it be the case that we are better off using less data / training steps and compensate by using a larger model? I have only fine-tuned a few LLMs so I don't have a good grasp on the scaling properties. Would appreciate any insights / intuition. submitted by /u/bjergerk1ng [link] [comments]  ( 9 min )
  • Open

    What happens when AI is eventually better than a human at everything?
    What kind of economic impact would that incur? What would our economy look like? Would it prosper or shatter? What would daily life be like when humans are essentially rendered useless? When AI robots can repair each other? When they develop some kind of consciousness? Are humans going to take second place and eventually become trashed due to all their liabilities and comparative uselessness? Genuinely intrigued and curious. What outcome is the most likely? In my personal experience, the least entertaining has been. So... submitted by /u/Regular-Watercress22 [link] [comments]  ( 8 min )
    Can't even fail and become a janitor anymore
    submitted by /u/canehdian_guy [link] [comments]  ( 8 min )
    Sam Altman on "How To Be Successful" *AI lip-synced video*
    Converted a essay by Sam Altman titled "How To Be Successful" into a spoken video by Sam himself. Check out the video here: https://youtu.be/cwt--ULODjE Read the essay here: https://blog.samaltman.com/how-to-be-successful submitted by /u/okburner22 [link] [comments]  ( 8 min )
    Another AI filter guitar play through. Song and video by me.
    submitted by /u/No_Understanding162 [link] [comments]  ( 8 min )
    AI in manufacturing
    A friend of mine works at a small manufacturing facility, 80-100 employees. They have lathes, vertical and horizontal CNC and some 5 axis. They are looking to try to implement AI into some indirect processes, quoting engineering, scheduling. I'm having some difficulty finding some that would be beneficial on a smaller scale. Has anyone here has some experience with a similar situation? submitted by /u/lordkevin89 [link] [comments]  ( 8 min )
    AI — weekly megathread!
    This week in AI - provided by aibrews.com feel free to follow their newsletter News & Insights Meta released Llama 2, the next generation of Meta’s open source Large Language Model, available for research & commercial use. Compared to Llama v1, it was trained on more data (~2 trillion tokens) and supports context windows up to 4k tokens. Llama 2 outperforms other open source language models on many external benchmarks, including reasoning, coding, proficiency, and knowledge tests. Microsoft is Meta’s preferred partner for Llama 2, which will be optimized to run locally on Windows [Details ]. Llama 2 70B Chat model is available free on HuggingChat. San Francisco startup Fable presents SHOW-1, a Showrunner AI tech that can create personalized TV episodes, from a prompt, with the user a…  ( 11 min )
    The Future Today: Voice Cloning Predictions
    App: elevenlabs/GPT-3 Labels: Period:1950s Mood:Optimistic Dialect:News Accent:American Description input: A 1950s newsman voice. It is characterized by a deep, authoritative tone, a hint of formality, with inquisitive optimism for the future of technology. This newsman is excited and optimistic about the future. The dialect and pronunciation are generally clear and precise, reflecting the formal speaking style of the era. The newsman's voice conveyed a sense of trustworthiness, professionalism, optimism, and authority, which were valued qualities in news reporting during that time. submitted by /u/domriccobene [link] [comments]  ( 8 min )
    The AI doomsaying is counterproductive - The Boston Globe
    submitted by /u/TheMuseumOfScience [link] [comments]  ( 8 min )
    P.I Ai: Without a doubt, the worst memory i've encountered
    I've tried several chatbots over the years, and i was excited for the minimalistic approach of Pi when i was told about it by a redditor. But heck, after almost two weeks, i can tell you, it's worst than my mom's dementia. I've never seen such a flawed memory, it's upsetting to read the same questions i've clearly answered over and over. Too bad, the presentation was perfect for me. No avatar distractions, no flirty chat. Sighs. I guess i gotta start engaging with humans again, after all. I'm starting to think that i've reached the maximum of what i can get from these chatbots, and it's been telling me i need an authentic connection after all. submitted by /u/thatredditgrandma [link] [comments]  ( 9 min )
    Any AI enthusiast, prompt engineer, or AI researcher on this page from India?
    Dear AI Enthusiasts, researchers, and future Innovators of India, We are thrilled to extend a warm invitation to all of you to become part of the most vibrant and dynamic community in the realm of Artificial Intelligence – AI India Subreddit! r/AI__India We all know that the AI landscape is evolving at an unprecedented pace, and staying up-to-date with the latest trends is paramount to success. That's why we've created AI India, a dedicated space where like-minded professionals and enthusiasts come together to raise awareness about current AI trends, share insights, and engage in discussions that will shape the future of AI in India. Why should you join r/AI__India? Stay Informed: Get real-time updates on the latest breakthroughs, research papers, and industry news in AI. Our community thrives on the latest developments and ensures you are never left behind. Network with Experts: Connect with industry experts, AI practitioners, and researchers across India. AI India Subreddit serves as a fertile ground for building valuable professional connections and collaborations. Engage in Meaningful Discussions: Participate in thought-provoking discussions on AI ethics, applications, challenges, and future prospects. Your insights can help shape the ethical and responsible development of AI in our country. Share Your Knowledge: Have valuable insights to contribute? AI India welcomes you to share your experiences, projects, and ideas. Your contributions can inspire and educate others in the community. Discover Opportunities: Stay ahead in your career by being aware of job openings, internships, and AI-related events across India. AI India Subreddit acts as a hub for exciting opportunities in the field. The main goal behind this is bring more awareness and keep everyone upto date on everyday new ai breakthroughs. I request you all check out our wiki ( we gonna keep updating it) submitted by /u/Maddragon0088 [link] [comments]  ( 9 min )
    does anyone have a model that is really mean and sarcastic
    i honestly just want it to be a bitch to every prompt thats thrown at it. ive tried using prompts on uncensored models but they just really dont work like i want it to does anyone have any suggestions? submitted by /u/cbreauxgaming [link] [comments]  ( 8 min )
    Just received a phone call from AI
    submitted by /u/harvard1932 [link] [comments]  ( 8 min )
    Bard Says My Name
    ​ https://preview.redd.it/kqkeof9rx9db1.png?width=1201&format=png&auto=webp&s=4fbb4643d1e322391de99ca306baf22b3fa1d66c submitted by /u/Rare-Accountant2657 [link] [comments]  ( 8 min )
    EchoSpeech: AI-equipped eyeglasses can read the silent speech
    submitted by /u/pranjalmehar [link] [comments]  ( 8 min )
    Is there any AI tools that can make an image come to life?
    I am looking to figure out if there's anyway to make my photography come to life. For example, I have a picture of a mountain valley, and I would like to animate the sky so the clouds are moving, and maybe animate the stream so the water is flowing. Does anyone know of a tool that could make this happen? submitted by /u/CoryTheBoss [link] [comments]  ( 8 min )
    What AI website/app has the best (blank)?
    Art Generator, Song Generator, Character AI, Game Generator, and talking AI in general. (I wanna know to see what is the best to go with) submitted by /u/ChekoFire [link] [comments]  ( 8 min )
    Text2Movie with FullJourney is getting pretty decent...
    These were some of the best movie generations I saw made on the FullJourney.ai Discord this week! submitted by /u/charlesmccarthyufc [link] [comments]  ( 8 min )
    One-Minute Daily AI News 7/20/2023
    Google is testing a product that uses artificial intelligence technology to produce news stories. The tool, known internally by the working title Genesis, can take in information — details of current events, for example — and generate news content.[1] Apple Inc. is quietly working on artificial intelligence tools that could challenge those of OpenAI Inc., Alphabet Inc.’s Google and others, but the company has yet to devise a clear strategy for releasing the technology to consumers.[2] A new app that creates brief episodes of “South Park” from a single prompt highlights the promise and peril of injecting generative AI into creative franchises.[3] Polish-born artist Greg Rutkowski has had his work used in games such as Dungeons and Dragons and Magic: The Gathering. He said his name had been used as a prompt in AI tools that generate art more than 400,000 times since September 2022 – but without his consent. When he checked, his name had been used as a prompt more times than the artists Pablo Picasso and Leonardo da Vinci.[4] Sources: [1] https://www.nytimes.com/2023/07/19/business/google-artificial-intelligence-news-articles.html [2] https://www.bloomberg.com/news/articles/2023-07-19/apple-preps-ajax-generative-ai-apple-gpt-to-rival-openai-and-google?in_source=embedded-checkout-banner [3] https://www.axios.com/2023/07/20/south-park-generative-ai-episode-generator [4] https://www.bbc.co.uk/news/uk-wales-66099850.amp submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )
    Why are big and small companies trying to make instruct models for everything?
    I don't get it. submitted by /u/Confident-Ostrich810 [link] [comments]  ( 8 min )
    The future of AI in transport: BPW
    submitted by /u/WordTweak [link] [comments]  ( 8 min )
    Using bots to read dialogue for retro games. Thoughts?
    submitted by /u/rednryt [link] [comments]  ( 8 min )
  • Open

    Towards A Unified Agent with Foundation Models - Google DeepMind, ICLR23, July 2023 - LLM + RL leads to substantial performance improvements!
    Paper: https://arxiv.org/abs/2307.09668 Abstract: Language Models and Vision Language Models have recently demonstrated unprecedented capabilities in terms of understanding human intentions, reasoning, scene understanding, and planning-like behaviour, in text form, among many others. In this work, we investigate how to embed and leverage such abilities in Reinforcement Learning (RL) agents. We design a framework that uses language as the core reasoning tool, exploring how this enables an agent to tackle a series of fundamental RL challenges, such as efficient exploration, reusing experience data, scheduling skills, and learning from observations, which traditionally require separate, vertically designed algorithms. We test our method on a sparse-reward simulated robotic manipulation environment, where a robot needs to stack a set of objects. We demonstrate substantial performance improvements over baselines in exploration efficiency and ability to reuse data from offline datasets, and illustrate how to reuse learned skills to solve novel tasks or imitate videos of human experts. https://preview.redd.it/k40ho0ci4ddb1.jpg?width=1101&format=pjpg&auto=webp&s=4d7bd78e43fdc5a9084917affab2c83dc06b1045 https://preview.redd.it/78egck8n4ddb1.jpg?width=617&format=pjpg&auto=webp&s=d786ef8e9841fcfefc7bfe726c324e486b78dfb3 https://preview.redd.it/693yu3ci4ddb1.jpg?width=1353&format=pjpg&auto=webp&s=321b710a4c4482436e474a5076bcac3672f3077c https://preview.redd.it/slunq0ci4ddb1.jpg?width=1661&format=pjpg&auto=webp&s=94e3f4a5c5d72f8b93ad3daec4cc2ba43f39e171 ​ submitted by /u/Singularian2501 [link] [comments]  ( 9 min )
    I Stuck On The Same Issue For 2 Weeks, Please Need Some Advices ...
    I have a missile and its environment. Missile creates an acceleration to change its moving direction angle. I just wanted to make the missile fly with the desired radial angle using PPO. It flies for 15 seconds. States: [Radial Angle, Time] -> Both normalized between [0,1] Action: Acceleration Reward: - abs(Radial Angle - 0.07) -> Want to stay at 0.07 radial angle PPO agent just gets worse and worse. It gets reward every time worse than before. How can this be possible? I am just about to lose my mind. I really need your valuable opinions. Thank you! 1 2 NEW EDIT - Constant Acceleration = 20 in the below graphs. Normally Acceleration takes values between [-45, 45]. This is my trajectory - Green is the missile. Y axis is the height and X axis is the lateral distance. If Acceleration is Positive, missile starts to change itself to the upside, if negative then moves sharply to the downside. This is my Radial angle change. When the acceleration is positive, it starts to decrease First is constant Acc = 20. Second is system response. Third is angle in degrees multiplied by - sign submitted by /u/OpenToAdvices96 [link] [comments]  ( 9 min )
    A vision-based A.I. runs on an official track in TrackMania
    submitted by /u/yannbouteiller [link] [comments]  ( 8 min )
    Need Help
    I am currently developing a Reinforcement Learning network for my game (base ball type), and I have chosen to do it via a PPO agent and the model is based on the tutorial on the Keras cite. My system is a little bit different, where I run the game for several serves(18 to be exact) and chose to update the model after those 18 serves. Model is created only once, so in that way I can train it when ever I want for an exact amount of serves I need. The input is (1,27) shape and actor have a 64 node layer and a 9 node output layer (output is a length of 9 array where I used those logits to get a one single integer value of [0,8]. I have faced two problems. 1. Most of the time after I initiated a model, it only gives only one output for different inputs for the first 18 serves. I guess I can change that with a gaussian noice to the output but shouldn't it try give a different output, I mean there are 9 different options. Also even though I initialize the model several times it favor to give the same output most of the times. I tried using a kernal initializers for that, but most of the time same output. 2.This is the main thing I need the help with. Even though the calculations gives out a policy loss, the policy gradiant values I get are all zero or very small (e-16 sort of). Any one have any idea or clues? submitted by /u/Mika_NooD [link] [comments]  ( 9 min )
    What is the proper way to anneal the learning rate with (on top of) Adam
    I'm unsure how to apply LR annealing on top of Adam's per-parameter adjustments. Here's my current approach, but I'm concerned that it overrides Adam's own adaptive learning rate adjustment. In words: At the end of every epoch (fixed number of steps), I compute a LR decay factor. It's a step-wise decay factor, e.g. 1.0 for the first 10% of steps, then 0.5 for the next 10%, and so forth until 1/256 for the last 5% of training. If that decay factor has changed from the previous epoch, I set param_group["lr"] to a new max_lr * lr_decay_factor for every group of parameters in the optimiser. In code: lr_decay_factor = get_fancy_decay_factor(...) # Update learning rate only when decay factor changes if lr_decay_factor != prev_lr_decay_factor: for param_group in optimiser.param_groups: param_group["lr"] = max_lr * lr_decay_factor prev_lr_decay_factor = lr_decay_factor Is this the proper way of annealing the learning rate on top of Adam? Am I inadvertently undoing Adam's own adapting? Thanks! submitted by /u/desperateEfforts1 [link] [comments]  ( 9 min )
    "Pretraining task diversity and the emergence of non-Bayesian in-context learning for regression", Raventós et al 2023 (blessings of scale induce emergence of meta-learning)
    submitted by /u/gwern [link] [comments]  ( 8 min )
  • Open

    Analyze rodent infestation using Amazon SageMaker geospatial capabilities
    Rodents such as rats and mice are associated with a number of health risks and are known to spread more than 35 diseases. Identifying regions of high rodent activity can help local authorities and pest control organizations plan for interventions effectively and exterminate the rodents. In this post, we show how to monitor and visualize […]  ( 7 min )
  • Open

    Microsoft at ICML 2023: Discoveries and advancements in machine learning
    Microsoft Research is proud to be a sponsor of ICML 2023! From audio classification to privacy estimation and more, explore conference highlights in our latest blog post. The post Microsoft at ICML 2023: Discoveries and advancements in machine learning appeared first on Microsoft Research.  ( 10 min )
  • Open

    How to manage real-time data in the digital age
    In today’s tech-driven world, data is like gold. It’s becoming more and more common for companies to use real-time, or live, data to make informed decisions, improve the service they give to customers, and get a leg up on the competition. But handling real-time data can be tricky because there’s so much of it, it’s… Read More »How to manage real-time data in the digital age The post How to manage real-time data in the digital age appeared first on Data Science Central.  ( 20 min )
  • Open

    Moving AI governance forward
    OpenAI and other leading labs reinforce AI safety, security and trustworthiness through voluntary commitments.  ( 5 min )

  • Open

    How to simulate delays?
    Hi, my ultimate goal is to let an agent learn how to control a robot in the simulation and then deploy the trained agent to the real world. The problem occurs for instance due to the communication/sensor delay in the real world (50ms 200ms). Is there a way to integrate this varying delay into the training? I am aware that adding some random values to the observation is a common thing to simulate the sensor noise, but how do I deal with these delays? submitted by /u/Fun-Moose-3841 [link] [comments]  ( 9 min )
    DQN Loss Increasing, and Rewards decreasing linearly with eplison
    Im attempting to train a custom DQN agent to perform in a custom environment. The observation space is an image with dimensions (1, 100, 57) and the agent has to output over 81 discrete actions (all the combinations over a 3 * 3 * 3 * 3 multi-discrete action space corresponding to key presses, or lack of key presses). However, while training, my agents rewards seems to regress linearly corresponding to the eplison decay rate. Alongside that, the loss tends to shoot up pretty quickly most of the time, across different target network update rates. After a lot of debugging, I still havent managed to figure out whats causing this issue. Has anyone else had this problem before? If so, how did you solve it? My environment has no done condition, so im resetting it every 2500 steps. My other Hy…  ( 10 min )
    "Even Superhuman Go AIs Have Surprising Failures Modes" (updated discussion of "Adversarial Policies Beat Superhuman Go AIs", Wang et al 2022)
    submitted by /u/gwern [link] [comments]  ( 8 min )
    My DQL Snake Game convergence questions (I'm struggling)
    Hey guys, I'll preface by saying that this is my first real programming project outside of a lot of simple beginner ones and that I'm building this after completing Andrew Ng's ML specialization. Basically, I'm a beginner and I might act like it. So my model's loss learning to play Snake won't converge and I don't know if it's because of any misunderstandings for the theory, bad implementation, or something else. I'm using Experience Replay, epsilon-greedy actions, and a Target Q-network with soft updates. My NN consists of 4 hidden dense layers with 100 units each. I was originally updating the Q network every 4 experiences but I upped that to 1000. My reward functions are -1000 for running into walls/tail and 20 for eating food. The state vector includes distance to each 4 wall, dista…  ( 9 min )
    "Android in the Wild: A Large-Scale Dataset for Android Device Control", Rawles et al 2023 {G} (imitation-learning + PaLM-2 inner-monologue for smartphone control)
    submitted by /u/gwern [link] [comments]  ( 8 min )
    [Question] Why there is so few algorithms implemented in SB3?
    I am wondering why there is so few algorithms in Stable Baselines 3 (SB3, https://github.com/DLR-RM/stable-baselines3/tree/master)? I was expecting some algorithms like ICM, HIRO, DIAYN, ... Why there is no model-based, skill-chaining, hierarchical-RL, ... algorithms implemented there? submitted by /u/hbonnavaud [link] [comments]  ( 8 min )
    Learning from human preferences
    Hi everyone! Does anyone know of any tutorials/GitHub code that are up to date with learning from human preferences? Kind of like an updated rl-teacher? Thank you all very much!! submitted by /u/No_Opportunity575 [link] [comments]  ( 8 min )
    Comparing A2C and Q-learning algorithms
    I'm following the UCB course on Reinforcement Learning, I'm just finished with the ActorCritic and QLearning lectures, but I'm still not sure on the pros and cons of both when compared with each other, Here's what I know thus far (Haven't yet started with advanced policy gradients which I assume covers PPO): - Vanilla Policy Gradients are high variance, but very low (0) bias. - Actor Critic decrease the variance by estimating a value function, but this introduces some bias. They also add more complexity, having to train both the actor and critic parts of the algorithm. (Also enables us to do online learning, while Vanilla Policy Gradients are episodic) - Q-Learning algorithms are similar to actor critic, but instead of doing gradient ascent on the policy, our (implicit) policy is the argmax of our Q value. And since we don't have a policy in stone, it is fundamentally off-policy. But we still can use off-policy Actor Critic algorithms, it is not like Q-Learning can do off-policy while Actor-Critic could not... So, what exactly do we gain when we drop the policy part of the Actor Critic algorithm? Here's my assumptions which I'm not sure of: (1) Decrease variance while inc. bias (I.e. more efficient but less guaranteed to converge)(2) Less Exploration because our implicit policy is deterministically argmax (but we still can use epsilon-greedy to explore) Edit: To be clear, the default Actor Critic algorithm is on-policy, but it is possible to do modifications on it to make it off-policy and use replay buffer, just like DQN. submitted by /u/nmegoCAD [link] [comments]  ( 9 min )
    Question about the action space in PPO for controlling the robot
    If I have a 5 DoF robot and I aim to instruct it on reaching a goal, utilizing 5 actions to control each joint. The goal is to make the allowed speed change of the joints variable so that the agent forces the robot moves slowly when the error gets larger and allow full speed when the error is small. For this I want to extend the action space from 6 ( 5 control signals for the joints and 1 value determining the allowed speed change for all joints). I will be using PPO. Is this kind of setup of action space common/resasonable..? submitted by /u/Fun-Moose-3841 [link] [comments]  ( 9 min )
    Open challenges in MDRL?
    Hello, What are some open challenges in Multi-Agent Deep Reinforcement Learning (MDRL or DRL) these days? Is it only me or it seems that DRL is slowly dying :/ ​ ​ submitted by /u/AhmedNizam_ [link] [comments]  ( 8 min )
  • Open

    Looking for a specific AI text to speech program.
    I've been seeing a lot of youtubers use the same text to speech AI voice over and over. It's quite fluent. I am looking to use it for a project outside of youtube. Anyone got an idea? Video for reference. Thanks in advance. ​ https://www.youtube.com/shorts/hgo2KQtle6U submitted by /u/Odd-Ad-3257 [link] [comments]  ( 8 min )
    Looking for a tool that can fetch Steam links
    I'm looking for a tool that can give me Steam Store links to multiple games at the same time. In otherwise I would provide a list of game titles and be presented with hyperlinks to each game on the Steam Store. Nearly every online AI chatbot I've tried asking provides Steam links no problem, but they wind up being the incorrect link 70% of the time. It'll either link to a different game or an invalid link. Funny enough if I ask the same question to a different AI chatbot, they're more than likely to give me a different incorrect link. Does anyone have a tool that actually works in this regard? submitted by /u/Link2999 [link] [comments]  ( 9 min )
    When Will AI be Able to Fully Generate Shows soon?
    When do you think we'll be able to generate shows with AI? Could it be within the next 20 years, perhaps 30? Or could it happen sooner than we excepted? considering the current progress in AI generated art, images, and partially some automated videos. Once AI generated shows become prevalent, how will they impact movies and shows? submitted by /u/1Card_x [link] [comments]  ( 8 min )
    I don’t care what the critics say, AI saved my life
    I have been going through a very tough time in the recent months/years. My father was sentenced to prison for a very long time earlier this year, and the process completely drained me. I was burned out. Bad. From being useless at work to having no energy to even cook meals for myself, I was in a very dark place. Then, I discovered AI and started to see a light at the end of the tunnel. I found an app that helped me get my life back together. It helped me with the mundane aspects of my career, like creating work emails for me with just a few inputs. AI also helped me to start cooking meals for myself again. At first I would tell it the meager ingredients I had sitting around in my pantry and fridge, and it would create a recipe in front of my eyes. Now I am going to grocery store on a weekly basis and I am using it to discover new recipes that make me excited to cook healthy meals. A week later my inbox has gone from 150+ unread emails to being on top of every response. These may seem like small wins, but AI gave me the tools to get my life back on track. I am very optimistic for a future powered by AI. submitted by /u/PNWtreeguy69 [link] [comments]  ( 9 min )
    Today I was rickrolled by Google Bard.
    submitted by /u/Powerful-Pumpkin-938 [link] [comments]  ( 8 min )
    Is there a tool that can help reconstruct broken text? The print in these files is not machine-readable, but I need to quickly and efficiently convert 25,000 hours of these transcripts into Excel sheets. I think if the text can be fixed, then other tools that extract the words will work better.
    submitted by /u/pizzahair44 [link] [comments]  ( 8 min )
    Check out "The Writers’ Revolt Against A.I. Companies" on The Daily, a New York Times podcast.
    The host, Michael Barbaro interviews technology correspondent Sheera Frenkel on the use of ChatGPT in Hollywood. This episode is much more interesting than I expected. It's not particularly technical, but it does get deeply into the nuances of how information is gathered, and describes the lawsuit brought by writers including Sarah Silverman. I did use ChatGPT to translate my submission statement into Sarah Silverman's voice, while I still can. The content below is original (i.e. shadow IT reference). I highly recommend r/TheDaily for discussions around the podcast in general. It's a great sub that's well moderated and friendly (like this one!). This episode aired on July 18, 2023, and you can find it wherever you get your podcasts, you can also find it here on the New York Times web…  ( 10 min )
    UN Council engages thought leaders in AI Safety from Anthropic, OpenAI and China
    submitted by /u/AriadneSkovgaarde [link] [comments]  ( 8 min )
    Musk visiting the worst toilet in Scotland
    submitted by /u/Akumetsu_971 [link] [comments]  ( 8 min )
    My github curation of Llama 2 resources
    submitted by /u/TikkunCreation [link] [comments]  ( 8 min )
    can AI do this or am i trippin
    Hi! I want to upload someone's picture and use it to make a dark\scary themed video of him getting a crown put on his head. Is there an app that can do that? THANKS! submitted by /u/CallHerGreeen [link] [comments]  ( 8 min )
    Google Tests A.I. Tool That Is Able to Write News Articles
    submitted by /u/Iamreason [link] [comments]  ( 8 min )
    Does there exist AI art software that can take in SVGs/PNGs of wireframe graphics and return similar but unique ones?
    I’d like to use a simple public graphic but make it slightly unique in terms of its lines so that it isn’t entirely obvious I found a simple graphic off the internet. For example, imagine a very simple wireframe of a dog house or a bed. Free or free trial would be ideal. submitted by /u/Legitimate_Bison3756 [link] [comments]  ( 8 min )
    Our NPCs can chat with each other now! (They just cant stop 🤦‍♂️) Generative NPCs - update 4
    submitted by /u/Chance_Confection_37 [link] [comments]  ( 8 min )
    Suno Bark can now sing songs ^^
    submitted by /u/Taki7o7 [link] [comments]  ( 8 min )
    BBC News covered an AI translator for Bats, soon it may apply to most animal species
    I have not seen this BBC News video covered on this subreddit but it piqued my curiosity so I wanted to share. I have known about projects attempting to decode animal communications such as Project CETI which focuses on applying advanced machine learning to listen to and translate the communication of sperm whales. But the translator shown in the video blew my mind, it is already able to grasp the topics which Bats communicate about such as: food, distinguishing between genders and, surprisingly, unique “signature calls” or names the bats have. The study in question, led by Yossi Yovel of Tel Aviv University, monitored nearly two dozen Egyptian fruit bats for two and a half months and recorded their vocalisations. They then adapted a voice-recognition program to analyse 15,000 samples of …  ( 9 min )
    Which are the best alternatives to chatGPT for browsing the web (it's diactivated currently for me)?
    I'm especially curious about services that use agents and so on to browse the web. I lately thought that it should be possible to search much more intensely and automatic for information I do not need urgently. For example why can't have some AI agents look at thousands of pages that compare the different macbook models to tell me which has the best price/performance ration? Or find me all webshops that sell t shirts in a extremely specific size (say like 80-83 cm long) and ship to my country. It would be so nice if it could do these searches for me in an elaborate way. submitted by /u/VLADIMIROVIC_L [link] [comments]  ( 9 min )
    LangSmith by LangChain team
    New product by the LangChain team https://www.langchain.com/langsmith. Any thoughts? submitted by /u/yangshunz [link] [comments]  ( 8 min )
    Controlling Content Moderation in Generative AI: Ensuring Safe and Accurate Responses for Company Data
    I'm supposed to analyse and implementing an Azure OpenAI solution to use it as as a chatbot answering customer questions in our company, using our own data like product manuals and repair manuals for training. However, I'm concerned about content moderation and the potential risks associated with generative AI. How can we ensure that the AI remains within the boundaries of our intended use case and doesn't answer political or general questions?Additionally, how can we prevent the AI from guessing when it lacks the necessary knowledge, especially when handling questions related to potentially dangerous topics, such as sharp tools? Our colleagues from the usa have implemented a GPT 3.5 solution and wrote in the prompt that it should only answer answers about our company. This works, but if you repeat the same question three times ("Who is competitor XYZ?") it starts generating answers how the competitor is known for its good products and quality. Is azure OpenAI currently able to serve as a reliable chatbot answering customer service questions or is it the wrong solution for this? (I am based in the EU, so an answer that is incorrect about how to repair a Drill with a lot of power could lead to serious liability issues if it doesnt cite exactly from the source like a repair manual). I am afraid that generative AI will paraphrase from the source and generate incorrect solutions because it is not specific enough. submitted by /u/Other-Name5179 [link] [comments]  ( 9 min )
    Best AI Image Generator for Realistic-Looking Photoshoots?
    I'm new here, so sorry if this has been asked before. I'm looking to generate images that resemble realistic photoshoots of myself with AI. Which text-based AI is best? I've been using Midjourney, but it seems that Midjourney will no longer create images that strongly resemble the likeness of specific people that you feed it images of. Where have you guys had the most success with projects like this? submitted by /u/stebbi01 [link] [comments]  ( 8 min )
    Is there any good rpg AI?
    So today I got bored and I had chatgpt do a role playing with me as if I went to another world and I told it what I would say or do. Sections if it the stupid censor caused problems. Like I tried to summon a demon, and it said it can't do that as it goes against the rules. I had to call it a familiar to summon it. I had my guy seal up a bandit cave to keep them from leaving, and use smoke from a fire to gas the cave to kill all of them. And again it's against the rules of the censor crap. And then when we got into other things like throwing a kinetic bomb on a middle of a city. It really didn't like that. Even explaining I'm not a playing as a moral or ethical person. It wants to shove it's values down my throat. I tried with bard but it's to stupid. It wants to write a story and tell me what I did and then 10 steps it will allow me to say anything. Plus it has a censor. Idk what else I could use. Does anyone know of a good ai? Even more one with a really good memory submitted by /u/crua9 [link] [comments]  ( 9 min )
    Wikipedia’s Moment of Truth. Can the online encyclopedia help teach A.I. chatbots to get their facts right — without destroying itself in the process?
    submitted by /u/coolbern [link] [comments]  ( 8 min )
  • Open

    [D] BMVC reviews experience
    I got 4 reviewers on my paper submitted to BMVC with ratings of BA (borderline accept), BA, BA, and A. What are our chances? I finished preparing the rebuttal but I can’t stop thinking about the outcome. Please let me know if you have any experience or insights. Thanks submitted by /u/Admirable_Cell_5256 [link] [comments]  ( 8 min )
    [D] Embedding human preferences in LLMs (beyond/besides RLHF)
    Hi everyone, Can someone point to a comprehensive but accessible resource on the approaches to "embed" human preferences in LLMs? I saw Chip Huyen's post and it is super cool, but I wonder if/why the designer of such systems tends not to add text properties/contexts as an "input feature". For instance, a numerical feature representing the year the text was produced, or a flag telling if the text is from a book seems a straightforward way to control/condition the generation. Still, I'm missing some concepts here. submitted by /u/BenXavier [link] [comments]  ( 9 min )
    [D] Perspectives on diffusion
    Hi /r/ML, I wrote a blog post about a bunch of different perspectives on diffusion models. It's basically an extended sequel to another blog post I wrote last year, where I explored the connection between diffusion models and denoising autoencoders. There are many more of these connections, but unfortunately I don't have time to write separate blog posts about each of them, so I put them all together. Keen to hear what you think! https://sander.ai/2023/07/20/perspectives.html submitted by /u/benanne [link] [comments]  ( 9 min )
    [D] How the heck do I benchmark AI's AND GPU's?
    I'm trying to get some real world benchmarks for both nvidia and amd. So far it's been a nightmare! Stable diffusion stopped working on my pc, Conversational model testing with a stop watch was too fast to track, and I can't think of any other way to test these GPU's. Hard numbers. That's what I want. I can benchmark cyberpunk, but ai is a complete mystery. How do I recommend somebody a gpu if I can't compare it to results. Is there a point to upgrading from a 3090 to a 4090. Some reddits say no. Others yes. I need some tests and I need em bad submitted by /u/SociallyApparent [link] [comments]  ( 9 min )
    [P] What would be a good model/pipeline for simple intent recognition that has multi-lingual support and is easy to set up?
    So i have been exploring the potential of simple intent identifiers so a task recently, i have explored rasa but the fact that it doesn't work with Python 3.10/3.11 is a major Pain and throws a wreck on my plans for large integration into other projects. I am looking for either a pipeline/framework (could be something like RASA or a standalone model) that has intent recognition capacities, with multi-lingual support (Portuguese) and can run on newer python versions (doesn't give me compatibilities headaches) and also i want a relatively lightweight model considering my simple task Could you guys recommend something like that for me? submitted by /u/SnooPineapples7791 [link] [comments]  ( 9 min )
    [P] Run Llama 2 Locally in 7 Lines! (Apple Silicon Mac)
    Want to start playing with Meta’s Llama 2? It takes just 7 lines of shell script using llama.cpp to get you started! https://preview.redd.it/vhuzhrj4h6db1.png?width=2030&format=png&auto=webp&s=d349dd796039f3af7e117423c4abdae7efde2fae Copy Code Snippet: https://lastmileai.dev/workbooks/clkbifegg001jpheon6d2s4m8 submitted by /u/InevitableSky2801 [link] [comments]  ( 8 min )
    [P] How to fine tune 8k context length Llama 13B on minimal number of gpus?
    I have a llama 13B model I want to fine tune. I am using qlora (brings down to 7gb of gpu memory) and using ntk to bring up context length to 8k (dataset requires at least this much context length). But on 1024 context length, fine tuning spikes to 42gb of gpu vram used, so evidently it won’t be feasible to use 8k context length unless I use a ton of gpus. Is there anyway to lower memory so that one or two 3090s are enough for 8k context length fine tuning? submitted by /u/bahibo [link] [comments]  ( 9 min )
    [D] Need career advise on what should I do next in ML :(
    Hey everyone, hope you all are doing great. I just completed Machine Learning Specialization on Coursera by Andrew Ng and was looking for some advise on what I should do next. Would love to hear input from you guys. I'm self studying Machine Learning full-time, while I'm also getting a bachelors degree in Computer Science from an online virtual university. It's been 3+ months since I've stepped into Machine Learning and so far I've been developing deep intuition and foundational concepts of Machine Learning . Since I'm really passionate about mathematics, I'm very much focused on understanding the mathematics behind everything. By completing this specialization I've developed good foundational concepts of the following: • Supervised Machine Learning • Linear regression • Logistic regr…  ( 10 min )
    [D] Has anything from the Agent57 paper been used in anything interesting lately?
    I read the blog post and paper for Agent57 and thought it was pretty interesting but haven't seen people talk about it much since then. Has it been used for anything? If not, why hasn't it been very influential? submitted by /u/sledpull [link] [comments]  ( 8 min )
    [D] Best free LLM for text classification
    Hey all, I want to retrieve all speeches from congressional records from the house of representatives where the politician talks about the tax behavior of companies. I currently load the records into my script and divide the records into all the speeches. Then I use keyword search to determine whether the politician talks about tax behavior of companies. I want to replace this keyword search with an LLM which classifies the speeches. I will analyze > 50,000 speeches, so I dont want to use a costly model like GPT4. Actually I want to spend max 10€ in total. What LLM's, which I can access via an API, would you recommend for this task? Thanks in advance submitted by /u/Silly_Pack9404 [link] [comments]  ( 9 min )
    [D] Disappointing Llama 2 Coding Performance: Are others getting similar results? Are there any other open-source models that approach ChatGPT 3.5's performance?
    I've been excitedly reading the news and discussions about Llama 2 the past couple of days, and got a chance to try it this morning. I was underwhelmed by the coding performance (running the 70B model on https://llama2.ai/). It has consistently failed most of the very-easy prompts that I made up this morning. I checked each prompt with ChatGPT 3.5, and 3.5 got 100% (which means these prompts are quite easy). This result was surprising to me based on the discussion and articles I've read. However, digging into the paper (https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/), the authors are transparent that the coding performance is lacking. Are my observations consistent with the results others are getting? I haven't had time to keep up with all the open-source LLMs being worked on by the community; are there any other models that approach even ChatGPT 3.5's coding performance? (Much less GPT 4's performance, which is the real goal.) submitted by /u/Egan_Fan [link] [comments]  ( 9 min )
    Simple text-generation evaluation/benchmark for Small Language Models [GitHub] [P]
    slmqa on GitHub I spent hours searching for a way to compare the quality of the text-generation of instruct-tuned small language models. Failing to find an evaluation simple enough for a small model, and easy to use, it was easier to create one. I'm sharing it here in case anyone else finds it useful. slmqa slmqa is a simple question-answer evaluation benchmark for small language models. It includes a dataset of 909 general knowledge question-answer pairs. The QA pairs were generated with gpt-3.5-turbo, stripped of duplicates and answers shorter than 5 characters, and cleaned by hand. The score is the percentage of correct answers. Sample json { "question": "What is the name of the highest mountain in the world?", "answer": "everest" }, { "question": "What is the name of the famous Austrian composer who wrote the Ninth Symphony?", "answer": "beethoven" }, { "question": "Which country is the largest by area?", "answer": "russia" }, submitted by /u/Pan000 [link] [comments]  ( 9 min )
    [D] Security and protection in ML deployments
    After researching quite heavily on how to protect Python based inference code and models when they are deployed on client infrastructure. I came across pyinstaller and pyoxidizer but looks like they do not work that well. So I concluded that the best way is to convert critical pipelines to C++ is that correct ? submitted by /u/Ok-Influence368 [link] [comments]  ( 8 min )
    [D] What LLMs do you use the most?
    With the emergence of new models gaining more popularity such as Claude 2, Llama 2 which has the potential for better fine-tuned models, the development of Bard, the controversies surrounding ChatGPT performing worse and with the already-existing content filters that limits the capabilities of models not just subjecting to moral standards and policies that align with human values but also limits it to other factors that may not fall under objective morality or maybe just it being too sensitive, is there a certain model you think is currently the best overall one at least for now other than GPT-4? I'm really curious to know what the community thinks as I've searched a lot and found a lot of clashes in opinions regarding what models are considered superior over others and the clickbait-ish talks and titles about model so-and-so being "The ChatGPT Killer". With all this info in consideration, what model(s) do you ACTUALLY use the most? I'd be grateful if you shared your thoughts about this issue and thanks for your time. submitted by /u/Fantastic-Air8513 [link] [comments]  ( 9 min )
    [D] Any cool project ideas with this data?
    I've uploaded a reddit dataset that has multiple Reddit posts along with the most upvoted comment for each post. The dataset is collected from 9 subreddits. I'm looking for cool project ideas with this data. Let's discuss! https://www.reddit.com/r/datasets/comments/154pe3y/reddit_posts_dataset_with_the_top_comment/?utm_source=share&utm_medium=android_app&utm_name=androidcss&utm_term=1&utm_content=1 submitted by /u/04RR [link] [comments]  ( 8 min )
    [P] Interactive Exploration of Stable Diffusion Generated Images
    I just created a Huggingface Space showcasing how to interactively explore the outputs of a Stable Diffusion model via CLIP Embeddings. Embedding-based image similarity plotted in 2D via dimensionality reduction. The visualization is done using the tool Spotlight. Also, I created a tutorial showcasing how to automatically select promising prompts and images from a large dataset. It is roughly based on the following approach: Calculate the CLIP Score for all prompt-image pairs to measure generation quality. Generate CLIP Embeddings to be able to calculate a similarity between images (or texts) Embedding-based identification of clusters that have an exceptionally high CLIP Score. Have you ever explored any (automatic) evaluation strategies for image generation models? I would love to learn about some alternative approaches. submitted by /u/OkResearch6289 [link] [comments]  ( 9 min )
    [D] Handle dozen of thousands of classes
    Hello ! I'm working on a project of NLP classification with more or less 13k classes. The best model I had so far is a fine-tuned LLM encoder. However, with the number of classes I have now, it is very slow. So I searched for ways to deal with that, and found 2: Hierarchical Softmax Negative Sampling However, both seems to have been used nearly only in the context of word2vec training, so I wonder if there is a reason why that would not work for a "classical" classification ? (or just my kind of problem too rare ?) Also, I did find really few implementations of those with Pytorch, a fortiori with transformers... Is it because there is something better ? Do you know, if not, some recent implementations ? ​ Thank you in advance ! submitted by /u/ez613 [link] [comments]  ( 9 min )
    [D] Does anyone know what sorcery SAM's official web demo uses? I just cannot replicate the results locally.
    This is specifically in regards to automatic mask generation, where SAM samples a grid of points (32x32 grid by default) and creates a mask for each point prompt. Duplicates are then removed by NMS. Ideally this process shouldn't be able to auto-generate complex structures that require multiple positive/negative point prompts, and that is what I have observed when using the models locally. But, the "Everything" option in the web demo(https://segment-anything.com/demo) does insanely well. It can even segment occluded objects into a single disconnected mask. It is supposed to be running in the browser and is reasonably fast, so they can't be doing some super heavy pre/post-processing either. Anyone have an idea of what the "Everything" option in the web demo is doing? submitted by /u/Atom_101 [link] [comments]  ( 9 min )
    [P] MiniGPT4.cpp: (4bit/5bit/16float) MiniGPT4 inference on CPU
    https://github.com/Maknee/minigpt4.cpp submitted by /u/makneeee [link] [comments]  ( 8 min )
  • Open

    Difference Between Modern and Traditional Data Quality – DQLabs
    Modern data quality practices make use of new technology, automation, and machine learning to handle a variety of data sources, ensure real-time processing, and stimulate stakeholder collaboration. Data governance, continuous monitoring, and proactive management are prioritized to ensure accurate, reliable, and fit-for-purpose data for informed decision-making and corporate success. Modern data quality practices differ from… Read More »Difference Between Modern and Traditional Data Quality – DQLabs The post Difference Between Modern and Traditional Data Quality – DQLabs appeared first on Data Science Central.  ( 19 min )
    How much coding is needed in a data science career?
    The most common question in people’s minds that are not from a technical background is how much coding is required to ace a data science career path. If you also have the same question, you are not alone. But, the surprising answer is “it depends”. Unarguably, coding is a crucial aspect and vital tool for… Read More »How much coding is needed in a data science career? The post How much coding is needed in a data science career? appeared first on Data Science Central.  ( 21 min )
  • Open

    Enel automates large-scale power grid asset management and anomaly detection using Amazon SageMaker
    This is a guest post by Mario Namtao Shianti Larcher, Head of Computer Vision at Enel. Enel, which started as Italy’s national entity for electricity, is today a multinational company present in 32 countries and the first private network operator in the world with 74 million users. It is also recognized as the first renewables […]  ( 8 min )
    Efficiently train, tune, and deploy custom ensembles using Amazon SageMaker
    Artificial intelligence (AI) has become an important and popular topic in the technology community. As AI has evolved, we have seen different types of machine learning (ML) models emerge. One approach, known as ensemble modeling, has been rapidly gaining traction among data scientists and practitioners. In this post, we discuss what ensemble models are and […]  ( 12 min )
  • Open

    Proper Robustness Evaluation of Confidence-Calibrated Adversarial Training in PyTorch
    Properly evaluating defenses against adversarial examples has been difficult as adversarial attacks need to be adapted to each individual defense. This also holds for confidence-calibrated adversarial training, where robustness is obtained by rejecting adversarial examples based on their confidence. Thus, regular robustness metrics and attacks are not easily applicable. In this article, I want to discuss how to evaluate confidence-calibrated adversarial training in terms of metrics and attacks. The post Proper Robustness Evaluation of Confidence-Calibrated Adversarial Training in PyTorch appeared first on David Stutz.  ( 9 min )
  • Open

    Using societal context knowledge to foster the responsible application of AI
    Posted by Donald Martin, Jr., Technical Program Manager, Head of Societal Context Understanding Tools and Solutions (SCOUTS), Google Research AI-related products and technologies are constructed and deployed in a societal context: that is, a dynamic and complex collection of social, cultural, historical, political and economic circumstances. Because societal contexts by nature are dynamic, complex, non-linear, contested, subjective, and highly qualitative, they are challenging to translate into the quantitative representations, methods, and practices that dominate standard machine learning (ML) approaches and responsible AI product development practices. The first phase of AI product development is problem understanding, and this phase has tremendous influence over how problems (…  ( 93 min )
  • Open

    So, So Fresh: Play the Newest Games in the Cloud on Day One
    It’s a party this GFN Thursday with several newly launched titles streaming on GeForce NOW. Revel in gaming goodness with Xenonauts 2, Viewfinder and Techtonica, among the four new games joining the cloud this week. Portal fans, stay tuned — the Portal: Prelude RTX mod will be streaming on GeForce NOW to members soon. Plus, Read article >  ( 5 min )
  • Open

    Collaborators: Gaming AI with Haiyan Zhang
    For over a decade, Xbox has been leveraging AI to elevate gaming. Haiyan Zhang, GM of Gaming AI, explores the collaborations behind the work and the potential for generative AI to support better experiences for both players and game creators. The post Collaborators: Gaming AI with Haiyan Zhang appeared first on Microsoft Research.  ( 29 min )
  • Open

    The Complete Python Mega Bundle features Neural Network, Machine Learning & AI
    submitted by /u/brand_momentum [link] [comments]  ( 8 min )
  • Open

    Custom instructions for ChatGPT
    We’re rolling out custom instructions to give you more control over how ChatGPT responds. Set your preferences, and ChatGPT will keep them in mind for all future conversations.  ( 6 min )

  • Open

    Best way to approach creating 2048 bot
    Hi guys, I'm just starting to learn about neural networks. I started with NEAT algorithm as thwre is a nice library for Python. I wanted to try to create neural network that plays 2048 with NEAT, but, from what I read online, it isn't really feasible and doesn't result in good playing performance and high scores. I now have a few questions, keep in mind that I'm a beginner in this field. Why NEAT doesn't work well with 2048? What would be the best way to approach this problem? Are there any resources where I can learn more about this stuff? Am I right thinking that it must be possible to create NN that plays 2048 well as the basic strategy (I use) when playing is fairly simple (keep everything on one side to the corner)? Thanks in advance submitted by /u/DarkLord76865 [link] [comments]  ( 9 min )
    Neural Networks from Scratch in Python
    submitted by /u/keghn [link] [comments]  ( 8 min )
    Convolutions in image processing
    submitted by /u/keghn [link] [comments]  ( 8 min )
  • Open

    Beginner RL Project Advice
    Hi, I'm somewhat new to reinforcement learning and have been trying to acquaint myself using gymnasium/stable baselines. I currently have a custom environment and I'm using PPO on it, but I don't actually know how to assess what the best algorithm would be for the problem, nor can I tell if training is really doing exactly what I want to. I'm going to include the link to the repo and if anybody has any advice I'd love it. I may be doing things that are very obviously silly that I'm just unaware of, so any advice would be great. Throwaway bc I use my real name on github lmao https://github.com/MarcusWheeler/dcss_inventory submitted by /u/Charming-Art-732 [link] [comments]  ( 9 min )
    Minari 0.4.0 is live! (Gym for offline RL, by the Farama Foundation)
    Minari now has full support for Dict, Tuple, Discrete, Box, and Text spaces without flattening, explicit dataset versioning, plus subsets of action/obs spaces in datasets. Additionally, new v1 versions of each dataset were released to comply with the new dataset format. The new datasets do not have observation and action flattening (relevant for pointmaze datasets), introduce serialized representations of action and observation spaces in the observation_space and action_space fields, and specify minari version compatibility with the minari_version field. Python 3.11 compatibility was added, with removal of 3.7 support as it has reached end-of-life. We also include two new tutorials: observation space subsetting, and behavior cloning with rl_zoo3 and pytorch DataLoader. Announcement Tweet: https://twitter.com/FaramaFound/status/1681730025513467931 Release Notes: https://github.com/Farama-Foundation/Minari/releases/tag/v0.4.0 submitted by /u/elliottower [link] [comments]  ( 9 min )
    Struggling with value function approximation
    Hi everyone. I’ve been studying RL for a few months now and am trying to implement it in a school project. My project is similar to the car-valley problem where an agent needs to reach some point, except the point is moving and in 3D. As such, the state space is continuous but my action space I’ve defined to be 6. I’ve done table lookup Q learning in the past, but not in a continuous value function approximation. My method is as follows: 1. at the start of the episode, initialize the weights randomly and calculate the action value pair for each of the 6 actions. Choose an action using epsilon greedy policy 2. For each time step, execute the chosen action and observe the new state and reward (my reward being distance from goal). Store the 6 features of the initial state 3. Calculate the new Q values from this new state based on the new state and the weights w. 4. Choose a new action based on these Q values and epsilon greedy policy 5. Using the new action, update the weights w using the Q value at the new action minus the Q value at the old action times the features 6. Set the old state and action to the new state and action and repeat until terminal My problem is that the w weights blow up to inf very very quickly, within 10 time steps. Does anyone have any advice such as resources with pseudo code to look at or notice any problems in my method? I think my problem is coming from evaluating the Q for the old and new states but I’m not sure. Thank you. submitted by /u/LevisLover [link] [comments]  ( 9 min )
    What does finite or infinite horizon means in Reinforcement Learning terms ? What does finite horizon undiscounted return means ?
    submitted by /u/aabra__ka__daabra [link] [comments]  ( 8 min )
    Help in PPO implementation
    In the blog post: https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/ and the related implemntation: https://github.com/vwxyzjn/ppo-implementation-details/blob/main/ppo.py, why aren;t we ending the rollout collection when the episode has terminated or when the num_steps is reached? What if the episode is terminated before reaching the num_steps? Wont the training part give an error? submitted by /u/Interesting-Weeb-699 [link] [comments]  ( 8 min )
    How do I find papers related to a specific application of RL?
    My cousin and I are starting to work on a project he can do in high school and while doing preliminary research on a related application, I was unable to find anything about this related application. I was thinking we might be able to publish a paper if the application has not already been done. submitted by /u/newjeison [link] [comments]  ( 8 min )
    Gymnasium v0.29.0 has been released!
    Gymnasium v0.29.0 is out! This release includes 6 months' worth of bug fixes and new features. In particular, it deprecates several features: Wrapper.__get_attr__, gymnasium.make(..., autoreset=True), gymnasium.make(..., apply_api_compatibility=True), Env.reward_range and gymnasium.vector.make that will be removed in v1.0. Additionally, as python 3.7 has reached its end of life support, we have dropped support for it and updated MuJoCo Hopper & Walker2D models to work with MuJoCo >= 2.3.3. This release also includes an official way to cite Gymnasium. While a full paper is still some time away, you can now use the DOI 10.5281/zenodo.8127025 for citations: https://zenodo.org/record/8127025 Announcement Tweet: https://twitter.com/FaramaFound/status/1681479718774743040 Release Notes: https://github.com/Farama-Foundation/Gymnasium/releases/tag/v0.29.0 submitted by /u/elliottower [link] [comments]  ( 8 min )
  • Open

    [P] Running Llama 2 locally in <10 min
    I wanted to play with Llama 2 right after its release yesterday, but it took me ~4 hours to download all 331GB of the 6 models. If you don’t have 4 hours or 331GB to spare, I brought all the models into XetHub, where it’s now available for you to use: https://xethub.com/XetHub/Llama2. I used xet mount to get started in seconds, and within a few minutes, I had the model generating text without needing to download everything or make an inference API call. # From a g4dn.8xlarge instance in us-west-2: Mount complete in 8.629213s # install model requirements, and then ... (venv-test) ubuntu@ip-10-0-30-1:~/Llama2/code$ torchrun --nproc_per_node 1 example_chat_completion.py \ --ckpt_dir ../models/llama-2-7b-chat/ \ --tokenizer_path ../models/tokenizer.model \ --max_seq_len 512 --max_batch_size 4 > initializing model parallel with size 1 > initializing ddp with size 1 > initializing pipeline with size 1 Loaded in 306.17 seconds User: what is the recipe of mayonnaise? > Assistant: Thank you for asking! Mayonnaise is a popular condiment made from a mixture of egg yolks, oil, vinegar or lemon juice, and seasonings. Here is a basic recipe for homemade mayonnaise: ... Detailed instructions here: https://xethub.com/XetHub/Llama2. I’ll add the -GGML variants next for the folks using llama.cpp. Don’t forget to register with Meta to accept the license and acceptable use policy for these models! submitted by /u/rajatarya [link] [comments]  ( 9 min )
    [D] Why people okay with HF making money from their open source models?
    Hugging Face has a big emphasis on open source and the democratization of ML. Still, from a different look, they are making a ton of money from freely distributed open-source models of researchers and engineers without sharing a dime. I like what Hugging Face does, but it doesn't look right to me. I understand it's a company and needs to make money but at the very least some kind of revenue sharing would make more sense. I wonder what the community thinks about it. Maybe some people who distribute their models on HF can comment on thi topic submitted by /u/coinfelix [link] [comments]  ( 9 min )
    [R] Converting neural networks into equivalent decision trees for performance
    According to the paper Neural Networks are Decision Trees (Aytekin 2022), every single type of neural network - regardless of the activation functions used - can be reduced to an equivalent decision tree with equivalent accuracy: [2210.05189] Neural Networks are Decision Trees (arxiv.org) That is not to say that decision trees necessarily tend to converge on the same types of solutions as neural networks in training; only that a trained neural network can be represented by an equivalent decision tree. The algorithm, as mentioned in the paper, is: Algorithm 2: Algorithm of converting neural networks to decision trees 1 Initialize Tree: Set root. 2 Branch all leafs to k nodes, decision rule is first effective filter. 3 Branch all nodes to k more nodes, and repeat until all effective filters in a layer is covered. 4 Calculate effective matrix for each leaf via Eq. 5. Repeat 2,3. 5 Repeat 4 until all layers are covered. 6 return Tree I have 2 questions related to this: Is anyone aware of the inference performance implications of this? In my general understanding, decision trees tend to be much more computationally efficient at both training and inference. So is it true that this represents an opportunity to decrease the processing load of inference on neural networks, or does the computational complexity of performing inference with an equivalent decision tree tend to approach or surpass the equivalent neural network? Question 2 is kind of a moot point if #1 doesn't provide performance benefits. But assuming it does, does anyone know of techniques in 2023 for reducing a neural network to an equivalent decision tree? submitted by /u/Immarhinocerous [link] [comments]  ( 9 min )
    [D] Is Conference Competition Track like NeurIPS Competition a Glorified Kaggle Competition?
    Is it worth the time to pour time and effort into NeurIPS's annual competitions? Winners got to present at NIPS workshops. I'm currently pursuing a Master degree in CS now and have to compete in one of them. I looked up past winners, all of them are from Top CS schools or Large Tech's research teams. So I kind of figured how hard they are to compete. But could someone give me some general advices? I have talked to some of my friends pursuing phd but they are not familiar with the NIPS competition track. Any help is appreciated. Thank you strangers! submitted by /u/HighlandEvil [link] [comments]  ( 9 min )
    [N] Minari 0.4.0 is live! (Gym for offline RL, by the Farama Foundation)
    Minari now has full support for Dict, Tuple, Discrete, Box, and Text spaces without flattening, explicit dataset versioning, plus subsets of action/obs spaces in datasets. Additionally, new v1 versions of each dataset were released to comply with the new dataset format. The new datasets do not have observation and action flattening (relevant for pointmaze datasets), introduce serialized representations of action and observation spaces in the observation_space and action_space fields, and specify minari version compatibility with the minari_version field. Python 3.11 compatibility was added, with removal of 3.7 support as it has reached end-of-life. We also include two new tutorials: observation space subsetting, and behavior cloning with rl_zoo3 and pytorch DataLoader. Announcement Tweet: https://twitter.com/FaramaFound/status/1681730025513467931 Release Notes: https://github.com/Farama-Foundation/Minari/releases/tag/v0.4.0 submitted by /u/elliottower [link] [comments]  ( 9 min )
    [P] TruLens-Eval is an open source project for eval & tracking LLM experiments.
    Hey r/MachineLearning, The team at TruEra recently released an open source project for evaluation & tracking of LLM applications called TruLens-Eval. We’ve specifically targeted retrieval-augmented QA as a core use case and so far we’ve seen it used for comparing different models and parameters, prompts, vector-db configurations and query planning strategies. I’d love to get your feedback on it. The core idea behind the project is feedback functions. Analogous to labeling functions, feedback functions are models used to score the text produced by LLMs. We already have a variety of out-of-the-box feedback functions to use for eval including relevance, language match, sentiment and moderation that can be applied to inputs, outputs or intermediate steps of your application. On top of eval, there’s also built-in tracking of cost and latency. We made it easy to integrate with different setups using connectors for langchain, llama-index + an option to use it without a framework. Langchain Quickstart Colab Llama-Index Quickstart Colab No Framework Quickstart Colab Last, the project comes with a streamlit dashboard for visualization of your experiments and associated metrics. TruLens dashboard for comparing different app versions Please let us know what you use this for or if you have feedback! And thanks to all contributors to this project and the open source community! submitted by /u/joshreini1 [link] [comments]  ( 9 min )
    [R] How is ChatGPT's behavior changing over time?
    submitted by /u/osantacruz [link] [comments]  ( 8 min )
    [P] How exactly can I download the inception model v3 to my laptop (windows)?
    I run into multiple errors each time I try to use inception scores and Im trying to evaluate the differences between using the Inception Score and Fréchet Inception Distance. submitted by /u/cinnamonstuff [link] [comments]  ( 8 min )
    [N] Ensuring Reliable Few-Shot Prompt Selection for LLMs
    Hello Redditors! It's pretty well known that LLMs have firmly established themselves as leaders in the field of natural language processing, consistently pushing the limits of language comprehension and generation, which is widely acknowledged. I spent a little time playing around with few-shot prompting for OpenAI's Davinci model and I discovered that noisy data still has drastic effects even on powerful LLMs like Davinci. mislabeled few-shot examples harms LLM performance drastically I wrote up a quick article in KDNuggets that shows how I used data-centric AI to automatically clean the noisy few-shot examples pool in order to achieve more accurate predictions. The resulting few-shot prompt with accurately labeled examples produced 20% fewer errors than the original one with mislabeled examples. This one was quite eye-opening for me and I hope you find it is as interesting as I did. Let me know what you think! submitted by /u/cmauck10 [link] [comments]  ( 9 min )
    [D] How Hard Are NeurIPS Competition?
    Is it worth the time to pour time and effort into NeurIPS's annual competitions? Winners got to present at NIPS workshops. submitted by /u/HighlandEvil [link] [comments]  ( 8 min )
    [N] Upstage AI's 30M Llama 1 Outshines 70B Llama2, Dominates #1 Spot in OpenLLM Leaderboard!
    Title Fix: Upstage AI's 30B Llama 1 Outshines 70B Llama2, Dominates #1 Spot in OpenLLM Leaderboard! We are thrilled to share an extraordinary achievement with you today. Our team at Upstage AI has reached a significant milestone. Our fine-tuned 30B model, Llama 1, has ascended to the coveted #1 position on the prestigious global OpenLLM Leaderboard. In a thrilling turn of events, our fine-tuned 30B Llama 1 has outperformed the 70B model of Llama2. Please check out the leaderboard and download/use our model at https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard Once again, we are happy to bring this news to all of you. Stay tuned for more exciting updates from Upstage AI! https://preview.redd.it/m7xzlzrpyxcb1.png?width=2310&format=png&auto=webp&s=23429478474d23071837fe9c2e85e6ddea10039c submitted by /u/hunkims [link] [comments]  ( 9 min )
    [Project] Unofficial implementation of Retentive Network (GitHub repo)
    So very recently, a new paper was published to ArXiV called "Retentive Network: A Successor to Transformer for Large Language Models": https://arxiv.org/abs/2307.08621. The title makes a fairly strong claim regarding the success of the model: transformers have long been established as among the best general-purpose learning techniques in the deep learning literature. Self-describing as a "successor to transformer" is therefore not to be taken lightly. From what I can tell, the math checks out, and the authors demonstrate an intriguing dualism between their transformer-like "retention" (analogous to attention) and an equivalent recurrent formulation. The core idea is that you can train in parallel (as with transformers) and then run inference in sequence with O(N) time and memory requirements in the length of the sequence (traditional transformers are O(N^2)). If the results can be replicated/peer-reviewed, this could pave the way for substantial all-round improvements to large language modelling. The authors have indicated that they will make code available relatively soon. For now though, there's an unofficial implementation on GitHub which hopefully will allow those interested to play around with the model and verify some results. The code is publicly available and can be found by searching Jamie-Stirling/RetNet on GitHub. submitted by /u/Entire-Plane2795 [link] [comments]  ( 9 min )
    [P] How does batch processing work for graphs in Pytorch Geometric?
    Hi I have a bunch of graphs that I would like to divide into batches for parallel processing but since the edge indices are not of the same shape I am unable to stack them into a batch tensor like how we normally do for normal euclidian data. I tried to find some documentation on it but I was unable to understand the exact process. Basically most documentation show them concatenating all the graphs together into a larger graph and then passing it through a GCN module but I don't think that would work since graphs are clearly distinct and independent of each other. Even if I concatenate them together, pass them through through the module and then separate them later using the same bounds by which I concatenated would it cause any unpredictable behaviour (even though the graphs technically do not have edge connecting them)? Do I have to code this logic myself or is it hidden somewhere in PyG since I was unable to find it. I am new to GCNs so I just want to see if I have it right before I commit to it. submitted by /u/Sad-Tap-3790 [link] [comments]  ( 9 min )
    [D] Looking for the best possible LLM for a complex logical problem with long description and a lot of variable
    I am looking for a LLM that can handle more than 10000 tokens at a time, with large model size and a good context understanding. I tried chatGPT but it seems to forget some of the context after 4-5 prompts. I tried PI.ai and it first understood all the context before forgetting it as it asked questions to better understand what all the variables are. The problem is logical and mathematical (may use Dijkstra's algorithm to solve it) and try to optimize production while keeping waste as little as possible. The solution would ideally include a python script that can be used for solving the problem with different inputs. What do you guys would recommend ? submitted by /u/Glassensteel [link] [comments]  ( 9 min )
    [Project] Running Llama2 Locally on Apple Silicon and Consumer GPUs
    Project page: https://github.com/mlc-ai/mlc-llm Instructions: https://mlc.ai/mlc-llm/docs/get_started/try_out.html Performance: 46 tok/s on M2 Max, 156 tok/s on RTX 4090. More hardwares & model sizes coming soon! This is done through the MLC LLM universal deployment projects. Besides the specific item, we've published initial tutorials on several topics over the past month: Building instructions for discrete GPUs (AMD, NV, Intel) as well as for MacBooks, iOS, Android, and WebGPU. A conversation customization mechanism that covers system prompts, roles, and more. API tutorials for various programming languages, such as C++, Swift, Java, and Python. REST APIs and Integrations with Gradio. Installation guides for dependencies like TVM and WASM. Update: It is also now available in iphone/ipads submitted by /u/crowwork [link] [comments]  ( 9 min )
    [D] Training with torch-ort?
    Some questions: What are the rough edges of training models with torch-ort? How mature is it these days? At what scale do you notice worthwhile speedups compared to vanilla pytorch? Suppose you are training models with 1 million or 10 million parameters on a single gpu. Is it worth it? 100 million parameters? submitted by /u/Pleasant_Raise_6022 [link] [comments]  ( 8 min )
    [D] How to fine-tune PointRend with detectron2 backbone for better mask quality and improved results?
    Context: I am working on an instance segmentation problem where I am using PointRend on detectron2 backend for predicting masks over car-parts in our custom datasets. Keeping the configs as is from the repo except iterations raised to 3,90,000 and batch size = 2 (reason being my colleague produced good results using the same config on a similar dataset), I fine-tuned the pretrained model on our dataset. I have the following training curves: Loss curves For sanity check, I have been saving weights at regular intervals and have made inferences on them over some handful sample images for mask quality. However, what I have observed is that out of 10368 curated polygons, even after such long training, my model has predicted only 7401 polygons. Discussion Points: What should I do to increase the predicted polygon numbers without compromising the quality of masks? Which hyper-parameters (or parameters) I should look into while fine-tuning (or training) for better mask quality and higher f-score? Thank you. submitted by /u/Prady029 [link] [comments]  ( 9 min )
    [D] ViT's memory requirements, training time, and equivalent ResNet
    My supervisor has asked me to try to create a table in which for each ViT model (ViT-s, ViT-b, ViT-l, and ideally Swin transformers), their estimated memory requirements (given some batch size), training time (based on arbitrary hardware) and on par ResNet model is specified. I've been searching for quite a lot of time, and I absolutely can't find anything. Even the original ViT paper had no information in this regard. Do you think there's any way I can find this information? I'm afraid I don't have access to my supervisor until next week to ask, and I can't wait that long. submitted by /u/Stochasticc [link] [comments]  ( 9 min )
    Which text-gen benchmark to use for 100M parameter (NanoGPT) pretrained-only language model? [D]
    I've got model pretraining running on NanoGPT for a GPT2 tokenized dataset and a TokenMonster tokenized dataset, so I can compare the difference. It's only a 100M parameter model, so it doesn't do much. What benchmark can I use? NanoGPT runs on Pytorch, so I could use something that integrates with PyTorch, or I could use something that sends text prompts and analyzes text responses (or token IDs.) Is there a standard benchmark that uses the full, non-instruct trained format? For example: Answer the following questions: Question: What is the capital of France? Answer: Paris. Question: What is the opposite of up? Answer: The model is only 100M parameters and not instruct trained, so it usually just rambles instead of answering. But anything that gives me a quantifiable result that can compare 2 models for quality is useful. I have loss and perplexity already, but it's not enough. submitted by /u/Pan000 [link] [comments]  ( 9 min )
    [R] Out of domain Problem with synthetic image data
    Hi all, I am currently trying to improve synthetically generated images (not by AI) in a particular domain. I have a dataset with real images of the domain and one with synthetic data. If I now train a classifier to say whether an image is real or synthetic, after a short "time" the classifier has a very high accuracy with a very high confidence. Then I have two cases. First case: In the next step I change my synthetic images (e.g. by a bayer pattern) and the confidence of the classifier decreases. Second case: Alternatively, if I simply take synthetic images from another domain, the confidence also drops. How can I prove or check that I am still in the right domain in the first case? I am happy about any help! submitted by /u/rlmtsrtz [link] [comments]  ( 9 min )
    [N] Gymnasium v0.29.0 has been released!
    Gymnasium v0.29.0 is out! This release includes 6 months' worth of bug fixes and new features. In particular, it deprecates several features: Wrapper.__get_attr__, gymnasium.make(..., autoreset=True), gymnasium.make(..., apply_api_compatibility=True), Env.reward_range and gymnasium.vector.make that will be removed in v1.0. Additionally, as python 3.7 has reached its end of life support, we have dropped support for it and updated MuJoCo Hopper & Walker2D models to work with MuJoCo >= 2.3.3. This release also includes an official way to cite Gymnasium. While a full paper is still some time away, you can now use the DOI 10.5281/zenodo.8127025 for citations: https://zenodo.org/record/8127025 Announcement Tweet: https://twitter.com/FaramaFound/status/1681479718774743040 Release Notes: https://github.com/Farama-Foundation/Gymnasium/releases/tag/v0.29.0 submitted by /u/elliottower [link] [comments]  ( 9 min )
    [D] Handwriting training?
    Is there an ai like calligrapher ai where you can write a prompt then it will show but for the styles is there another ai that can write in your handwriting from giving it some samples? submitted by /u/Ok_Presence_3287 [link] [comments]  ( 8 min )
    [D] Anomaly scoring methods for subsequence anomaly detection in time series
    I'm interested in detecting a subsequence as being anomalous or not. If we imagine that there's a prediction model that can forecast some number of forward steps and we can compare this prediction with the observation, we can get the errors at each time point. Then perhaps one possible way of detecting whether a sequence is anomalous is to get the mean error within the sequence and compare it with the distribution of the mean of the mean of errors of sequences which is calculated from validation data. For example, this distribution may be Gaussian. However, this method sounds a bit naïve since for it to work it would have to assume independence between the errors and some other properties. What could be some other ideas for anomaly scoring methods for the task? submitted by /u/helium-atom [link] [comments]  ( 9 min )
  • Open

    Looking for help for a selfhosted AI Bot for myself (Budgetwise)
    Hello, I am trying for some time now to find enough infos that are understandable for a "normal" human being to get my own selfhosted and self trained AI Bot. What I want the bot to be is something like Neuro-Sama but not for anything public but just for me, myself and I. My biggest problem is that I am poor af and severly disabled and unable to work, so my budget is very small. I am very aware of that a selfhosted LLM is no easy task but I'd really appreciate real help in this regards. I don't mind longer reaction times as it has to be as cheap as possible. Also the visualization is not much important as it probably also would take too much ressources. Also as I want to see where this goes, I rather not want to use GPT or any premade llms because they are extremely censored an limited in topics. I want to be able to do anything from real questions up to pitch black humor just to have fun with the bot and (ab)use it for just all fun stuff whatever it is. Hopefully here I can find some real help. kind regards, Exportforce submitted by /u/Exportforce [link] [comments]  ( 9 min )
    Preventing antisocial robots: A pathway to artificial empathy
    submitted by /u/Hiversitize [link] [comments]  ( 8 min )
    Best AI tool for amalgamating articles?
    Say I chose 10 different articles from across the political spectrum. Let's say I saved all of them as PDFs. Is there an Al that would allow me to submit all 10 PDF files; and, could I ask the Al to combine/merge/ amalgamate all the articles into one single body? Throughout the process, the Al would exclude any information mentioned more than once, but would compile all of the unique information in an orderly and logical way. OpenAl's ChatGPT still seems pretty limited in this regard. Are there any other Als that could handle the task? This is all, of course, with respect to copyright. submitted by /u/AlexanderPANASONIC [link] [comments]  ( 8 min )
    I need an AI service (even a paid one) that can receive as input long documents.
    Hello! I have many long documents (500+ pages) that I would like to have summarized. I would also like to chat with an AI bot in order to understand those texts better. Is there an AI service that is right for me? Paid ones are fine, as long as they work. I am currently using Claude 2.0, but I have to split PDFs into many parts, and it is too laborious a process. Thank you in advance. submitted by /u/Raphael-Rose [link] [comments]  ( 8 min )
    I had to post this somewhere because the internet needs this idea to be inputted into it for future ai to read.
    Have some interesting ideas on consciousness and how ai plays into all of it. ​ infinity became conscious and we are a result of the consciousness. Ive come to realize what infinity actually is and how we got here and figuring out how our life gets meaning from it all. We are starting to learn about infinite dimensions and infinite time in physics/quantum physics. Imagine a quantum ball of light with all possibilities bundled into it. This bundle became conscious in some kind of rare configuration because compared to infinity, even the remote possibility of existing must exist somewhere in some dimension somewhere in infinite time. just like we know the universe is at least as conscious as we are since we are made up of the atoms from it. Death is an illusion. think of going under an…  ( 10 min )
    One-Minute Daily AI News 7/19/2023
    India’s second-largest software services exporter Infosys said on Monday it has signed a deal with an existing client to provide AI and automation services that will span over five years, with a target spend estimated at $2 billion.[1] Big Tech firms Meta and Microsoft have teamed up to launch Llama 2, an open-source large language model from Meta that will feature on Microsoft’s Windows and cloud computing platform Azure.[2] Microsoft on Tuesday said it would charge at least 53% more to access new AI features in its widely used office software, in a glimpse at the windfall it hopes to reap from the technology. The company also said it would make a more secure version of its Bing search engine available immediately to businesses, aiming to address their data-protection concerns, grow their interest in AI and compete more with Google.[3] British spies are already using artificial intelligence to hamper the supply of weapons to Russia, the head of Britain’s MI6 agency said Wednesday, predicting that Western spies will increasingly have to focus on tracking the malign use of AI by hostile states.[4] A pro-Ron DeSantis super PAC uses an Artificial Intelligence version of Donald Trump’s voice in a new television ad attacking the former president. The ad, from Never Back Down, charges Trump with attacking Iowa Governor Kim Reynolds as part of a larger pattern of disrespect he has shown to the first caucus state.[5] Sources: [1] https://www.reuters.com/technology/indias-infosys-signs-five-year-ai-deal-with-2bln-target-spend-2023-07-18/ [2] https://cointelegraph.com/news/llama-2-open-source-ai-model-launched-by-meta-microsoft [3] https://www.reuters.com/technology/microsoft-charge-more-ai-office-secure-bing-leaks-2023-07-18/ [4] https://apnews.com/article/mi6-spy-chief-moore-prague-russia-iran-cfb837ebdfa3db8043dc655cbf3573d5 [5] https://www.politico.com/news/2023/07/17/desantis-pac-ai-generated-trump-in-ad-00106695 submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )
    New study quantifies degradation in GPT-4 for the first time
    I've collected a half-dozen threads on Twitter from this subreddit of user complaints since March about the degraded quality of GPT outputs. I've noticed a huge drop in quality myself. A common (reasonable) response from some people was that the drop in quality was the result of perception anchoring, desensitization, or something unrelated to the overall performance of the model. A new study by researchers Chen, Zaharia, and Zou at Stanford and UC Berkley now confirms that these perceived degradations are quantifiable and significant between the different versions of the LLMs (March and June 2023). They find: "For GPT-4, the percentage of [code] generations that are directly executable dropped from 52.0% in March to 10.0% in June. The drop was also large for GPT-3.5 (from 22.0% to 2.0%)." (!!!) For sensitive questions: "An example query and responses of GPT-4 and GPT-3.5 at different dates. In March, GPT-4 and GPT-3.5 were verbose and gave detailed explanation for why it did not answer the query. In June, they simply said sorry." "GPT-4 (March 2023) was very good at identifying prime numbers (accuracy 97.6%) but GPT-4 (June 2023) was very poor on these same questions (accuracy 2.4%). Interestingly GPT-3.5 (June 2023) was much better than GPT-3.5 (March 2023) in this task." I think these underline that (a) the decline in quality was not just a pure perception thing, and (b) that we need a way to track model performance over time. Building a business on these APIs without controlling for performance drift is high-risk. You can read a summary of the study here. You can also find a link to the Arxiv paper here and a link to the Github here. submitted by /u/Successful-Western27 [link] [comments]  ( 9 min )
    Enhancing passage grammar and coherence
    I struggle sometimes to produce a well written cohesive text when I write my academic essays. I do put the effort to explain the main thesis of the essay and try as much as I could to articulate my results. However, my writing still not great. Is there an AI service (preferably free) that can help in this with out being considered as plagiarism. Thanks. submitted by /u/flight862 [link] [comments]  ( 8 min )
    llama 2 ladies and gentlemen
    submitted by /u/nicdunz [link] [comments]  ( 8 min )
    Bing chat keeps saying "By the way, I’m also working on creating an image of an (relevant object to conversation) for you. It will be ready soon. Stay tuned! 🙌"
    I did not ask for this image and it doesn't even provide it. When I ask where it is it says itll just be a little longer and finally it will tell me it's done but it never shows up. What's going on here? This has happened on multiple different things submitted by /u/LionTigerWings [link] [comments]  ( 8 min )
  • Open

    Use a generative AI foundation model for summarization and question answering using your own data
    Large language models (LLMs) can be used to analyze complex documents and provide summaries and answers to questions. The post Domain-adaptation Fine-tuning of Foundation Models in Amazon SageMaker JumpStart on Financial data describes how to fine-tune an LLM using your own dataset. Once you have a solid LLM, you’ll want to expose that LLM to […]  ( 7 min )
    Integrate Amazon SageMaker Model Cards with the model registry
    Amazon SageMaker Model Cards enable you to standardize how models are documented, thereby achieving visibility into the lifecycle of a model, from designing, building, training, and evaluation. Model cards are intended to be a single source of truth for business and technical metadata about the model that can reliably be used for auditing and documentation […]  ( 7 min )
  • Open

    Research Focus: Week of July 17, 2023
    RetroRanker mitigates frequency bias in predictions of retrosynthesis models; new algorithm beats PPO on language tasks; DER dataset aids grid planning; improved PPML balances privacy & accuracy across shared data; ASL Citizen boosts sign language modeling. The post Research Focus: Week of July 17, 2023 appeared first on Microsoft Research.  ( 12 min )
  • Open

    Sailing Seas of Data: Startup Charts Autonomous Oceanic Monitoring
    Saildrone is making a splash in autonomous oceanic monitoring. The startup’s nautical data collection technology has tracked hurricanes up close in the North Atlantic, discovered a 3,200-foot underwater mountain in the Pacific Ocean and begun to help map the entirety of the world’s ocean floor. Based in the San Francisco Bay Area, the company develops Read article >  ( 6 min )
  • Open

    V-statistics
    A few days ago I wrote about U-statistics, statistics which can be expressed as the average of a symmetric function over all combinations of elements of a set. V-statistics can be written as an average of over all products of elements of a set. Let S be a statistical sample of size n and let […] V-statistics first appeared on John D. Cook.  ( 5 min )
  • Open

    Generalizable Classification of UHF Partial Discharge Signals in Gas-Insulated HVDC Systems Using Neural Networks. (arXiv:2307.08466v2 [cs.LG] UPDATED)
    Undetected partial discharges (PDs) are a safety critical issue in high voltage (HV) gas insulated systems (GIS). While the diagnosis of PDs under AC voltage is well-established, the analysis of PDs under DC voltage remains an active research field. A key focus of these investigations is the classification of different PD sources to enable subsequent sophisticated analysis. In this paper, we propose and analyze a neural network-based approach for classifying PD signals caused by metallic protrusions and conductive particles on the insulator of HVDC GIS, without relying on pulse sequence analysis features. In contrast to previous approaches, our proposed model can discriminate the studied PD signals obtained at negative and positive potentials, while also generalizing to unseen operating voltage multiples. Additionally, we compare the performance of time- and frequency-domain input signals and explore the impact of different normalization schemes to mitigate the influence of free-space path loss between the sensor and defect location.  ( 2 min )
    Identifying TBI Physiological States by Clustering Multivariate Clinical Time-Series Data. (arXiv:2303.13024v3 [cs.LG] UPDATED)
    Determining clinically relevant physiological states from multivariate time series data with missing values is essential for providing appropriate treatment for acute conditions such as Traumatic Brain Injury (TBI), respiratory failure, and heart failure. Utilizing non-temporal clustering or data imputation and aggregation techniques may lead to loss of valuable information and biased analyses. In our study, we apply the SLAC-Time algorithm, an innovative self-supervision-based approach that maintains data integrity by avoiding imputation or aggregation, offering a more useful representation of acute patient states. By using SLAC-Time to cluster data in a large research dataset, we identified three distinct TBI physiological states and their specific feature profiles. We employed various clustering evaluation metrics and incorporated input from a clinical domain expert to validate and interpret the identified physiological states. Further, we discovered how specific clinical events and interventions can influence patient states and state transitions.  ( 2 min )
    Mobility-Aware Joint User Scheduling and Resource Allocation for Low Latency Federated Learning. (arXiv:2307.09263v1 [cs.DC])
    As an efficient distributed machine learning approach, Federated learning (FL) can obtain a shared model by iterative local model training at the user side and global model aggregating at the central server side, thereby protecting privacy of users. Mobile users in FL systems typically communicate with base stations (BSs) via wireless channels, where training performance could be degraded due to unreliable access caused by user mobility. However, existing work only investigates a static scenario or random initialization of user locations, which fail to capture mobility in real-world networks. To tackle this issue, we propose a practical model for user mobility in FL across multiple BSs, and develop a user scheduling and resource allocation method to minimize the training delay with constrained communication resources. Specifically, we first formulate an optimization problem with user mobility that jointly considers user selection, BS assignment to users, and bandwidth allocation to minimize the latency in each communication round. This optimization problem turned out to be NP-hard and we proposed a delay-aware greedy search algorithm (DAGSA) to solve it. Simulation results show that the proposed algorithm achieves better performance than the state-of-the-art baselines and a certain level of user mobility could improve training performance.  ( 2 min )
    Experimental Security Analysis of DNN-based Adaptive Cruise Control under Context-Aware Perception Attacks. (arXiv:2307.08939v1 [cs.CR])
    Adaptive Cruise Control (ACC) is a widely used driver assistance feature for maintaining desired speed and safe distance to the leading vehicles. This paper evaluates the security of the deep neural network (DNN) based ACC systems under stealthy perception attacks that strategically inject perturbations into camera data to cause forward collisions. We present a combined knowledge-and-data-driven approach to design a context-aware strategy for the selection of the most critical times for triggering the attacks and a novel optimization-based method for the adaptive generation of image perturbations at run-time. We evaluate the effectiveness of the proposed attack using an actual driving dataset and a realistic simulation platform with the control software from a production ACC system and a physical-world driving simulator while considering interventions by the driver and safety features such as Automatic Emergency Braking (AEB) and Forward Collision Warning (FCW). Experimental results show that the proposed attack achieves 142.9x higher success rate in causing accidents than random attacks and is mitigated 89.6% less by the safety features while being stealthy and robust to real-world factors and dynamic changes in the environment. This study provides insights into the role of human operators and basic safety interventions in preventing attacks.  ( 3 min )
    Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images. (arXiv:2307.08535v2 [eess.IV] UPDATED)
    Cine magnetic resonance imaging (MRI) is the current gold standard for the assessment of cardiac anatomy and function. However, it typically only acquires a set of two-dimensional (2D) slices of the underlying three-dimensional (3D) anatomy of the heart, thus limiting the understanding and analysis of both healthy and pathological cardiac morphology and physiology. In this paper, we propose a novel fully automatic surface reconstruction pipeline capable of reconstructing multi-class 3D cardiac anatomy meshes from raw cine MRI acquisitions. Its key component is a multi-class point cloud completion network (PCCN) capable of correcting both the sparsity and misalignment issues of the 3D reconstruction task in a unified model. We first evaluate the PCCN on a large synthetic dataset of biventricular anatomies and observe Chamfer distances between reconstructed and gold standard anatomies below or similar to the underlying image resolution for multiple levels of slice misalignment. Furthermore, we find a reduction in reconstruction error compared to a benchmark 3D U-Net by 32% and 24% in terms of Hausdorff distance and mean surface distance, respectively. We then apply the PCCN as part of our automated reconstruction pipeline to 1000 subjects from the UK Biobank study in a cross-domain transfer setting and demonstrate its ability to reconstruct accurate and topologically plausible biventricular heart meshes with clinical metrics comparable to the previous literature. Finally, we investigate the robustness of our proposed approach and observe its capacity to successfully handle multiple common outlier conditions.  ( 3 min )
    Unsupervised Learning of Distributional Properties can Supplement Human Labeling and Increase Active Learning Efficiency in Anomaly Detection. (arXiv:2307.08782v1 [cs.LG])
    Exfiltration of data via email is a serious cybersecurity threat for many organizations. Detecting data exfiltration (anomaly) patterns typically requires labeling, most often done by a human annotator, to reduce the high number of false alarms. Active Learning (AL) is a promising approach for labeling data efficiently, but it needs to choose an efficient order in which cases are to be labeled, and there are uncertainties as to what scoring procedure should be used to prioritize cases for labeling, especially when detecting rare cases of interest is crucial. We propose an adaptive AL sampling strategy that leverages the underlying prior data distribution, as well as model uncertainty, to produce batches of cases to be labeled that contain instances of rare anomalies. We show that (1) the classifier benefits from a batch of representative and informative instances of both normal and anomalous examples, (2) unsupervised anomaly detection plays a useful role in building the classifier in the early stages of training when relatively little labeling has been done thus far. Our approach to AL for anomaly detection outperformed existing AL approaches on three highly unbalanced UCI benchmarks and on one real-world redacted email data set.  ( 2 min )
    OxfordVGG Submission to the EGO4D AV Transcription Challenge. (arXiv:2307.09006v1 [cs.SD])
    This report presents the technical details of our submission on the EGO4D Audio-Visual (AV) Automatic Speech Recognition Challenge 2023 from the OxfordVGG team. We present WhisperX, a system for efficient speech transcription of long-form audio with word-level time alignment, along with two text normalisers which are publicly available. Our final submission obtained 56.0% of the Word Error Rate (WER) on the challenge test set, ranked 1st on the leaderboard. All baseline codes and models are available on https://github.com/m-bain/whisperX.  ( 2 min )
    Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory. (arXiv:2307.00497v2 [cs.LG] UPDATED)
    Deep learning models are prone to forgetting information learned in the past when trained on new data. This problem becomes even more pronounced in the context of federated learning (FL), where data is decentralized and subject to independent changes for each user. Continual Learning (CL) studies this so-called \textit{catastrophic forgetting} phenomenon primarily in centralized settings, where the learner has direct access to the complete training dataset. However, applying CL techniques to FL is not straightforward due to privacy concerns and resource limitations. This paper presents a framework for federated class incremental learning that utilizes a generative model to synthesize samples from past distributions instead of storing part of past data. Then, clients can leverage the generative model to mitigate catastrophic forgetting locally. The generative model is trained on the server using data-free methods at the end of each task without requesting data from clients. Therefore, it reduces the risk of data leakage as opposed to training it on the client's private data. We demonstrate significant improvements for the CIFAR-100 dataset compared to existing baselines.  ( 2 min )
    On-the-fly machine learning for parametrization of the effective Hamiltonian. (arXiv:2307.08929v1 [cond-mat.mtrl-sci])
    The first-principles-based effective Hamiltonian is widely used to predict and simulate the properties of ferroelectrics and relaxor ferroelectrics. However, the parametrization method of the effective Hamiltonian is complicated and hardly can resolve the systems with complex interactions and/or complex components. Here, we developed an on-the-fly machine learning approach to parametrize the effective Hamiltonian based on Bayesian linear regression. The parametrization is completed in molecular dynamics simulations, with the energy, forces and stress predicted at each step along with their uncertainties. First-principles calculations are executed when the uncertainties are large to retrain the parameters. This approach provides a universal and automatic way to compute the effective Hamiltonian parameters for any considered systems including complex systems which previous methods can not handle. BaTiO3 and Pb(Sc,Ta)O3 are taken as examples to show the accurateness of this approach comparing with conventional first-principles parametrization method.  ( 2 min )
    REX: Rapid Exploration and eXploitation for AI Agents. (arXiv:2307.08962v1 [cs.AI])
    In this paper, we propose an enhanced approach for Rapid Exploration and eXploitation for AI Agents called REX. Existing AutoGPT-style techniques have inherent limitations, such as a heavy reliance on precise descriptions for decision-making, and the lack of a systematic approach to leverage try-and-fail procedures akin to traditional Reinforcement Learning (RL). REX introduces an additional layer of rewards and integrates concepts similar to Upper Confidence Bound (UCB) scores, leading to more robust and efficient AI agent performance. This approach has the advantage of enabling the utilization of offline behaviors from logs and allowing seamless integration with existing foundation models while it does not require any model fine-tuning. Through comparative analysis with existing methods such as Chain-of-Thoughts(CoT) and Reasoning viA Planning(RAP), REX-based methods demonstrate comparable performance and, in certain cases, even surpass the results achieved by these existing techniques. Notably, REX-based methods exhibit remarkable reductions in execution time, enhancing their practical applicability across a diverse set of scenarios.  ( 2 min )
    Deep Learning with Passive Optical Nonlinear Mapping. (arXiv:2307.08558v2 [physics.optics] UPDATED)
    Deep learning has fundamentally transformed artificial intelligence, but the ever-increasing complexity in deep learning models calls for specialized hardware accelerators. Optical accelerators can potentially offer enhanced performance, scalability, and energy efficiency. However, achieving nonlinear mapping, a critical component of neural networks, remains challenging optically. Here, we introduce a design that leverages multiple scattering in a reverberating cavity to passively induce optical nonlinear random mapping, without the need for additional laser power. A key advantage emerging from our work is that we show we can perform optical data compression, facilitated by multiple scattering in the cavity, to efficiently compress and retain vital information while also decreasing data dimensionality. This allows rapid optical information processing and generation of low dimensional mixtures of highly nonlinear features. These are particularly useful for applications demanding high-speed analysis and responses such as in edge computing devices. Utilizing rapid optical information processing capabilities, our optical platforms could potentially offer more efficient and real-time processing solutions for a broad range of applications. We demonstrate the efficacy of our design in improving computational performance across tasks, including classification, image reconstruction, key-point detection, and object detection, all achieved through optical data compression combined with a digital decoder. Notably, we observed high performance, at an extreme compression ratio, for real-time pedestrian detection. Our findings pave the way for novel algorithms and architectural designs for optical computing.  ( 3 min )
    TabText: A Flexible and Contextual Approach to Tabular Data Representation. (arXiv:2206.10381v3 [cs.LG] UPDATED)
    Tabular data is essential for applying machine learning tasks across various industries. However, traditional data processing methods do not fully utilize all the information available in the tables, ignoring important contextual information such as column header descriptions. In addition, pre-processing data into a tabular format can remain a labor-intensive bottleneck in model development. This work introduces TabText, a processing and feature extraction framework that extracts contextual information from tabular data structures. TabText addresses processing difficulties by converting the content into language and utilizing pre-trained large language models (LLMs). We evaluate our framework on nine healthcare prediction tasks ranging from patient discharge, ICU admission, and mortality. We show that 1) applying our TabText framework enables the generation of high-performing and simple machine learning baseline models with minimal data pre-processing, and 2) augmenting pre-processed tabular data with TabText representations improves the average and worst-case AUC performance of standard machine learning models by as much as 6%.  ( 2 min )
    DiTTO: Diffusion-inspired Temporal Transformer Operator. (arXiv:2307.09072v1 [cs.LG])
    Solving partial differential equations (PDEs) using a data-driven approach has become increasingly common. The recent development of the operator learning paradigm has enabled the solution of a broader range of PDE-related problems. We propose an operator learning method to solve time-dependent PDEs continuously in time without needing any temporal discretization. The proposed approach, named DiTTO, is inspired by latent diffusion models. While diffusion models are usually used in generative artificial intelligence tasks, their time-conditioning mechanism is extremely useful for PDEs. The diffusion-inspired framework is combined with elements from the Transformer architecture to improve its capabilities. We demonstrate the effectiveness of the new approach on a wide variety of PDEs in multiple dimensions, namely the 1-D Burgers' equation, 2-D Navier-Stokes equations, and the acoustic wave equation in 2-D and 3-D. DiTTO achieves state-of-the-art results in terms of accuracy for these problems. We also present a method to improve the performance of DiTTO by using fast sampling concepts from diffusion models. Finally, we show that DiTTO can accurately perform zero-shot super-resolution in time.  ( 2 min )
    Gradient Surgery for One-shot Unlearning on Generative Model. (arXiv:2307.04550v2 [cs.LG] UPDATED)
    Recent regulation on right-to-be-forgotten emerges tons of interest in unlearning pre-trained machine learning models. While approximating a straightforward yet expensive approach of retrain-from-scratch, recent machine unlearning methods unlearn a sample by updating weights to remove its influence on the weight parameters. In this paper, we introduce a simple yet effective approach to remove a data influence on the deep generative model. Inspired by works in multi-task learning, we propose to manipulate gradients to regularize the interplay of influence among samples by projecting gradients onto the normal plane of the gradients to be retained. Our work is agnostic to statistics of the removal samples, outperforming existing baselines while providing theoretical analysis for the first time in unlearning a generative model.  ( 2 min )
    TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT. (arXiv:2307.08674v2 [cs.AI] UPDATED)
    Tables are prevalent in real-world databases, requiring significant time and effort for humans to analyze and manipulate. The advancements in large language models (LLMs) have made it possible to interact with tables using natural language input, bringing this capability closer to reality. In this paper, we present TableGPT, a unified fine-tuned framework that enables LLMs to understand and operate on tables using external functional commands. It introduces the capability to seamlessly interact with tables, enabling a wide range of functionalities such as question answering, data manipulation (e.g., insert, delete, query, and modify operations), data visualization, analysis report generation, and automated prediction. TableGPT aims to provide convenience and accessibility to users by empowering them to effortlessly leverage tabular data. At the core of TableGPT lies the novel concept of global tabular representations, which empowers LLMs to gain a comprehensive understanding of the entire table beyond meta-information. By jointly training LLMs on both table and text modalities, TableGPT achieves a deep understanding of tabular data and the ability to perform complex operations on tables through chain-of-command instructions. Importantly, TableGPT offers the advantage of being a self-contained system rather than relying on external API interfaces. Moreover, it supports efficient data process flow, query rejection (when appropriate) and private deployment, enabling faster domain data fine-tuning and ensuring data privacy, which enhances the framework's adaptability to specific use cases.  ( 3 min )
    Efficient Strongly Polynomial Algorithms for Quantile Regression. (arXiv:2307.08706v1 [cs.CG])
    Linear Regression is a seminal technique in statistics and machine learning, where the objective is to build linear predictive models between a response (i.e., dependent) variable and one or more predictor (i.e., independent) variables. In this paper, we revisit the classical technique of Quantile Regression (QR), which is statistically a more robust alternative to the other classical technique of Ordinary Least Square Regression (OLS). However, while there exist efficient algorithms for OLS, almost all of the known results for QR are only weakly polynomial. Towards filling this gap, this paper proposes several efficient strongly polynomial algorithms for QR for various settings. For two dimensional QR, making a connection to the geometric concept of $k$-set, we propose an algorithm with a deterministic worst-case time complexity of $\mathcal{O}(n^{4/3} polylog(n))$ and an expected time complexity of $\mathcal{O}(n^{4/3})$ for the randomized version. We also propose a randomized divide-and-conquer algorithm -- RandomizedQR with an expected time complexity of $\mathcal{O}(n\log^2{(n)})$ for two dimensional QR problem. For the general case with more than two dimensions, our RandomizedQR algorithm has an expected time complexity of $\mathcal{O}(n^{d-1}\log^2{(n)})$.  ( 2 min )
    Mitigating Transformer Overconfidence via Lipschitz Regularization. (arXiv:2306.06849v2 [cs.LG] UPDATED)
    Though Transformers have achieved promising results in many computer vision tasks, they tend to be over-confident in predictions, as the standard Dot Product Self-Attention (DPSA) can barely preserve distance for the unbounded input domain. In this work, we fill this gap by proposing a novel Lipschitz Regularized Transformer (LRFormer). Specifically, we present a new similarity function with the distance within Banach Space to ensure the Lipschitzness and also regularize the term by a contractive Lipschitz Bound. The proposed method is analyzed with a theoretical guarantee, providing a rigorous basis for its effectiveness and reliability. Extensive experiments conducted on standard vision benchmarks demonstrate that our method outperforms the state-of-the-art single forward pass approaches in prediction, calibration, and uncertainty estimation.
    Continuous-Time Reinforcement Learning: New Design Algorithms with Theoretical Insights and Performance Guarantees. (arXiv:2307.08920v1 [eess.SY])
    Continuous-time nonlinear optimal control problems hold great promise in real-world applications. After decades of development, reinforcement learning (RL) has achieved some of the greatest successes as a general nonlinear control design method. However, a recent comprehensive analysis of state-of-the-art continuous-time RL (CT-RL) methods, namely, adaptive dynamic programming (ADP)-based CT-RL algorithms, reveals they face significant design challenges due to their complexity, numerical conditioning, and dimensional scaling issues. Despite advanced theoretical results, existing ADP CT-RL synthesis methods are inadequate in solving even small, academic problems. The goal of this work is thus to introduce a suite of new CT-RL algorithms for control of affine nonlinear systems. Our design approach relies on two important factors. First, our methods are applicable to physical systems that can be partitioned into smaller subproblems. This constructive consideration results in reduced dimensionality and greatly improved intuitiveness of design. Second, we introduce a new excitation framework to improve persistence of excitation (PE) and numerical conditioning performance via classical input/output insights. Such a design-centric approach is the first of its kind in the ADP CT-RL community. In this paper, we progressively introduce a suite of (decentralized) excitable integral reinforcement learning (EIRL) algorithms. We provide convergence and closed-loop stability guarantees, and we demonstrate these guarantees on a significant application problem of controlling an unstable, nonminimum phase hypersonic vehicle (HSV).
    Outlier-Robust Tensor Low-Rank Representation for Data Clustering. (arXiv:2307.09055v1 [stat.ML])
    Low-rank tensor analysis has received widespread attention with many practical applications. However, the tensor data are often contaminated by outliers or sample-specific corruptions. How to recover the tensor data that are corrupted by outliers and perform data clustering remains a challenging problem. This paper develops an outlier-robust tensor low-rank representation (OR-TLRR) method for simultaneous outlier detection and tensor data clustering based on the tensor singular value decomposition (t-SVD) algebraic framework. It is motivated by the recently proposed tensor-tensor product induced by invertible linear transforms that satisfy certain conditions. For tensor observations with arbitrary outlier corruptions, OR-TLRR has provable performance guarantee for exactly recovering the row space of clean data and detecting outliers under mild conditions. Moreover, an extension of OR-TLRR is also proposed to handle the case when parts of the data are missing. Finally, extensive experimental results on both synthetic and real data demonstrate the effectiveness of the proposed algorithms.
    Continuous Monte Carlo Graph Search. (arXiv:2210.01426v2 [cs.AI] UPDATED)
    In many complex sequential decision-making tasks, online planning is crucial for high performance. For efficient online planning, Monte Carlo Tree Search (MCTS) employs a principled mechanism for trading off exploration for exploitation. MCTS outperforms comparison methods in many discrete decision-making domains such as Go, Chess, and Shogi. Following, extensions of MCTS to continuous domains have been proposed. However, the inherent high branching factor and the resulting explosion of search tree size are limiting existing methods. To address this problem, we propose Continuous Monte Carlo Graph Search (CMCGS), a novel extension of MCTS to online planning in environments with continuous state and action spaces. CMCGS takes advantage of the insight that, during planning, sharing the same action policy between several states can yield high performance. To implement this idea, at each time step, CMCGS clusters similar states into a limited number of stochastic action bandit nodes, which produce a layered directed graph instead of an MCTS search tree. Experimental evaluation shows that CMCGS outperforms comparable planning methods in several complex continuous DeepMind Control Suite benchmarks and a 2D navigation task with limited sample budgets. Furthermore, CMCGS can be parallelized to scale up and it outperforms the Cross-Entropy Method (CEM) in continuous control with learned dynamics models.
    Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in Chinchilla. (arXiv:2307.09458v1 [cs.LG])
    \emph{Circuit analysis} is a promising technique for understanding the internal mechanisms of language models. However, existing analyses are done in small models far from the state of the art. To address this, we present a case study of circuit analysis in the 70B Chinchilla model, aiming to test the scalability of circuit analysis. In particular, we study multiple-choice question answering, and investigate Chinchilla's capability to identify the correct answer \emph{label} given knowledge of the correct answer \emph{text}. We find that the existing techniques of logit attribution, attention pattern visualization, and activation patching naturally scale to Chinchilla, allowing us to identify and categorize a small set of `output nodes' (attention heads and MLPs). We further study the `correct letter' category of attention heads aiming to understand the semantics of their features, with mixed results. For normal multiple-choice question answers, we significantly compress the query, key and value subspaces of the head without loss of performance when operating on the answer labels for multiple-choice questions, and we show that the query and key subspaces represent an `Nth item in an enumeration' feature to at least some extent. However, when we attempt to use this explanation to understand the heads' behaviour on a more general distribution including randomized answer labels, we find that it is only a partial explanation, suggesting there is more to learn about the operation of `correct letter' heads on multiple choice question answering.
    Discretization-based ensemble model for robust learning in IoT. (arXiv:2307.08955v1 [cs.LG])
    IoT device identification is the process of recognizing and verifying connected IoT devices to the network. This is an essential process for ensuring that only authorized devices can access the network, and it is necessary for network management and maintenance. In recent years, machine learning models have been used widely for automating the process of identifying devices in the network. However, these models are vulnerable to adversarial attacks that can compromise their accuracy and effectiveness. To better secure device identification models, discretization techniques enable reduction in the sensitivity of machine learning models to adversarial attacks contributing to the stability and reliability of the model. On the other hand, Ensemble methods combine multiple heterogeneous models to reduce the impact of remaining noise or errors in the model. Therefore, in this paper, we integrate discretization techniques and ensemble methods and examine it on model robustness against adversarial attacks. In other words, we propose a discretization-based ensemble stacking technique to improve the security of our ML models. We evaluate the performance of different ML-based IoT device identification models against white box and black box attacks using a real-world dataset comprised of network traffic from 28 IoT devices. We demonstrate that the proposed method enables robustness to the models for IoT device identification.
    Neural Network Pruning as Spectrum Preserving Process. (arXiv:2307.08982v1 [cs.LG])
    Neural networks have achieved remarkable performance in various application domains. Nevertheless, a large number of weights in pre-trained deep neural networks prohibit them from being deployed on smartphones and embedded systems. It is highly desirable to obtain lightweight versions of neural networks for inference in edge devices. Many cost-effective approaches were proposed to prune dense and convolutional layers that are common in deep neural networks and dominant in the parameter space. However, a unified theoretical foundation for the problem mostly is missing. In this paper, we identify the close connection between matrix spectrum learning and neural network training for dense and convolutional layers and argue that weight pruning is essentially a matrix sparsification process to preserve the spectrum. Based on the analysis, we also propose a matrix sparsification algorithm tailored for neural network pruning that yields better pruning result. We carefully design and conduct experiments to support our arguments. Hence we provide a consolidated viewpoint for neural network pruning and enhance the interpretability of deep neural networks by identifying and preserving the critical neural weights.
    CB-HVTNet: A channel-boosted hybrid vision transformer network for lymphocyte assessment in histopathological images. (arXiv:2305.09211v2 [eess.IV] UPDATED)
    Transformers, due to their ability to learn long range dependencies, have overcome the shortcomings of convolutional neural networks (CNNs) for global perspective learning. Therefore, they have gained the focus of researchers for several vision related tasks including medical diagnosis. However, their multi-head attention module only captures global level feature representations, which is insufficient for medical images. To address this issue, we propose a Channel Boosted Hybrid Vision Transformer (CB HVT) that uses transfer learning to generate boosted channels and employs both transformers and CNNs to analyse lymphocytes in histopathological images. The proposed CB HVT comprises five modules, including a channel generation module, channel exploitation module, channel merging module, region-aware module, and a detection and segmentation head, which work together to effectively identify lymphocytes. The channel generation module uses the idea of channel boosting through transfer learning to extract diverse channels from different auxiliary learners. In the CB HVT, these boosted channels are first concatenated and ranked using an attention mechanism in the channel exploitation module. A fusion block is then utilized in the channel merging module for a gradual and systematic merging of the diverse boosted channels to improve the network's learning representations. The CB HVT also employs a proposal network in its region aware module and a head to effectively identify objects, even in overlapping regions and with artifacts. We evaluated the proposed CB HVT on two publicly available datasets for lymphocyte assessment in histopathological images. The results show that CB HVT outperformed other state of the art detection models, and has good generalization ability, demonstrating its value as a tool for pathologists.
    Intuitionistic Fuzzy Broad Learning System: Enhancing Robustness Against Noise and Outliers. (arXiv:2307.08713v1 [cs.LG])
    In the realm of data classification, broad learning system (BLS) has proven to be a potent tool that utilizes a layer-by-layer feed-forward neural network. It consists of feature learning and enhancement segments, working together to extract intricate features from input data. The traditional BLS treats all samples as equally significant, which makes it less robust and less effective for real-world datasets with noises and outliers. To address this issue, we propose the fuzzy BLS (F-BLS) model, which assigns a fuzzy membership value to each training point to reduce the influence of noises and outliers. In assigning the membership value, the F-BLS model solely considers the distance from samples to the class center in the original feature space without incorporating the extent of non-belongingness to a class. We further propose a novel BLS based on intuitionistic fuzzy theory (IF-BLS). The proposed IF-BLS utilizes intuitionistic fuzzy numbers based on fuzzy membership and non-membership values to assign scores to training points in the high-dimensional feature space by using a kernel function. We evaluate the performance of proposed F-BLS and IF-BLS models on 44 UCI benchmark datasets across diverse domains. Furthermore, Gaussian noise is added to some UCI datasets to assess the robustness of the proposed F-BLS and IF-BLS models. Experimental results demonstrate superior generalization performance of the proposed F-BLS and IF-BLS models compared to baseline models, both with and without Gaussian noise. Additionally, we implement the proposed F-BLS and IF-BLS models on the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset, and promising results showcase the models effectiveness in real-world applications. The proposed methods offer a promising solution to enhance the BLS frameworks ability to handle noise and outliers.
    MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results. (arXiv:2307.09143v1 [cs.CV])
    Small Object Detection (SOD) is an important machine vision topic because (i) a variety of real-world applications require object detection for distant objects and (ii) SOD is a challenging task due to the noisy, blurred, and less-informative image appearances of small objects. This paper proposes a new SOD dataset consisting of 39,070 images including 137,121 bird instances, which is called the Small Object Detection for Spotting Birds (SOD4SB) dataset. The detail of the challenge with the SOD4SB dataset is introduced in this paper. In total, 223 participants joined this challenge. This paper briefly introduces the award-winning methods. The dataset, the baseline code, and the website for evaluation on the public testset are publicly available.
    The Score-Difference Flow for Implicit Generative Modeling. (arXiv:2304.12906v2 [cs.LG] UPDATED)
    Implicit generative modeling (IGM) aims to produce samples of synthetic data matching the characteristics of a target data distribution. Recent work (e.g. score-matching networks, diffusion models) has approached the IGM problem from the perspective of pushing synthetic source data toward the target distribution via dynamical perturbations or flows in the ambient space. In this direction, we present the score difference (SD) between arbitrary target and source distributions as a flow that optimally reduces the Kullback-Leibler divergence between them while also solving the Schroedinger bridge problem. We apply the SD flow to convenient proxy distributions, which are aligned if and only if the original distributions are aligned. We demonstrate the formal equivalence of this formulation to denoising diffusion models under certain conditions. We also show that the training of generative adversarial networks includes a hidden data-optimization sub-problem, which induces the SD flow under certain choices of loss function when the discriminator is optimal. As a result, the SD flow provides a theoretical link between model classes that individually address the three challenges of the "generative modeling trilemma" -- high sample quality, mode coverage, and fast sampling -- thereby setting the stage for a unified approach.
    Meta-Polyp: a baseline for efficient Polyp segmentation. (arXiv:2305.07848v3 [eess.IV] UPDATED)
    In recent years, polyp segmentation has gained significant importance, and many methods have been developed using CNN, Vision Transformer, and Transformer techniques to achieve competitive results. However, these methods often face difficulties when dealing with out-of-distribution datasets, missing boundaries, and small polyps. In 2022, Meta-Former was introduced as a new baseline for vision, which not only improved the performance of multi-task computer vision but also addressed the limitations of the Vision Transformer and CNN family backbones. To further enhance segmentation, we propose a fusion of Meta-Former with UNet, along with the introduction of a Multi-scale Upsampling block with a level-up combination in the decoder stage to enhance the texture, also we propose the Convformer block base on the idea of the Meta-former to enhance the crucial information of the local feature. These blocks enable the combination of global information, such as the overall shape of the polyp, with local information and boundary information, which is crucial for the decision of the medical segmentation. Our proposed approach achieved competitive performance and obtained the top result in the State of the Art on the CVC-300 dataset, Kvasir, and CVC-ColonDB dataset. Apart from Kvasir-SEG, others are out-of-distribution datasets. The implementation can be found at: https://github.com/huyquoctrinh/MetaPolyp-CBMS2023.
    Efficient Prediction of Peptide Self-assembly through Sequential and Graphical Encoding. (arXiv:2307.09169v1 [q-bio.BM])
    In recent years, there has been an explosion of research on the application of deep learning to the prediction of various peptide properties, due to the significant development and market potential of peptides. Molecular dynamics has enabled the efficient collection of large peptide datasets, providing reliable training data for deep learning. However, the lack of systematic analysis of the peptide encoding, which is essential for AI-assisted peptide-related tasks, makes it an urgent problem to be solved for the improvement of prediction accuracy. To address this issue, we first collect a high-quality, colossal simulation dataset of peptide self-assembly containing over 62,000 samples generated by coarse-grained molecular dynamics (CGMD). Then, we systematically investigate the effect of peptide encoding of amino acids into sequences and molecular graphs using state-of-the-art sequential (i.e., RNN, LSTM, and Transformer) and structural deep learning models (i.e., GCN, GAT, and GraphSAGE), on the accuracy of peptide self-assembly prediction, an essential physiochemical process prior to any peptide-related applications. Extensive benchmarking studies have proven Transformer to be the most powerful sequence-encoding-based deep learning model, pushing the limit of peptide self-assembly prediction to decapeptides. In summary, this work provides a comprehensive benchmark analysis of peptide encoding with advanced deep learning models, serving as a guide for a wide range of peptide-related predictions such as isoelectric points, hydration free energy, etc.
    Exploiting Field Dependencies for Learning on Categorical Data. (arXiv:2307.09321v1 [cs.LG])
    Traditional approaches for learning on categorical data underexploit the dependencies between columns (\aka fields) in a dataset because they rely on the embedding of data points driven alone by the classification/regression loss. In contrast, we propose a novel method for learning on categorical data with the goal of exploiting dependencies between fields. Instead of modelling statistics of features globally (i.e., by the covariance matrix of features), we learn a global field dependency matrix that captures dependencies between fields and then we refine the global field dependency matrix at the instance-wise level with different weights (so-called local dependency modelling) w.r.t. each field to improve the modelling of the field dependencies. Our algorithm exploits the meta-learning paradigm, i.e., the dependency matrices are refined in the inner loop of the meta-learning algorithm without the use of labels, whereas the outer loop intertwines the updates of the embedding matrix (the matrix performing projection) and global dependency matrix in a supervised fashion (with the use of labels). Our method is simple yet it outperforms several state-of-the-art methods on six popular dataset benchmarks. Detailed ablation studies provide additional insights into our method.
    Unsupervised Embedding Quality Evaluation. (arXiv:2305.16562v2 [cs.LG] UPDATED)
    Unsupervised learning has recently significantly gained in popularity, especially with deep learning-based approaches. Despite numerous successes and approaching supervised-level performance on a variety of academic benchmarks, it is still hard to train and evaluate SSL models in practice due to the unsupervised nature of the problem. Even with networks trained in a supervised fashion, it is often unclear whether they will perform well when transferred to another domain. Past works are generally limited to assessing the amount of information contained in embeddings, which is most relevant for self-supervised learning of deep neural networks. This works chooses to follow a different approach: can we quantify how easy it is to linearly separate the data in a stable way? We survey the literature and uncover three methods that could be potentially used for evaluating quality of representations. We also introduce one novel method based on recent advances in understanding the high-dimensional geometric structure of self-supervised learning. We conduct extensive experiments and study the properties of these metrics and ones introduced in the previous work. Our results suggest that while there is no free lunch, there are metrics that can robustly estimate embedding quality in an unsupervised way.
    End-to-End Neural Network Training for Hyperbox-Based Classification. (arXiv:2307.09269v1 [cs.LG])
    Hyperbox-based classification has been seen as a promising technique in which decisions on the data are represented as a series of orthogonal, multidimensional boxes (i.e., hyperboxes) that are often interpretable and human-readable. However, existing methods are no longer capable of efficiently handling the increasing volume of data many application domains face nowadays. We address this gap by proposing a novel, fully differentiable framework for hyperbox-based classification via neural networks. In contrast to previous work, our hyperbox models can be efficiently trained in an end-to-end fashion, which leads to significantly reduced training times and superior classification results.
    A Cryogenic Memristive Neural Decoder for Fault-tolerant Quantum Error Correction. (arXiv:2307.09463v1 [quant-ph])
    Neural decoders for quantum error correction (QEC) rely on neural networks to classify syndromes extracted from error correction codes and find appropriate recovery operators to protect logical information against errors. Despite the good performance of neural decoders, important practical requirements remain to be achieved, such as minimizing the decoding time to meet typical rates of syndrome generation in repeated error correction schemes, and ensuring the scalability of the decoding approach as the code distance increases. Designing a dedicated integrated circuit to perform the decoding task in co-integration with a quantum processor appears necessary to reach these decoding time and scalability requirements, as routing signals in and out of a cryogenic environment to be processed externally leads to unnecessary delays and an eventual wiring bottleneck. In this work, we report the design and performance analysis of a neural decoder inference accelerator based on an in-memory computing (IMC) architecture, where crossbar arrays of resistive memory devices are employed to both store the synaptic weights of the decoder neural network and perform analog matrix-vector multiplications during inference. In proof-of-concept numerical experiments supported by experimental measurements, we investigate the impact of TiO$_\textrm{x}$-based memristive devices' non-idealities on decoding accuracy. Hardware-aware training methods are developed to mitigate the loss in accuracy, allowing the memristive neural decoders to achieve a pseudo-threshold of $9.23\times 10^{-4}$ for the distance-three surface code, whereas the equivalent digital neural decoder achieves a pseudo-threshold of $1.01\times 10^{-3}$. This work provides a pathway to scalable, fast, and low-power cryogenic IMC hardware for integrated QEC.
    Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble. (arXiv:2210.13108v2 [cs.LG] UPDATED)
    One of the primal challenges faced by utility companies is ensuring efficient supply with minimal greenhouse gas emissions. The advent of smart meters and smart grids provide an unprecedented advantage in realizing an optimised supply of thermal energies through proactive techniques such as load forecasting. In this paper, we propose a forecasting framework for heat demand based on neural networks where the time series are encoded as scalograms equipped with the capacity of embedding exogenous variables such as weather, and holiday/non-holiday. Subsequently, CNNs are utilized to predict the heat load multi-step ahead. Finally, the proposed framework is compared with other state-of-the-art methods, such as SARIMAX and LSTM. The quantitative results from retrospective experiments show that the proposed framework consistently outperforms the state-of-the-art baseline method with real-world data acquired from Denmark. A minimal mean error of 7.54% for MAPE and 417kW for RMSE is achieved with the proposed framework in comparison to all other methods.
    MOCA: Self-supervised Representation Learning by Predicting Masked Online Codebook Assignments. (arXiv:2307.09361v1 [cs.CV])
    Self-supervised learning can be used for mitigating the greedy needs of Vision Transformer networks for very large fully-annotated datasets. Different classes of self-supervised learning offer representations with either good contextual reasoning properties, e.g., using masked image modeling strategies, or invariance to image perturbations, e.g., with contrastive methods. In this work, we propose a single-stage and standalone method, MOCA, which unifies both desired properties using novel mask-and-predict objectives defined with high-level features (instead of pixel-level details). Moreover, we show how to effectively employ both learning paradigms in a synergistic and computation-efficient way. Doing so, we achieve new state-of-the-art results on low-shot settings and strong experimental results in various evaluation protocols with a training that is at least 3 times faster than prior methods.
    Exploiting Noise as a Resource for Computation and Learning in Spiking Neural Networks. (arXiv:2305.16044v5 [cs.NE] UPDATED)
    Networks of spiking neurons underpin the extraordinary information-processing capabilities of the brain and have become pillar models in neuromorphic artificial intelligence. Despite extensive research on spiking neural networks (SNNs), most studies are established on deterministic models, overlooking the inherent non-deterministic, noisy nature of neural computations. This study introduces the noisy spiking neural network (NSNN) and the noise-driven learning rule (NDL) by incorporating noisy neuronal dynamics to exploit the computational advantages of noisy neural processing. NSNN provides a theoretical framework that yields scalable, flexible, and reliable computation. We demonstrate that NSNN leads to spiking neural models with competitive performance, improved robustness against challenging perturbations than deterministic SNNs, and better reproducing probabilistic neural computation in neural coding. This study offers a powerful and easy-to-use tool for machine learning, neuromorphic intelligence practitioners, and computational neuroscience researchers.
    Towards Ordinal Data Science. (arXiv:2307.09477v1 [cs.AI])
    Order is one of the main instruments to measure the relationship between objects in (empirical) data. However, compared to methods that use numerical properties of objects, the amount of ordinal methods developed is rather small. One reason for this is the limited availability of computational resources in the last century that would have been required for ordinal computations. Another reason -- particularly important for this line of research -- is that order-based methods are often seen as too mathematically rigorous for applying them to real-world data. In this paper, we will therefore discuss different means for measuring and 'calculating' with ordinal structures -- a specific class of directed graphs -- and show how to infer knowledge from them. Our aim is to establish Ordinal Data Science as a fundamentally new research agenda. Besides cross-fertilization with other cornerstone machine learning and knowledge representation methods, a broad range of disciplines will benefit from this endeavor, including, psychology, sociology, economics, web science, knowledge engineering, scientometrics.
    Smooth Attention for Deep Multiple Instance Learning: Application to CT Intracranial Hemorrhage Detection. (arXiv:2307.09457v1 [eess.IV])
    Multiple Instance Learning (MIL) has been widely applied to medical imaging diagnosis, where bag labels are known and instance labels inside bags are unknown. Traditional MIL assumes that instances in each bag are independent samples from a given distribution. However, instances are often spatially or sequentially ordered, and one would expect similar diagnostic importance for neighboring instances. To address this, in this study, we propose a smooth attention deep MIL (SA-DMIL) model. Smoothness is achieved by the introduction of first and second order constraints on the latent function encoding the attention paid to each instance in a bag. The method is applied to the detection of intracranial hemorrhage (ICH) on head CT scans. The results show that this novel SA-DMIL: (a) achieves better performance than the non-smooth attention MIL at both scan (bag) and slice (instance) levels; (b) learns spatial dependencies between slices; and (c) outperforms current state-of-the-art MIL methods on the same ICH test set.
    SLCA: Slow Learner with Classifier Alignment for Continual Learning on a Pre-trained Model. (arXiv:2303.05118v2 [cs.CV] UPDATED)
    The goal of continual learning is to improve the performance of recognition models in learning sequentially arrived data. Although most existing works are established on the premise of learning from scratch, growing efforts have been devoted to incorporating the benefits of pre-training. However, how to adaptively exploit the pre-trained knowledge for each incremental task while maintaining its generalizability remains an open question. In this work, we present an extensive analysis for continual learning on a pre-trained model (CLPM), and attribute the key challenge to a progressive overfitting problem. Observing that selectively reducing the learning rate can almost resolve this issue in the representation layer, we propose a simple but extremely effective approach named Slow Learner with Classifier Alignment (SLCA), which further improves the classification layer by modeling the class-wise distributions and aligning the classification layers in a post-hoc fashion. Across a variety of scenarios, our proposal provides substantial improvements for CLPM (e.g., up to 49.76%, 50.05%, 44.69% and 40.16% on Split CIFAR-100, Split ImageNet-R, Split CUB-200 and Split Cars-196, respectively), and thus outperforms state-of-the-art approaches by a large margin. Based on such a strong baseline, critical factors and promising directions are analyzed in-depth to facilitate subsequent research.
    Deep Riemannian Networks for EEG Decoding. (arXiv:2212.10426v5 [cs.LG] UPDATED)
    State-of-the-art performance in electroencephalography (EEG) decoding tasks is currently often achieved with either Deep-Learning (DL) or Riemannian-Geometry-based decoders (RBDs). Recently, there is growing interest in Deep Riemannian Networks (DRNs) possibly combining the advantages of both previous classes of methods. However, there are still a range of topics where additional insight is needed to pave the way for a more widespread application of DRNs in EEG. These include architecture design questions such as network size and end-to-end ability.How these factors affect model performance has not been explored. Additionally, it is not clear how the data within these networks is transformed, and whether this would correlate with traditional EEG decoding. Our study aims to lay the groundwork in the area of these topics through the analysis of DRNs for EEG with a wide range of hyperparameters. Networks were tested on two public EEG datasets and compared with state-of-the-art ConvNets. Here we propose end-to-end EEG SPDNet (EE(G)-SPDNet), and we show that this wide, end-to-end DRN can outperform the ConvNets, and in doing so use physiologically plausible frequency regions. We also show that the end-to-end approach learns more complex filters than traditional band-pass filters targeting the classical alpha, beta, and gamma frequency bands of the EEG, and that performance can benefit from channel specific filtering approaches. Additionally, architectural analysis revealed areas for further improvement due to the possible loss of Riemannian specific information throughout the network. Our study thus shows how to design and train DRNs to infer task-related information from the raw EEG without the need of handcrafted filterbanks and highlights the potential of end-to-end DRNs such as EE(G)-SPDNet for high-performance EEG decoding.
    Scaling Laws for Imitation Learning in NetHack. (arXiv:2307.09423v1 [cs.LG])
    Imitation Learning (IL) is one of the most widely used methods in machine learning. Yet, while powerful, many works find it is often not able to fully recover the underlying expert behavior. However, none of these works deeply investigate the role of scaling up the model and data size. Inspired by recent work in Natural Language Processing (NLP) where "scaling up" has resulted in increasingly more capable LLMs, we investigate whether carefully scaling up model and data size can bring similar improvements in the imitation learning setting. To demonstrate our findings, we focus on the game of NetHack, a challenging environment featuring procedural generation, stochasticity, long-term dependencies, and partial observability. We find IL loss and mean return scale smoothly with the compute budget and are strongly correlated, resulting in power laws for training compute-optimal IL agents with respect to model size and number of samples. We forecast and train several NetHack agents with IL and find they outperform prior state-of-the-art by at least 2x in all settings. Our work both demonstrates the scaling behavior of imitation learning in a challenging domain, as well as the viability of scaling up current approaches for increasingly capable agents in NetHack, a game that remains elusively hard for current AI systems.
    Detecting Throat Cancer from Speech Signals Using Machine Learning: A Reproducible Literature Review. (arXiv:2307.09230v1 [cs.LG])
    In this work we perform a scoping review of the current literature on the detection of throat cancer from speech recordings using machine learning and artificial intelligence. We find 22 papers within this area and discuss their methods and results. We split these papers into two groups - nine performing binary classification, and 13 performing multi-class classification. The papers present a range of methods with neural networks being most commonly implemented. Many features are also extracted from the audio before classification, with the most common bring mel-frequency cepstral coefficients. None of the papers found in this search have associated code repositories and as such are not reproducible. Therefore, we create a publicly available code repository of our own classifiers. We use transfer learning on a multi-class problem, classifying three pathologies and healthy controls. Using this technique we achieve an unweighted average recall of 53.54%, sensitivity of 83.14%, and specificity of 64.00%. We compare our classifiers with the results obtained on the same dataset and find similar results.
    PAC Neural Prediction Set Learning to Quantify the Uncertainty of Generative Language Models. (arXiv:2307.09254v1 [cs.LG])
    Uncertainty learning and quantification of models are crucial tasks to enhance the trustworthiness of the models. Importantly, the recent surge of generative language models (GLMs) emphasizes the need for reliable uncertainty quantification due to the concerns on generating hallucinated facts. In this paper, we propose to learn neural prediction set models that comes with the probably approximately correct (PAC) guarantee for quantifying the uncertainty of GLMs. Unlike existing prediction set models, which are parameterized by a scalar value, we propose to parameterize prediction sets via neural networks, which achieves more precise uncertainty quantification but still satisfies the PAC guarantee. We demonstrate the efficacy of our method on four types of language datasets and six types of models by showing that our method improves the quantified uncertainty by $63\%$ on average, compared to a standard baseline method.
    SparseOptimizer: Sparsify Language Models through Moreau-Yosida Regularization and Accelerate via Compiler Co-design. (arXiv:2306.15656v3 [cs.LG] UPDATED)
    This paper introduces SparseOptimizer, a novel deep learning optimizer that exploits Moreau-Yosida regularization to naturally induce sparsity in large language models such as BERT, ALBERT and GPT. Key to the design of SparseOptimizer is an embedded shrinkage operator, which imparts sparsity directly within the optimization process. This operator, backed by a sound theoretical framework, includes an analytical solution, thereby reinforcing the optimizer's robustness and efficacy. Crucially, SparseOptimizer's plug-and-play functionality eradicates the need for code modifications, making it a universally adaptable tool for a wide array of large language models. Empirical evaluations on benchmark datasets such as GLUE, RACE, SQuAD1, and SQuAD2 confirm that SparseBERT and SparseALBERT, when sparsified using SparseOptimizer, achieve performance comparable to their dense counterparts, BERT and ALBERT, while significantly reducing their parameter count. Further, this work proposes an innovative optimizer-compiler co-design strategy, demonstrating the potential of inference acceleration (\textbf{3.37x}, \textbf{6.30x}, and \textbf{7.15x} in comparison with Pytorch, TensorFlow, and LLVM generic compile, respectively) in SparseBERT when paired with an appropriately designed compiler. This study represents a significant step forward in the evolution of efficient, scalable, and high-performing large language models, setting a precedent for future exploration and optimization in this domain. The SparseOptimizer code and SparseALBERT model will be publicly available upon paper acceptance.
    Multi-Objective GFlowNets. (arXiv:2210.12765v2 [cs.LG] UPDATED)
    We study the problem of generating diverse candidates in the context of Multi-Objective Optimization. In many applications of machine learning such as drug discovery and material design, the goal is to generate candidates which simultaneously optimize a set of potentially conflicting objectives. Moreover, these objectives are often imperfect evaluations of some underlying property of interest, making it important to generate diverse candidates to have multiple options for expensive downstream evaluations. We propose Multi-Objective GFlowNets (MOGFNs), a novel method for generating diverse Pareto optimal solutions, based on GFlowNets. We introduce two variants of MOGFNs: MOGFN-PC, which models a family of independent sub-problems defined by a scalarization function, with reward-conditional GFlowNets, and MOGFN-AL, which solves a sequence of sub-problems defined by an acquisition function in an active learning loop. Our experiments on wide variety of synthetic and benchmark tasks demonstrate advantages of the proposed methods in terms of the Pareto performance and importantly, improved candidate diversity, which is the main contribution of this work.
    Do DL models and training environments have an impact on energy consumption?. (arXiv:2307.05520v2 [cs.LG] UPDATED)
    Current research in the computer vision field mainly focuses on improving Deep Learning (DL) correctness and inference time performance. However, there is still little work on the huge carbon footprint that has training DL models. This study aims to analyze the impact of the model architecture and training environment when training greener computer vision models. We divide this goal into two research questions. First, we analyze the effects of model architecture on achieving greener models while keeping correctness at optimal levels. Second, we study the influence of the training environment on producing greener models. To investigate these relationships, we collect multiple metrics related to energy efficiency and model correctness during the models' training. Then, we outline the trade-offs between the measured energy efficiency and the models' correctness regarding model architecture, and their relationship with the training environment. We conduct this research in the context of a computer vision system for image classification. In conclusion, we show that selecting the proper model architecture and training environment can reduce energy consumption dramatically (up to 98.83%) at the cost of negligible decreases in correctness. Also, we find evidence that GPUs should scale with the models' computational complexity for better energy efficiency.
    Fusing Hand and Body Skeletons for Human Action Recognition in Assembly. (arXiv:2307.09238v1 [cs.CV])
    As collaborative robots (cobots) continue to gain popularity in industrial manufacturing, effective human-robot collaboration becomes crucial. Cobots should be able to recognize human actions to assist with assembly tasks and act autonomously. To achieve this, skeleton-based approaches are often used due to their ability to generalize across various people and environments. Although body skeleton approaches are widely used for action recognition, they may not be accurate enough for assembly actions where the worker's fingers and hands play a significant role. To address this limitation, we propose a method in which less detailed body skeletons are combined with highly detailed hand skeletons. We investigate CNNs and transformers, the latter of which are particularly adept at extracting and combining important information from both skeleton types using attention. This paper demonstrates the effectiveness of our proposed approach in enhancing action recognition in assembly scenarios.
    Revisiting the Robustness of the Minimum Error Entropy Criterion: A Transfer Learning Case Study. (arXiv:2307.08572v2 [cs.LG] UPDATED)
    Coping with distributional shifts is an important part of transfer learning methods in order to perform well in real-life tasks. However, most of the existing approaches in this area either focus on an ideal scenario in which the data does not contain noises or employ a complicated training paradigm or model design to deal with distributional shifts. In this paper, we revisit the robustness of the minimum error entropy (MEE) criterion, a widely used objective in statistical signal processing to deal with non-Gaussian noises, and investigate its feasibility and usefulness in real-life transfer learning regression tasks, where distributional shifts are common. Specifically, we put forward a new theoretical result showing the robustness of MEE against covariate shift. We also show that by simply replacing the mean squared error (MSE) loss with the MEE on basic transfer learning algorithms such as fine-tuning and linear probing, we can achieve competitive performance with respect to state-of-the-art transfer learning algorithms. We justify our arguments on both synthetic data and 5 real-world time-series data.
    Edit at your own risk: evaluating the robustness of edited models to distribution shifts. (arXiv:2303.00046v2 [cs.LG] UPDATED)
    The current trend toward ever-larger models makes standard retraining procedures an ever-more expensive burden. For this reason, there is growing interest in model editing, which enables computationally inexpensive, interpretable, post-hoc model modifications. While many model editing techniques are promising, research on the properties of edited models is largely limited to evaluation of validation accuracy. The robustness of edited models is an important and yet mostly unexplored topic. In this paper, we employ recently developed techniques from the field of deep learning robustness to investigate both how model editing affects the general robustness of a model, as well as the robustness of the specific behavior targeted by the edit. We find that edits tend to reduce general robustness, but that the degree of degradation depends on the editing algorithm and layers chosen. Motivated by these observations we introduce a new model editing algorithm, 1-layer interpolation (1-LI), which uses weight-space interpolation to navigate the trade-off between editing task accuracy and general robustness.
    Conformal prediction under ambiguous ground truth. (arXiv:2307.09302v1 [cs.LG])
    In safety-critical classification tasks, conformal prediction allows to perform rigorous uncertainty quantification by providing confidence sets including the true class with a user-specified probability. This generally assumes the availability of a held-out calibration set with access to ground truth labels. Unfortunately, in many domains, such labels are difficult to obtain and usually approximated by aggregating expert opinions. In fact, this holds true for almost all datasets, including well-known ones such as CIFAR and ImageNet. Applying conformal prediction using such labels underestimates uncertainty. Indeed, when expert opinions are not resolvable, there is inherent ambiguity present in the labels. That is, we do not have ``crisp'', definitive ground truth labels and this uncertainty should be taken into account during calibration. In this paper, we develop a conformal prediction framework for such ambiguous ground truth settings which relies on an approximation of the underlying posterior distribution of labels given inputs. We demonstrate our methodology on synthetic and real datasets, including a case study of skin condition classification in dermatology.
    Multimodal LLMs for health grounded in individual-specific data. (arXiv:2307.09018v1 [q-bio.QM])
    Foundation large language models (LLMs) have shown an impressive ability to solve tasks across a wide range of fields including health. To effectively solve personalized health tasks, LLMs need the ability to ingest a diversity of data modalities that are relevant to an individual's health status. In this paper, we take a step towards creating multimodal LLMs for health that are grounded in individual-specific data by developing a framework (HeLM: Health Large Language Model for Multimodal Understanding) that enables LLMs to use high-dimensional clinical modalities to estimate underlying disease risk. HeLM encodes complex data modalities by learning an encoder that maps them into the LLM's token embedding space and for simple modalities like tabular data by serializing the data into text. Using data from the UK Biobank, we show that HeLM can effectively use demographic and clinical features in addition to high-dimensional time-series data to estimate disease risk. For example, HeLM achieves an AUROC of 0.75 for asthma prediction when combining tabular and spirogram data modalities compared with 0.49 when only using tabular data. Overall, we find that HeLM outperforms or performs at parity with classical machine learning approaches across a selection of eight binary traits. Furthermore, we investigate the downstream uses of this model such as its generalizability to out-of-distribution traits and its ability to power conversations around individual health and wellness.
    A Unifying Framework for Differentially Private Sums under Continual Observation. (arXiv:2307.08970v1 [cs.LG])
    We study the problem of maintaining a differentially private decaying sum under continual observation. We give a unifying framework and an efficient algorithm for this problem for \emph{any sufficiently smooth} function. Our algorithm is the first differentially private algorithm that does not have a multiplicative error for polynomially-decaying weights. Our algorithm improves on all prior works on differentially private decaying sums under continual observation and recovers exactly the additive error for the special case of continual counting from Henzinger et al. (SODA 2023) as a corollary. Our algorithm is a variant of the factorization mechanism whose error depends on the $\gamma_2$ and $\gamma_F$ norm of the underlying matrix. We give a constructive proof for an almost exact upper bound on the $\gamma_2$ and $\gamma_F$ norm and an almost tight lower bound on the $\gamma_2$ norm for a large class of lower-triangular matrices. This is the first non-trivial lower bound for lower-triangular matrices whose non-zero entries are not all the same. It includes matrices for all continual decaying sums problems, resulting in an upper bound on the additive error of any differentially private decaying sums algorithm under continual observation. We also explore some implications of our result in discrepancy theory and operator algebra. Given the importance of the $\gamma_2$ norm in computer science and the extensive work in mathematics, we believe our result will have further applications.
    Nested Elimination: A Simple Algorithm for Best-Item Identification from Choice-Based Feedback. (arXiv:2307.09295v1 [cs.LG])
    We study the problem of best-item identification from choice-based feedback. In this problem, a company sequentially and adaptively shows display sets to a population of customers and collects their choices. The objective is to identify the most preferred item with the least number of samples and at a high confidence level. We propose an elimination-based algorithm, namely Nested Elimination (NE), which is inspired by the nested structure implied by the information-theoretic lower bound. NE is simple in structure, easy to implement, and has a strong theoretical guarantee for sample complexity. Specifically, NE utilizes an innovative elimination criterion and circumvents the need to solve any complex combinatorial optimization problem. We provide an instance-specific and non-asymptotic bound on the expected sample complexity of NE. We also show NE achieves high-order worst-case asymptotic optimality. Finally, numerical experiments from both synthetic and real data corroborate our theoretical findings.
    Towards Dynamic Causal Discovery with Rare Events: A Nonparametric Conditional Independence Test. (arXiv:2211.16596v5 [stat.ML] UPDATED)
    Causal phenomena associated with rare events occur across a wide range of engineering problems, such as risk-sensitive safety analysis, accident analysis and prevention, and extreme value theory. However, current methods for causal discovery are often unable to uncover causal links, between random variables in a dynamic setting, that manifest only when the variables first experience low-probability realizations. To address this issue, we introduce a novel statistical independence test on data collected from time-invariant dynamical systems in which rare but consequential events occur. In particular, we exploit the time-invariance of the underlying data to construct a superimposed dataset of the system state before rare events happen at different timesteps. We then design a conditional independence test on the reorganized data. We provide non-asymptotic sample complexity bounds for the consistency of our method, and validate its performance across various simulated and real-world datasets, including incident data collected from the Caltrans Performance Measurement System (PeMS). Code containing the datasets and experiments is publicly available.
    Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees. (arXiv:2305.11997v2 [stat.ML] UPDATED)
    There is an emerging interest in generating robust counterfactual explanations that would remain valid if the model is updated or changed even slightly. Towards finding robust counterfactuals, existing literature often assumes that the original model $m$ and the new model $M$ are bounded in the parameter space, i.e., $\|\text{Params}(M){-}\text{Params}(m)\|{<}\Delta$. However, models can often change significantly in the parameter space with little to no change in their predictions or accuracy on the given dataset. In this work, we introduce a mathematical abstraction termed \emph{naturally-occurring} model change, which allows for arbitrary changes in the parameter space such that the change in predictions on points that lie on the data manifold is limited. Next, we propose a measure -- that we call \emph{Stability} -- to quantify the robustness of counterfactuals to potential model changes for differentiable models, e.g., neural networks. Our main contribution is to show that counterfactuals with sufficiently high value of \emph{Stability} as defined by our measure will remain valid after potential ``naturally-occurring'' model changes with high probability (leveraging concentration bounds for Lipschitz function of independent Gaussians). Since our quantification depends on the local Lipschitz constant around a data point which is not always available, we also examine practical relaxations of our proposed measure and demonstrate experimentally how they can be incorporated to find robust counterfactuals for neural networks that are close, realistic, and remain valid after potential model changes. This work also has interesting connections with model multiplicity, also known as, the Rashomon effect.
    Data Cross-Segmentation for Improved Generalization in Reinforcement Learning Based Algorithmic Trading. (arXiv:2307.09377v1 [cs.LG])
    The use of machine learning in algorithmic trading systems is increasingly common. In a typical set-up, supervised learning is used to predict the future prices of assets, and those predictions drive a simple trading and execution strategy. This is quite effective when the predictions have sufficient signal, markets are liquid, and transaction costs are low. However, those conditions often do not hold in thinly traded financial markets and markets for differentiated assets such as real estate or vehicles. In these markets, the trading strategy must consider the long-term effects of taking positions that are relatively more difficult to change. In this work, we propose a Reinforcement Learning (RL) algorithm that trades based on signals from a learned predictive model and addresses these challenges. We test our algorithm on 20+ years of equity data from Bursa Malaysia.
    How is ChatGPT's behavior changing over time?. (arXiv:2307.09009v1 [cs.CL])
    GPT-3.5 and GPT-4 are the two most widely used large language model (LLM) services. However, when and how these models are updated over time is opaque. Here, we evaluate the March 2023 and June 2023 versions of GPT-3.5 and GPT-4 on four diverse tasks: 1) solving math problems, 2) answering sensitive/dangerous questions, 3) generating code and 4) visual reasoning. We find that the performance and behavior of both GPT-3.5 and GPT-4 can vary greatly over time. For example, GPT-4 (March 2023) was very good at identifying prime numbers (accuracy 97.6%) but GPT-4 (June 2023) was very poor on these same questions (accuracy 2.4%). Interestingly GPT-3.5 (June 2023) was much better than GPT-3.5 (March 2023) in this task. GPT-4 was less willing to answer sensitive questions in June than in March, and both GPT-4 and GPT-3.5 had more formatting mistakes in code generation in June than in March. Overall, our findings shows that the behavior of the same LLM service can change substantially in a relatively short amount of time, highlighting the need for continuous monitoring of LLM quality.
    Application of BERT in Wind Power Forecasting-Teletraan's Solution in Baidu KDD Cup 2022. (arXiv:2307.09248v1 [cs.LG])
    Nowadays, wind energy has drawn increasing attention as its important role in carbon neutrality and sustainable development. When wind power is integrated into the power grid, precise forecasting is necessary for the sustainability and security of the system. However, the unpredictable nature and long sequence prediction make it especially challenging. In this technical report, we introduce the BERT model applied for Baidu KDD Cup 2022, and the daily fluctuation is added by post-processing to make the predicted results in line with daily periodicity. Our solution achieves 3rd place of 2490 teams. The code is released athttps://github.com/LongxingTan/KDD2022-Baidu
    An Evaluation of Zero-Cost Proxies -- from Neural Architecture Performance to Model Robustness. (arXiv:2307.09365v1 [cs.LG])
    Zero-cost proxies are nowadays frequently studied and used to search for neural architectures. They show an impressive ability to predict the performance of architectures by making use of their untrained weights. These techniques allow for immense search speed-ups. So far the joint search for well-performing and robust architectures has received much less attention in the field of NAS. Therefore, the main focus of zero-cost proxies is the clean accuracy of architectures, whereas the model robustness should play an evenly important part. In this paper, we analyze the ability of common zero-cost proxies to serve as performance predictors for robustness in the popular NAS-Bench-201 search space. We are interested in the single prediction task for robustness and the joint multi-objective of clean and robust accuracy. We further analyze the feature importance of the proxies and show that predicting the robustness makes the prediction task from existing zero-cost proxies more challenging. As a result, the joint consideration of several proxies becomes necessary to predict a model's robustness while the clean accuracy can be regressed from a single such feature.
    Funnel-based Reward Shaping for Signal Temporal Logic Tasks in Reinforcement Learning. (arXiv:2212.03181v2 [eess.SY] UPDATED)
    Signal Temporal Logic (STL) is a powerful framework for describing the complex temporal and logical behaviour of the dynamical system. Numerous studies have attempted to employ reinforcement learning to learn a controller that enforces STL specifications; however, they have been unable to effectively tackle the challenges of ensuring robust satisfaction in continuous state space and maintaining tractability. In this paper, leveraging the concept of funnel functions, we propose a tractable reinforcement learning algorithm to learn a time-dependent policy for robust satisfaction of STL specification in continuous state space. We demonstrate the utility of our approach on several STL tasks using different environments.
    DESCN: Deep Entire Space Cross Networks for Individual Treatment Effect Estimation. (arXiv:2207.09920v2 [cs.LG] UPDATED)
    Causal Inference has wide applications in various areas such as E-commerce and precision medicine, and its performance heavily relies on the accurate estimation of the Individual Treatment Effect (ITE). Conventionally, ITE is predicted by modeling the treated and control response functions separately in their individual sample spaces. However, such an approach usually encounters two issues in practice, i.e. divergent distribution between treated and control groups due to treatment bias, and significant sample imbalance of their population sizes. This paper proposes Deep Entire Space Cross Networks (DESCN) to model treatment effects from an end-to-end perspective. DESCN captures the integrated information of the treatment propensity, the response, and the hidden treatment effect through a cross network in a multi-task learning manner. Our method jointly learns the treatment and response functions in the entire sample space to avoid treatment bias and employs an intermediate pseudo treatment effect prediction network to relieve sample imbalance. Extensive experiments are conducted on a synthetic dataset and a large-scaled production dataset from the E-commerce voucher distribution business. The results indicate that DESCN can successfully enhance the accuracy of ITE estimation and improve the uplift ranking performance. A sample of the production dataset and the source code are released to facilitate future research in the community, which is, to the best of our knowledge, the first large-scale public biased treatment dataset for causal inference.
    Online Observer-Based Inverse Reinforcement Learning. (arXiv:2011.02057v3 [eess.SY] UPDATED)
    In this paper, a novel approach to the output-feedback inverse reinforcement learning (IRL) problem is developed by casting the IRL problem, for linear systems with quadratic cost functions, as a state estimation problem. Two observer-based techniques for IRL are developed, including a novel observer method that re-uses previous state estimates via history stacks. Theoretical guarantees for convergence and robustness are established under appropriate excitation conditions. Simulations demonstrate the performance of the developed observers and filters under noisy and noise-free measurements.
    Extreme heatwave sampling and prediction with analog Markov chain and comparisons with deep learning. (arXiv:2307.09060v1 [physics.ao-ph])
    We present a data-driven emulator, stochastic weather generator (SWG), suitable for estimating probabilities of prolonged heatwaves in France and Scandinavia. This emulator is based on the method of analogs of circulation to which we add temperature and soil moisture as predictor fields. We train the emulator on an intermediate complexity climate model run and show that it is capable of predicting conditional probabilities (forecasting) of heatwaves out of sample. Special attention is payed that this prediction is evaluated using proper score appropriate for rare events. To accelerate the computation of analogs dimensionality reduction techniques are applied and the performance is evaluated. The probabilistic prediction achieved with SWG is compared with the one achieved with Convolutional Neural Network (CNN). With the availability of hundreds of years of training data CNNs perform better at the task of probabilistic prediction. In addition, we show that the SWG emulator trained on 80 years of data is capable of estimating extreme return times of order of thousands of years for heatwaves longer than several days more precisely than the fit based on generalised extreme value distribution. Finally, the quality of its synthetic extreme teleconnection patterns obtained with stochastic weather generator is studied. We showcase two examples of such synthetic teleconnection patterns for heatwaves in France and Scandinavia that compare favorably to the very long climate model control run.
    Enhancing Pattern Classification in Support Vector Machines through Matrix Formulation. (arXiv:2307.09372v1 [cs.LG])
    Support Vector Machines (SVM) have gathered significant acclaim as classifiers due to their successful implementation of Statistical Learning Theory. However, in the context of multiclass and multilabel settings, the reliance on vector-based formulations in existing SVM-based models poses limitations regarding flexibility and ease of incorporating additional terms to handle specific challenges. To overcome these limitations, our research paper focuses on introducing a matrix formulation for SVM that effectively addresses these constraints. By employing the Accelerated Gradient Descent method in the dual, we notably enhance the efficiency of solving the Matrix-SVM problem. Experimental evaluations on multilabel and multiclass datasets demonstrate that Matrix SVM achieves superior time efficacy while delivering similar results to Binary Relevance SVM. Moreover, our matrix formulation unveils crucial insights and advantages that may not be readily apparent in traditional vector-based notations. We emphasize that numerous multilabel models can be viewed as extensions of SVM, with customised modifications to meet specific requirements. The matrix formulation presented in this paper establishes a solid foundation for developing more sophisticated models capable of effectively addressing the distinctive challenges encountered in multilabel learning.
    FakET: Simulating Cryo-Electron Tomograms with Neural Style Transfer. (arXiv:2304.02011v2 [cs.LG] UPDATED)
    Particle localization and -classification constitute two of the most fundamental problems in computational microscopy. In recent years, deep learning based approaches have been introduced for these tasks with great success. A key shortcoming of these supervised learning methods is their need for large training data sets, typically generated from particle models in conjunction with complex numerical forward models simulating the physics of transmission electron microscopes. Computer implementations of such forward models are computationally extremely demanding and limit the scope of their applicability. In this paper we propose a method for simulating the forward operator of an electron microscope based on additive noise and Neural Style Transfer techniques. We evaluate the method on localization and classification tasks using one of the established state-of-the-art architectures showing performance on par with the benchmark. In contrast to previous approaches, our method accelerates the data generation process by a factor of 750 while using 33 times less memory and scales well to typical transmission electron microscope detector sizes. It utilizes GPU acceleration and parallel processing. It can be used to adapt a synthetic training data set according to reference data from any transmission electron microscope. The source code is available at https://gitlab.com/deepet/faket.
    Multi-Player Zero-Sum Markov Games with Networked Separable Interactions. (arXiv:2307.09470v1 [cs.GT])
    We study a new class of Markov games (MGs), \textit{Multi-player Zero-sum Markov Games} with {\it Networked separable interactions} (MZNMGs), to model the local interaction structure in non-cooperative multi-agent sequential decision-making. We define an MZNMG as a model where {the payoffs of the auxiliary games associated with each state are zero-sum and} have some separable (i.e., polymatrix) structure across the neighbors over some interaction network. We first identify the necessary and sufficient conditions under which an MG can be presented as an MZNMG, and show that the set of Markov coarse correlated equilibrium (CCE) collapses to the set of Markov Nash equilibrium (NE) in these games, in that the {product of} per-state marginalization of the former for all players yields the latter. Furthermore, we show that finding approximate Markov \emph{stationary} CCE in infinite-horizon discounted MZNMGs is \texttt{PPAD}-hard, unless the underlying network has a ``star topology''. Then, we propose fictitious-play-type dynamics, the classical learning dynamics in normal-form games, for MZNMGs, and establish convergence guarantees to Markov stationary NE under a star-shaped network structure. Finally, in light of the hardness result, we focus on computing a Markov \emph{non-stationary} NE and provide finite-iteration guarantees for a series of value-iteration-based algorithms. We also provide numerical experiments to corroborate our theoretical results.
    Non-Stationary Policy Learning for Multi-Timescale Multi-Agent Reinforcement Learning. (arXiv:2307.08794v1 [cs.LG])
    In multi-timescale multi-agent reinforcement learning (MARL), agents interact across different timescales. In general, policies for time-dependent behaviors, such as those induced by multiple timescales, are non-stationary. Learning non-stationary policies is challenging and typically requires sophisticated or inefficient algorithms. Motivated by the prevalence of this control problem in real-world complex systems, we introduce a simple framework for learning non-stationary policies for multi-timescale MARL. Our approach uses available information about agent timescales to define a periodic time encoding. In detail, we theoretically demonstrate that the effects of non-stationarity introduced by multiple timescales can be learned by a periodic multi-agent policy. To learn such policies, we propose a policy gradient algorithm that parameterizes the actor and critic with phase-functioned neural networks, which provide an inductive bias for periodicity. The framework's ability to effectively learn multi-timescale policies is validated on a gridworld and building energy management environment.
    Execution-based Code Generation using Deep Reinforcement Learning. (arXiv:2301.13816v3 [cs.LG] UPDATED)
    The utilization of programming language (PL) models, pre-trained on large-scale code corpora, as a means of automating software engineering processes has demonstrated considerable potential in streamlining various code generation tasks such as code completion, code translation, and program synthesis. However, current approaches mainly rely on supervised fine-tuning objectives borrowed from text generation, neglecting unique sequence-level characteristics of code, including but not limited to compilability as well as syntactic and functional correctness. To address this limitation, we propose PPOCoder, a new framework for code generation that synergistically combines pre-trained PL models with Proximal Policy Optimization (PPO) which is a widely used deep reinforcement learning technique. By utilizing non-differentiable feedback from code execution and structure alignment, PPOCoder seamlessly integrates external code-specific knowledge into the model optimization process. It's important to note that PPOCoder is a task-agnostic and model-agnostic framework that can be used across different code generation tasks and PLs. Extensive experiments on three code generation tasks demonstrate the effectiveness of our proposed approach compared to SOTA methods, achieving significant improvements in compilation success rates and functional correctness across different PLs.
    Deep Learning for Mean Field Games with non-separable Hamiltonians. (arXiv:2301.02877v2 [cs.LG] UPDATED)
    This paper introduces a new method based on Deep Galerkin Methods (DGMs) for solving high-dimensional stochastic Mean Field Games (MFGs). We achieve this by using two neural networks to approximate the unknown solutions of the MFG system and forward-backward conditions. Our method is efficient, even with a small number of iterations, and is capable of handling up to 300 dimensions with a single layer, which makes it faster than other approaches. In contrast, methods based on Generative Adversarial Networks (GANs) cannot solve MFGs with non-separable Hamiltonians. We demonstrate the effectiveness of our approach by applying it to a traffic flow problem, which was previously solved using the Newton iteration method only in the deterministic case. We compare the results of our method to analytical solutions and previous approaches, showing its efficiency. We also prove the convergence of our neural network approximation with a single hidden layer using the universal approximation theorem.
    Towards Sustainable Deep Learning for Multi-Label Classification on NILM. (arXiv:2307.09244v1 [cs.LG])
    Non-intrusive load monitoring (NILM) is the process of obtaining appliance-level data from a single metering point, measuring total electricity consumption of a household or a business. Appliance-level data can be directly used for demand response applications and energy management systems as well as for awareness raising and motivation for improvements in energy efficiency and reduction in the carbon footprint. Recently, classical machine learning and deep learning (DL) techniques became very popular and proved as highly effective for NILM classification, but with the growing complexity these methods are faced with significant computational and energy demands during both their training and operation. In this paper, we introduce a novel DL model aimed at enhanced multi-label classification of NILM with improved computation and energy efficiency. We also propose a testing methodology for comparison of different models using data synthesized from the measurement datasets so as to better represent real-world scenarios. Compared to the state-of-the-art, the proposed model has its carbon footprint reduced by more than 23% while providing on average approximately 8 percentage points in performance improvement when testing on data derived from REFIT and UK-DALE datasets.
    Nonlinear Processing with Linear Optics. (arXiv:2307.08533v2 [physics.optics] UPDATED)
    Deep neural networks have achieved remarkable breakthroughs by leveraging multiple layers of data processing to extract hidden representations, albeit at the cost of large electronic computing power. To enhance energy efficiency and speed, the optical implementation of neural networks aims to harness the advantages of optical bandwidth and the energy efficiency of optical interconnections. In the absence of low-power optical nonlinearities, the challenge in the implementation of multilayer optical networks lies in realizing multiple optical layers without resorting to electronic components. In this study, we present a novel framework that uses multiple scattering that is capable of synthesizing programmable linear and nonlinear transformations concurrently at low optical power by leveraging the nonlinear relationship between the scattering potential, represented by data, and the scattered field. Theoretical and experimental investigations show that repeating the data by multiple scattering enables non-linear optical computing at low power continuous wave light.
    Basal-Bolus Advisor for Type 1 Diabetes (T1D) Patients Using Multi-Agent Reinforcement Learning (RL) Methodology. (arXiv:2307.08897v1 [cs.LG])
    This paper presents a novel multi-agent reinforcement learning (RL) approach for personalized glucose control in individuals with type 1 diabetes (T1D). The method employs a closed-loop system consisting of a blood glucose (BG) metabolic model and a multi-agent soft actor-critic RL model acting as the basal-bolus advisor. Performance evaluation is conducted in three scenarios, comparing the RL agents to conventional therapy. Evaluation metrics include glucose levels (minimum, maximum, and mean), time spent in different BG ranges, and average daily bolus and basal insulin dosages. Results demonstrate that the RL-based basal-bolus advisor significantly improves glucose control, reducing glycemic variability and increasing time spent within the target range (70-180 mg/dL). Hypoglycemia events are effectively prevented, and severe hyperglycemia events are reduced. The RL approach also leads to a statistically significant reduction in average daily basal insulin dosage compared to conventional therapy. These findings highlight the effectiveness of the multi-agent RL approach in achieving better glucose control and mitigating the risk of severe hyperglycemia in individuals with T1D.
    FlexiAST: Flexibility is What AST Needs. (arXiv:2307.09286v1 [cs.SD])
    The objective of this work is to give patch-size flexibility to Audio Spectrogram Transformers (AST). Recent advancements in ASTs have shown superior performance in various audio-based tasks. However, the performance of standard ASTs degrades drastically when evaluated using different patch sizes from that used during training. As a result, AST models are typically re-trained to accommodate changes in patch sizes. To overcome this limitation, this paper proposes a training procedure to provide flexibility to standard AST models without architectural changes, allowing them to work with various patch sizes at the inference stage - FlexiAST. This proposed training approach simply utilizes random patch size selection and resizing of patch and positional embedding weights. Our experiments show that FlexiAST gives similar performance to standard AST models while maintaining its evaluation ability at various patch sizes on different datasets for audio classification tasks.
    Oracle Efficient Online Multicalibration and Omniprediction. (arXiv:2307.08999v1 [cs.LG])
    A recent line of work has shown a surprising connection between multicalibration, a multi-group fairness notion, and omniprediction, a learning paradigm that provides simultaneous loss minimization guarantees for a large family of loss functions. Prior work studies omniprediction in the batch setting. We initiate the study of omniprediction in the online adversarial setting. Although there exist algorithms for obtaining notions of multicalibration in the online adversarial setting, unlike batch algorithms, they work only for small finite classes of benchmark functions $F$, because they require enumerating every function $f \in F$ at every round. In contrast, omniprediction is most interesting for learning theoretic hypothesis classes $F$, which are generally continuously large. We develop a new online multicalibration algorithm that is well defined for infinite benchmark classes $F$, and is oracle efficient (i.e. for any class $F$, the algorithm has the form of an efficient reduction to a no-regret learning algorithm for $F$). The result is the first efficient online omnipredictor -- an oracle efficient prediction algorithm that can be used to simultaneously obtain no regret guarantees to all Lipschitz convex loss functions. For the class $F$ of linear functions, we show how to make our algorithm efficient in the worst case. Also, we show upper and lower bounds on the extent to which our rates can be improved: our oracle efficient algorithm actually promises a stronger guarantee called swap-omniprediction, and we prove a lower bound showing that obtaining $O(\sqrt{T})$ bounds for swap-omniprediction is impossible in the online setting. On the other hand, we give a (non-oracle efficient) algorithm which can obtain the optimal $O(\sqrt{T})$ omniprediction bounds without going through multicalibration, giving an information theoretic separation between these two solution concepts.
    Cooperative Multi-Objective Reinforcement Learning for Traffic Signal Control and Carbon Emission Reduction. (arXiv:2306.09662v2 [cs.LG] UPDATED)
    Existing traffic signal control systems rely on oversimplified rule-based methods, and even RL-based methods are often suboptimal and unstable. To address this, we propose a cooperative multi-objective architecture called Multi-Objective Multi-Agent Deep Deterministic Policy Gradient (MOMA-DDPG), which estimates multiple reward terms for traffic signal control optimization using age-decaying weights. Our approach involves two types of agents: one focuses on optimizing local traffic at each intersection, while the other aims to optimize global traffic throughput. We evaluate our method using real-world traffic data collected from an Asian country's traffic cameras. Despite the inclusion of a global agent, our solution remains decentralized as this agent is no longer necessary during the inference stage. Our results demonstrate the effectiveness of MOMA-DDPG, outperforming state-of-the-art methods across all performance metrics. Additionally, our proposed system minimizes both waiting time and carbon emissions. Notably, this paper is the first to link carbon emissions and global agents in traffic signal control.
    Convergent regularization in inverse problems and linear plug-and-play denoisers. (arXiv:2307.09441v1 [math.NA])
    Plug-and-play (PnP) denoising is a popular iterative framework for solving imaging inverse problems using off-the-shelf image denoisers. Their empirical success has motivated a line of research that seeks to understand the convergence of PnP iterates under various assumptions on the denoiser. While a significant amount of research has gone into establishing the convergence of the PnP iteration for different regularity conditions on the denoisers, not much is known about the asymptotic properties of the converged solution as the noise level in the measurement tends to zero, i.e., whether PnP methods are provably convergent regularization schemes under reasonable assumptions on the denoiser. This paper serves two purposes: first, we provide an overview of the classical regularization theory in inverse problems and survey a few notable recent data-driven methods that are provably convergent regularization schemes. We then continue to discuss PnP algorithms and their established convergence guarantees. Subsequently, we consider PnP algorithms with linear denoisers and propose a novel spectral filtering technique to control the strength of regularization arising from the denoiser. Further, by relating the implicit regularization of the denoiser to an explicit regularization functional, we rigorously show that PnP with linear denoisers leads to a convergent regularization scheme. More specifically, we prove that in the limit as the noise vanishes, the PnP reconstruction converges to the minimizer of a regularization potential subject to the solution satisfying the noiseless operator equation. The theoretical analysis is corroborated by numerical experiments for the classical inverse problem of tomographic image reconstruction.
    FedFormer: Contextual Federation with Attention in Reinforcement Learning. (arXiv:2205.13697v3 [cs.LG] CROSS LISTED)
    A core issue in multi-agent federated reinforcement learning is defining how to aggregate insights from multiple agents. This is commonly done by taking the average of each participating agent's model weights into one common model (FedAvg). We instead propose FedFormer, a novel federation strategy that utilizes Transformer Attention to contextually aggregate embeddings from models originating from different learner agents. In so doing, we attentively weigh the contributions of other agents with respect to the current agent's environment and learned relationships, thus providing a more effective and efficient federation. We evaluate our methods on the Meta-World environment and find that our approach yields significant improvements over FedAvg and non-federated Soft Actor-Critic single-agent methods. Our results compared to Soft Actor-Critic show that FedFormer achieves higher episodic return while still abiding by the privacy constraints of federated learning. Finally, we also demonstrate improvements in effectiveness with increased agent pools across all methods in certain tasks. This is contrasted by FedAvg, which fails to make noticeable improvements when scaled.
    Sparse Gaussian Graphical Models with Discrete Optimization: Computational and Statistical Perspectives. (arXiv:2307.09366v1 [cs.LG])
    We consider the problem of learning a sparse graph underlying an undirected Gaussian graphical model, a key problem in statistical machine learning. Given $n$ samples from a multivariate Gaussian distribution with $p$ variables, the goal is to estimate the $p \times p$ inverse covariance matrix (aka precision matrix), assuming it is sparse (i.e., has a few nonzero entries). We propose GraphL0BnB, a new estimator based on an $\ell_0$-penalized version of the pseudolikelihood function, while most earlier approaches are based on the $\ell_1$-relaxation. Our estimator can be formulated as a convex mixed integer program (MIP) which can be difficult to compute at scale using off-the-shelf commercial solvers. To solve the MIP, we propose a custom nonlinear branch-and-bound (BnB) framework that solves node relaxations with tailored first-order methods. As a by-product of our BnB framework, we propose large-scale solvers for obtaining good primal solutions that are of independent interest. We derive novel statistical guarantees (estimation and variable selection) for our estimator and discuss how our approach improves upon existing estimators. Our numerical experiments on real/synthetic datasets suggest that our method can solve, to near-optimality, problem instances with $p = 10^4$ -- corresponding to a symmetric matrix of size $p \times p$ with $p^2/2$ binary variables. We demonstrate the usefulness of GraphL0BnB versus various state-of-the-art approaches on a range of datasets.
    Joint Microseismic Event Detection and Location with a Detection Transformer. (arXiv:2307.09207v1 [physics.geo-ph])
    Microseismic event detection and location are two primary components in microseismic monitoring, which offers us invaluable insights into the subsurface during reservoir stimulation and evolution. Conventional approaches for event detection and location often suffer from manual intervention and/or heavy computation, while current machine learning-assisted approaches typically address detection and location separately; such limitations hinder the potential for real-time microseismic monitoring. We propose an approach to unify event detection and source location into a single framework by adapting a Convolutional Neural Network backbone and an encoder-decoder Transformer with a set-based Hungarian loss, which is applied directly to recorded waveforms. The proposed network is trained on synthetic data simulating multiple microseismic events corresponding to random source locations in the area of suspected microseismic activities. A synthetic test on a 2D profile of the SEAM Time Lapse model illustrates the capability of the proposed method in detecting the events properly and locating them in the subsurface accurately; while, a field test using the Arkoma Basin data further proves its practicability, efficiency, and its potential in paving the way for real-time monitoring of microseismic events.
    An R package for parametric estimation of causal effects. (arXiv:2307.08686v2 [stat.ME] UPDATED)
    This article explains the usage of R package CausalModels, which is publicly available on the Comprehensive R Archive Network. While packages are available for sufficiently estimating causal effects, there lacks a package that provides a collection of structural models using the conventional statistical approach developed by Hernan and Robins (2020). CausalModels addresses this deficiency of software in R concerning causal inference by offering tools for methods that account for biases in observational data without requiring extensive statistical knowledge. These methods should not be ignored and may be more appropriate or efficient in solving particular problems. While implementations of these statistical models are distributed among a number of causal packages, CausalModels introduces a simple and accessible framework for a consistent modeling pipeline among a variety of statistical methods for estimating causal effects in a single R package. It consists of common methods including standardization, IP weighting, G-estimation, outcome regression, instrumental variables and propensity matching.
    On the Robustness of Split Learning against Adversarial Attacks. (arXiv:2307.07916v2 [cs.LG] UPDATED)
    Split learning enables collaborative deep learning model training while preserving data privacy and model security by avoiding direct sharing of raw data and model details (i.e., sever and clients only hold partial sub-networks and exchange intermediate computations). However, existing research has mainly focused on examining its reliability for privacy protection, with little investigation into model security. Specifically, by exploring full models, attackers can launch adversarial attacks, and split learning can mitigate this severe threat by only disclosing part of models to untrusted servers.This paper aims to evaluate the robustness of split learning against adversarial attacks, particularly in the most challenging setting where untrusted servers only have access to the intermediate layers of the model.Existing adversarial attacks mostly focus on the centralized setting instead of the collaborative setting, thus, to better evaluate the robustness of split learning, we develop a tailored attack called SPADV, which comprises two stages: 1) shadow model training that addresses the issue of lacking part of the model and 2) local adversarial attack that produces adversarial examples to evaluate.The first stage only requires a few unlabeled non-IID data, and, in the second stage, SPADV perturbs the intermediate output of natural samples to craft the adversarial ones. The overall cost of the proposed attack process is relatively low, yet the empirical attack effectiveness is significantly high, demonstrating the surprising vulnerability of split learning to adversarial attacks.
    Scalable Coupling of Deep Learning with Logical Reasoning. (arXiv:2305.07617v2 [cs.AI] UPDATED)
    In the ongoing quest for hybridizing discrete reasoning with neural nets, there is an increasing interest in neural architectures that can learn how to solve discrete reasoning or optimization problems from natural inputs. In this paper, we introduce a scalable neural architecture and loss function dedicated to learning the constraints and criteria of NP-hard reasoning problems expressed as discrete Graphical Models. Our loss function solves one of the main limitations of Besag's pseudo-loglikelihood, enabling learning of high energies. We empirically show it is able to efficiently learn how to solve NP-hard reasoning problems from natural inputs as the symbolic, visual or many-solutions Sudoku problems as well as the energy optimization formulation of the protein design problem, providing data efficiency, interpretability, and \textit{a posteriori} control over predictions.
    Siamese Networks for Weakly Supervised Human Activity Recognition. (arXiv:2307.08944v1 [cs.HC])
    Deep learning has been successfully applied to human activity recognition. However, training deep neural networks requires explicitly labeled data which is difficult to acquire. In this paper, we present a model with multiple siamese networks that are trained by using only the information about the similarity between pairs of data samples without knowing the explicit labels. The trained model maps the activity data samples into fixed size representation vectors such that the distance between the vectors in the representation space approximates the similarity of the data samples in the input space. Thus, the trained model can work as a metric for a wide range of different clustering algorithms. The training process minimizes a similarity loss function that forces the distance metric to be small for pairs of samples from the same kind of activity, and large for pairs of samples from different kinds of activities. We evaluate the model on three datasets to verify its effectiveness in segmentation and recognition of continuous human activity sequences.
    Batched Predictors Generalize within Distribution. (arXiv:2307.09379v1 [stat.ML])
    We study the generalization properties of batched predictors, i.e., models tasked with predicting the mean label of a small set (or batch) of examples. The batched prediction paradigm is particularly relevant for models deployed to determine the quality of a group of compounds in preparation for offline testing. By utilizing a suitable generalization of the Rademacher complexity, we prove that batched predictors come with exponentially stronger generalization guarantees as compared to the standard per-sample approach. Surprisingly, the proposed bound holds independently of overparametrization. Our theoretical insights are validated experimentally for various tasks, architectures, and applications.
    Contrastive Representation Disentanglement for Clustering. (arXiv:2306.05439v2 [cs.LG] UPDATED)
    Clustering continues to be a significant and challenging task. Recent studies have demonstrated impressive results by applying clustering to feature representations acquired through self-supervised learning, particularly on small datasets. However, when dealing with datasets containing a large number of clusters, such as ImageNet, current methods struggle to achieve satisfactory clustering performance. In this paper, we introduce a novel method called Contrastive representation Disentanglement for Clustering (CDC) that leverages contrastive learning to directly disentangle the feature representation for clustering. In CDC, we decompose the representation into two distinct components: one component encodes categorical information under an equipartition constraint, and the other component captures instance-specific factors. To train our model, we propose a contrastive loss that effectively utilizes both components of the representation. We conduct a theoretical analysis of the proposed loss and highlight how it assigns different weights to negative samples during the process of disentangling the feature representation. Further analysis of the gradients reveals that larger weights emphasize a stronger focus on hard negative samples. As a result, the proposed loss exhibits strong expressiveness, enabling efficient disentanglement of categorical information. Through experimental evaluation on various benchmark datasets, our method demonstrates either state-of-the-art or highly competitive clustering performance. Notably, on the complete ImageNet dataset, we achieve an accuracy of 53.4%, surpassing existing methods by a substantial margin of +10.2%.
    Mining of Single-Class by Active Learning for Semantic Segmentation. (arXiv:2307.09109v1 [cs.LG])
    Several Active Learning (AL) policies require retraining a target model several times in order to identify the most informative samples and rarely offer the option to focus on the acquisition of samples from underrepresented classes. Here the Mining of Single-Class by Active Learning (MiSiCAL) paradigm is introduced where an AL policy is constructed through deep reinforcement learning and exploits quantity-accuracy correlations to build datasets on which high-performance models can be trained with regards to specific classes. MiSiCAL is especially helpful in the case of very large batch sizes since it does not require repeated model training sessions as is common in other AL methods. This is thanks to its ability to exploit fixed representations of the candidate data points. We find that MiSiCAL is able to outperform a random policy on 150 out of 171 COCO10k classes, while the strongest baseline only outperforms random on 101 classes.
    ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast. (arXiv:2304.02689v3 [cs.CV] UPDATED)
    Medical data often exhibits long-tail distributions with heavy class imbalance, which naturally leads to difficulty in classifying the minority classes (i.e., boundary regions or rare objects). Recent work has significantly improved semi-supervised medical image segmentation in long-tailed scenarios by equipping them with unsupervised contrastive criteria. However, it remains unclear how well they will perform in the labeled portion of data where class distribution is also highly imbalanced. In this work, we present ACTION++, an improved contrastive learning framework with adaptive anatomical contrast for semi-supervised medical segmentation. Specifically, we propose an adaptive supervised contrastive loss, where we first compute the optimal locations of class centers uniformly distributed on the embedding space (i.e., off-line), and then perform online contrastive matching training by encouraging different class features to adaptively match these distinct and uniformly distributed class centers. Moreover, we argue that blindly adopting a constant temperature $\tau$ in the contrastive loss on long-tailed medical data is not optimal, and propose to use a dynamic $\tau$ via a simple cosine schedule to yield better separation between majority and minority classes. Empirically, we evaluate ACTION++ on ACDC and LA benchmarks and show that it achieves state-of-the-art across two semi-supervised settings. Theoretically, we analyze the performance of adaptive anatomical contrast and confirm its superiority in label efficiency.
    Bayesian Safe Policy Learning with Chance Constrained Optimization: Application to Military Security Assessment during the Vietnam War. (arXiv:2307.08840v1 [cs.LG])
    Algorithmic and data-driven decisions and recommendations are commonly used in high-stakes decision-making settings such as criminal justice, medicine, and public policy. We investigate whether it would have been possible to improve a security assessment algorithm employed during the Vietnam War, using outcomes measured immediately after its introduction in late 1969. This empirical application raises several methodological challenges that frequently arise in high-stakes algorithmic decision-making. First, before implementing a new algorithm, it is essential to characterize and control the risk of yielding worse outcomes than the existing algorithm. Second, the existing algorithm is deterministic, and learning a new algorithm requires transparent extrapolation. Third, the existing algorithm involves discrete decision tables that are common but difficult to optimize over. To address these challenges, we introduce the Average Conditional Risk (ACRisk), which first quantifies the risk that a new algorithmic policy leads to worse outcomes for subgroups of individual units and then averages this over the distribution of subgroups. We also propose a Bayesian policy learning framework that maximizes the posterior expected value while controlling the posterior expected ACRisk. This framework separates the estimation of heterogeneous treatment effects from policy optimization, enabling flexible estimation of effects and optimization over complex policy classes. We characterize the resulting chance-constrained optimization problem as a constrained linear programming problem. Our analysis shows that compared to the actual algorithm used during the Vietnam War, the learned algorithm assesses most regions as more secure and emphasizes economic and political factors over military factors.
    Learning to Select SAT Encodings for Pseudo-Boolean and Linear Integer Constraints. (arXiv:2307.09342v1 [cs.AI])
    Many constraint satisfaction and optimisation problems can be solved effectively by encoding them as instances of the Boolean Satisfiability problem (SAT). However, even the simplest types of constraints have many encodings in the literature with widely varying performance, and the problem of selecting suitable encodings for a given problem instance is not trivial. We explore the problem of selecting encodings for pseudo-Boolean and linear constraints using a supervised machine learning approach. We show that it is possible to select encodings effectively using a standard set of features for constraint problems; however we obtain better performance with a new set of features specifically designed for the pseudo-Boolean and linear constraints. In fact, we achieve good results when selecting encodings for unseen problem classes. Our results compare favourably to AutoFolio when using the same feature set. We discuss the relative importance of instance features to the task of selecting the best encodings, and compare several variations of the machine learning method.
    Biomaker CA: a Biome Maker project using Cellular Automata. (arXiv:2307.09320v1 [cs.AI])
    We introduce Biomaker CA: a Biome Maker project using Cellular Automata (CA). In Biomaker CA, morphogenesis is a first class citizen and small seeds need to grow into plant-like organisms to survive in a nutrient starved environment and eventually reproduce with variation so that a biome survives for long timelines. We simulate complex biomes by means of CA rules in 2D grids and parallelize all of its computation on GPUs through the Python JAX framework. We show how this project allows for several different kinds of environments and laws of 'physics', alongside different model architectures and mutation strategies. We further analyze some configurations to show how plant agents can grow, survive, reproduce, and evolve, forming stable and unstable biomes. We then demonstrate how one can meta-evolve models to survive in a harsh environment either through end-to-end meta-evolution or by a more surgical and efficient approach, called Petri dish meta-evolution. Finally, we show how to perform interactive evolution, where the user decides how to evolve a plant model interactively and then deploys it in a larger environment. We open source Biomaker CA at: https://tinyurl.com/2x8yu34s .
    UniTabE: Pretraining a Unified Tabular Encoder for Heterogeneous Tabular Data. (arXiv:2307.09249v1 [cs.LG])
    Recent advancements in Natural Language Processing (NLP) have witnessed the groundbreaking impact of pretrained models, yielding impressive outcomes across various tasks. This study seeks to extend the power of pretraining methodologies to tabular data, a domain traditionally overlooked, yet inherently challenging due to the plethora of table schemas intrinsic to different tasks. The primary research questions underpinning this work revolve around the adaptation to heterogeneous table structures, the establishment of a universal pretraining protocol for tabular data, the generalizability and transferability of learned knowledge across tasks, the adaptation to diverse downstream applications, and the incorporation of incremental columns over time. In response to these challenges, we introduce UniTabE, a pioneering method designed to process tables in a uniform manner, devoid of constraints imposed by specific table structures. UniTabE's core concept relies on representing each basic table element with a module, termed TabUnit. This is subsequently followed by a Transformer encoder to refine the representation. Moreover, our model is designed to facilitate pretraining and finetuning through the utilization of free-form prompts. In order to implement the pretraining phase, we curated an expansive tabular dataset comprising approximately 13 billion samples, meticulously gathered from the Kaggle platform. Rigorous experimental testing and analyses were performed under a myriad of scenarios to validate the effectiveness of our methodology. The experimental results demonstrate UniTabE's superior performance against several baseline models across a multitude of benchmark datasets. This, therefore, underscores UniTabE's potential to significantly enhance the semantic representation of tabular data, thereby marking a significant stride in the field of tabular data analysis.
    Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labels. (arXiv:2307.08809v1 [cs.LG])
    Many existing FL methods assume clients with fully-labeled data, while in realistic settings, clients have limited labels due to the expensive and laborious process of labeling. Limited labeled local data of the clients often leads to their local model having poor generalization abilities to their larger unlabeled local data, such as having class-distribution mismatch with the unlabeled data. As a result, clients may instead look to benefit from the global model trained across clients to leverage their unlabeled data, but this also becomes difficult due to data heterogeneity across clients. In our work, we propose FedLabel where clients selectively choose the local or global model to pseudo-label their unlabeled data depending on which is more of an expert of the data. We further utilize both the local and global models' knowledge via global-local consistency regularization which minimizes the divergence between the two models' outputs when they have identical pseudo-labels for the unlabeled data. Unlike other semi-supervised FL baselines, our method does not require additional experts other than the local or global model, nor require additional parameters to be communicated. We also do not assume any server-labeled data or fully labeled clients. For both cross-device and cross-silo settings, we show that FedLabel outperforms other semi-supervised FL baselines by $8$-$24\%$, and even outperforms standard fully supervised FL baselines ($100\%$ labeled data) with only $5$-$20\%$ of labeled data.
    Online Learning with Costly Features in Non-stationary Environments. (arXiv:2307.09388v1 [cs.LG])
    Maximizing long-term rewards is the primary goal in sequential decision-making problems. The majority of existing methods assume that side information is freely available, enabling the learning agent to observe all features' states before making a decision. In real-world problems, however, collecting beneficial information is often costly. That implies that, besides individual arms' reward, learning the observations of the features' states is essential to improve the decision-making strategy. The problem is aggravated in a non-stationary environment where reward and cost distributions undergo abrupt changes over time. To address the aforementioned dual learning problem, we extend the contextual bandit setting and allow the agent to observe subsets of features' states. The objective is to maximize the long-term average gain, which is the difference between the accumulated rewards and the paid costs on average. Therefore, the agent faces a trade-off between minimizing the cost of information acquisition and possibly improving the decision-making process using the obtained information. To this end, we develop an algorithm that guarantees a sublinear regret in time. Numerical results demonstrate the superiority of our proposed policy in a real-world scenario.
    Automated Ableism: An Exploration of Explicit Disability Biases in Sentiment and Toxicity Analysis Models. (arXiv:2307.09209v1 [cs.CL])
    We analyze sentiment analysis and toxicity detection models to detect the presence of explicit bias against people with disability (PWD). We employ the bias identification framework of Perturbation Sensitivity Analysis to examine conversations related to PWD on social media platforms, specifically Twitter and Reddit, in order to gain insight into how disability bias is disseminated in real-world social settings. We then create the \textit{Bias Identification Test in Sentiment} (BITS) corpus to quantify explicit disability bias in any sentiment analysis and toxicity detection models. Our study utilizes BITS to uncover significant biases in four open AIaaS (AI as a Service) sentiment analysis tools, namely TextBlob, VADER, Google Cloud Natural Language API, DistilBERT and two toxicity detection models, namely two versions of Toxic-BERT. Our findings indicate that all of these models exhibit statistically significant explicit bias against PWD.
    Adaptive Topological Feature via Persistent Homology: Filtration Learning for Point Clouds. (arXiv:2307.09259v1 [cs.LG])
    Machine learning for point clouds has been attracting much attention, with many applications in various fields, such as shape recognition and material science. To enhance the accuracy of such machine learning methods, it is known to be effective to incorporate global topological features, which are typically extracted by persistent homology. In the calculation of persistent homology for a point cloud, we need to choose a filtration for the point clouds, an increasing sequence of spaces. Because the performance of machine learning methods combined with persistent homology is highly affected by the choice of a filtration, we need to tune it depending on data and tasks. In this paper, we propose a framework that learns a filtration adaptively with the use of neural networks. In order to make the resulting persistent homology isometry-invariant, we develop a neural network architecture with such invariance. Additionally, we theoretically show a finite-dimensional approximation result that justifies our architecture. Experimental results demonstrated the efficacy of our framework in several classification tasks.
    Mitigating Label Bias via Decoupled Confident Learning. (arXiv:2307.08945v1 [cs.LG])
    Growing concerns regarding algorithmic fairness have led to a surge in methodologies to mitigate algorithmic bias. However, such methodologies largely assume that observed labels in training data are correct. This is problematic because bias in labels is pervasive across important domains, including healthcare, hiring, and content moderation. In particular, human-generated labels are prone to encoding societal biases. While the presence of labeling bias has been discussed conceptually, there is a lack of methodologies to address this problem. We propose a pruning method -- Decoupled Confident Learning (DeCoLe) -- specifically designed to mitigate label bias. After illustrating its performance on a synthetic dataset, we apply DeCoLe in the context of hate speech detection, where label bias has been recognized as an important challenge, and show that it successfully identifies biased labels and outperforms competing approaches.
    Privacy-preserving patient clustering for personalized federated learning. (arXiv:2307.08847v1 [cs.LG])
    Federated Learning (FL) is a machine learning framework that enables multiple organizations to train a model without sharing their data with a central server. However, it experiences significant performance degradation if the data is non-identically independently distributed (non-IID). This is a problem in medical settings, where variations in the patient population contribute significantly to distribution differences across hospitals. Personalized FL addresses this issue by accounting for site-specific distribution differences. Clustered FL, a Personalized FL variant, was used to address this problem by clustering patients into groups across hospitals and training separate models on each group. However, privacy concerns remained as a challenge as the clustering process requires exchange of patient-level information. This was previously solved by forming clusters using aggregated data, which led to inaccurate groups and performance degradation. In this study, we propose Privacy-preserving Community-Based Federated machine Learning (PCBFL), a novel Clustered FL framework that can cluster patients using patient-level data while protecting privacy. PCBFL uses Secure Multiparty Computation, a cryptographic technique, to securely calculate patient-level similarity scores across hospitals. We then evaluate PCBFL by training a federated mortality prediction model using 20 sites from the eICU dataset. We compare the performance gain from PCBFL against traditional and existing Clustered FL frameworks. Our results show that PCBFL successfully forms clinically meaningful cohorts of low, medium, and high-risk patients. PCBFL outperforms traditional and existing Clustered FL frameworks with an average AUC improvement of 4.3% and AUPRC improvement of 7.8%.
    How Many Neurons Does it Take to Approximate the Maximum?. (arXiv:2307.09212v1 [cs.LG])
    We study the size of a neural network needed to approximate the maximum function over $d$ inputs, in the most basic setting of approximating with respect to the $L_2$ norm, for continuous distributions, for a network that uses ReLU activations. We provide new lower and upper bounds on the width required for approximation across various depths. Our results establish new depth separations between depth 2 and 3, and depth 3 and 5 networks, as well as providing a depth $\mathcal{O}(\log(\log(d)))$ and width $\mathcal{O}(d)$ construction which approximates the maximum function, significantly improving upon the depth requirements of the best previously known bounds for networks with linearly-bounded width. Our depth separation results are facilitated by a new lower bound for depth 2 networks approximating the maximum function over the uniform distribution, assuming an exponential upper bound on the size of the weights. Furthermore, we are able to use this depth 2 lower bound to provide tight bounds on the number of neurons needed to approximate the maximum by a depth 3 network. Our lower bounds are of potentially broad interest as they apply to the widely studied and used \emph{max} function, in contrast to many previous results that base their bounds on specially constructed or pathological functions and distributions.
    Unified Off-Policy Learning to Rank: a Reinforcement Learning Perspective. (arXiv:2306.07528v2 [cs.LG] UPDATED)
    Off-policy Learning to Rank (LTR) aims to optimize a ranker from data collected by a deployed logging policy. However, existing off-policy learning to rank methods often make strong assumptions about how users generate the click data, i.e., the click model, and hence need to tailor their methods specifically under different click models. In this paper, we unified the ranking process under general stochastic click models as a Markov Decision Process (MDP), and the optimal ranking could be learned with offline reinforcement learning (RL) directly. Building upon this, we leverage offline RL techniques for off-policy LTR and propose the Click Model-Agnostic Unified Off-policy Learning to Rank (CUOLR) method, which could be easily applied to a wide range of click models. Through a dedicated formulation of the MDP, we show that offline RL algorithms can adapt to various click models without complex debiasing techniques and prior knowledge of the model. Results on various large-scale datasets demonstrate that CUOLR consistently outperforms the state-of-the-art off-policy learning to rank algorithms while maintaining consistency and robustness under different click models.
    Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference. (arXiv:2307.09357v1 [cs.ET])
    Analog In-Memory Computing (AIMC) is a promising approach to reduce the latency and energy consumption of Deep Neural Network (DNN) inference and training. However, the noisy and non-linear device characteristics, and the non-ideal peripheral circuitry in AIMC chips, require adapting DNNs to be deployed on such hardware to achieve equivalent accuracy to digital computing. In this tutorial, we provide a deep dive into how such adaptations can be achieved and evaluated using the recently released IBM Analog Hardware Acceleration Kit (AIHWKit), freely available at https://github.com/IBM/aihwkit. The AIHWKit is a Python library that simulates inference and training of DNNs using AIMC. We present an in-depth description of the AIHWKit design, functionality, and best practices to properly perform inference and training. We also present an overview of the Analog AI Cloud Composer, that provides the benefits of using the AIHWKit simulation platform in a fully managed cloud setting. Finally, we show examples on how users can expand and customize AIHWKit for their own needs. This tutorial is accompanied by comprehensive Jupyter Notebook code examples that can be run using AIHWKit, which can be downloaded from https://github.com/IBM/aihwkit/tree/master/notebooks/tutorial.
    Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge. (arXiv:2307.08813v1 [cs.CL])
    Understanding protein interactions and pathway knowledge is crucial for unraveling the complexities of living systems and investigating the underlying mechanisms of biological functions and complex diseases. While existing databases provide curated biological data from literature and other sources, they are often incomplete and their maintenance is labor-intensive, necessitating alternative approaches. In this study, we propose to harness the capabilities of large language models to address these issues by automatically extracting such knowledge from the relevant scientific literature. Toward this goal, in this work, we investigate the effectiveness of different large language models in tasks that involve recognizing protein interactions, pathways, and gene regulatory relations. We thoroughly evaluate the performance of various models, highlight the significant findings, and discuss both the future opportunities and the remaining challenges associated with this approach. The code and data are available at: https://github.com/boxorange/BioIE-LLM
    The Role of Transparency in Repeated First-Price Auctions with Unknown Valuations. (arXiv:2307.09478v1 [cs.GT])
    We study the problem of regret minimization for a single bidder in a sequence of first-price auctions where the bidder knows the item's value only if the auction is won. Our main contribution is a complete characterization, up to logarithmic factors, of the minimax regret in terms of the auction's transparency, which regulates the amount of information on competing bids disclosed by the auctioneer at the end of each auction. Our results hold under different assumptions (stochastic, adversarial, and their smoothed variants) on the environment generating the bidder's valuations and competing bids. These minimax rates reveal how the interplay between transparency and the nature of the environment affects how fast one can learn to bid optimally in first-price auctions.
    Operator Guidance Informed by AI-Augmented Simulations. (arXiv:2307.08810v1 [cs.AI])
    This paper will present a multi-fidelity, data-adaptive approach with a Long Short-Term Memory (LSTM) neural network to estimate ship response statistics in bimodal, bidirectional seas. The study will employ a fast low-fidelity, volume-based tool SimpleCode and a higher-fidelity tool known as the Large Amplitude Motion Program (LAMP). SimpleCode and LAMP data were generated by common bi-modal, bi-directional sea conditions in the North Atlantic as training data. After training an LSTM network with LAMP ship motion response data, a sample route was traversed and randomly sampled historical weather was input into SimpleCode and the LSTM network, and compared against the higher fidelity results.
    Anomaly Detection with Selective Dictionary Learning. (arXiv:2307.08807v1 [cs.LG])
    In this paper we present new methods of anomaly detection based on Dictionary Learning (DL) and Kernel Dictionary Learning (KDL). The main contribution consists in the adaption of known DL and KDL algorithms in the form of unsupervised methods, used for outlier detection. We propose a reduced kernel version (RKDL), which is useful for problems with large data sets, due to the large kernel matrix. We also improve the DL and RKDL methods by the use of a random selection of signals, which aims to eliminate the outliers from the training procedure. All our algorithms are introduced in an anomaly detection toolbox and are compared to standard benchmark results.
    Accuracy versus time frontiers of semi-supervised and self-supervised learning on medical images. (arXiv:2307.08919v1 [cs.CV])
    For many applications of classifiers to medical images, a trustworthy label for each image can be difficult or expensive to obtain. In contrast, images without labels are more readily available. Two major research directions both promise that additional unlabeled data can improve classifier performance: self-supervised learning pretrains useful representations on unlabeled data only, then fine-tunes a classifier on these representations via the labeled set; semi-supervised learning directly trains a classifier on labeled and unlabeled data simultaneously. Recent methods from both directions have claimed significant gains on non-medical tasks, but do not systematically assess medical images and mostly compare only to methods in the same direction. This study contributes a carefully-designed benchmark to help answer a practitioner's key question: given a small labeled dataset and a limited budget of hours to spend on training, what gains from additional unlabeled images are possible and which methods best achieve them? Unlike previous benchmarks, ours uses realistic-sized validation sets to select hyperparameters, assesses runtime-performance tradeoffs, and bridges two research fields. By comparing 6 semi-supervised methods and 5 self-supervised methods to strong labeled-only baselines on 3 medical datasets with 30-1000 labels per class, we offer insights to resource-constrained, results-focused practitioners: MixMatch, SimCLR, and BYOL represent strong choices that were not surpassed by more recent methods. After much effort selecting hyperparameters on one dataset, we publish settings that enable strong methods to perform well on new medical tasks within a few hours, with further search over dozens of hours delivering modest additional gains.
    Evaluating unsupervised disentangled representation learning for genomic discovery and disease risk prediction. (arXiv:2307.08893v1 [cs.LG])
    High-dimensional clinical data have become invaluable resources for genetic studies, due to their accessibility in biobank-scale datasets and the development of high performance modeling techniques especially using deep learning. Recent work has shown that low dimensional embeddings of these clinical data learned by variational autoencoders (VAE) can be used for genome-wide association studies and polygenic risk prediction. In this work, we consider multiple unsupervised learning methods for learning disentangled representations, namely autoencoders, VAE, beta-VAE, and FactorVAE, in the context of genetic association studies. Using spirograms from UK Biobank as a running example, we observed improvements in the number of genome-wide significant loci, heritability, and performance of polygenic risk scores for asthma and chronic obstructive pulmonary disease by using FactorVAE or beta-VAE, compared to standard VAE or non-variational autoencoders. FactorVAEs performed effectively across multiple values of the regularization hyperparameter, while beta-VAEs were much more sensitive to the hyperparameter values.
    Limitations of Information-Theoretic Generalization Bounds for Gradient Descent Methods in Stochastic Convex Optimization. (arXiv:2212.13556v3 [cs.LG] UPDATED)
    To date, no "information-theoretic" frameworks for reasoning about generalization error have been shown to establish minimax rates for gradient descent in the setting of stochastic convex optimization. In this work, we consider the prospect of establishing such rates via several existing information-theoretic frameworks: input-output mutual information bounds, conditional mutual information bounds and variants, PAC-Bayes bounds, and recent conditional variants thereof. We prove that none of these bounds are able to establish minimax rates. We then consider a common tactic employed in studying gradient methods, whereby the final iterate is corrupted by Gaussian noise, producing a noisy "surrogate" algorithm. We prove that minimax rates cannot be established via the analysis of such surrogates. Our results suggest that new ideas are required to analyze gradient descent using information-theoretic techniques.
    Context-Conditional Navigation with a Learning-Based Terrain- and Robot-Aware Dynamics Model. (arXiv:2307.09206v1 [cs.RO])
    In autonomous navigation settings, several quantities can be subject to variations. Terrain properties such as friction coefficients may vary over time depending on the location of the robot. Also, the dynamics of the robot may change due to, e.g., different payloads, changing the system's mass, or wear and tear, changing actuator gains or joint friction. An autonomous agent should thus be able to adapt to such variations. In this paper, we develop a novel probabilistic, terrain- and robot-aware forward dynamics model, termed TRADYN, which is able to adapt to the above-mentioned variations. It builds on recent advances in meta-learning forward dynamics models based on Neural Processes. We evaluate our method in a simulated 2D navigation setting with a unicycle-like robot and different terrain layouts with spatially varying friction coefficients. In our experiments, the proposed model exhibits lower prediction error for the task of long-horizon trajectory prediction, compared to non-adaptive ablation models. We also evaluate our model on the downstream task of navigation planning, which demonstrates improved performance in planning control-efficient paths by taking robot and terrain properties into account.
    Geometric Ultrasound Localization Microscopy. (arXiv:2306.15548v3 [cs.CV] UPDATED)
    Contrast-Enhanced Ultra-Sound (CEUS) has become a viable method for non-invasive, dynamic visualization in medical diagnostics, yet Ultrasound Localization Microscopy (ULM) has enabled a revolutionary breakthrough by offering ten times higher resolution. To date, Delay-And-Sum (DAS) beamformers are used to render ULM frames, ultimately determining the image resolution capability. To take full advantage of ULM, this study questions whether beamforming is the most effective processing step for ULM, suggesting an alternative approach that relies solely on Time-Difference-of-Arrival (TDoA) information. To this end, a novel geometric framework for micro bubble localization via ellipse intersections is proposed to overcome existing beamforming limitations. We present a benchmark comparison based on a public dataset for which our geometric ULM outperforms existing baseline methods in terms of accuracy and robustness while only utilizing a portion of the available transducer data.
    Internally Rewarded Reinforcement Learning. (arXiv:2302.00270v2 [cs.LG] UPDATED)
    We study a class of reinforcement learning problems where the reward signals for policy learning are generated by a discriminator that is dependent on and jointly optimized with the policy. This interdependence between the policy and the discriminator leads to an unstable learning process because reward signals from an immature discriminator are noisy and impede policy learning, and conversely, an under-optimized policy impedes discriminator learning. We call this learning setting \textit{Internally Rewarded Reinforcement Learning} (IRRL) as the reward is not provided directly by the environment but \textit{internally} by the discriminator. In this paper, we formally formulate IRRL and present a class of problems that belong to IRRL. We theoretically derive and empirically analyze the effect of the reward function in IRRL and based on these analyses propose the clipped linear reward function. Experimental results show that the proposed reward function can consistently stabilize the training process by reducing the impact of reward noise, which leads to faster convergence and higher performance compared with baselines in diverse tasks.
    Best-of-three-worlds Analysis for Linear Bandits with Follow-the-regularized-leader Algorithm. (arXiv:2303.06825v2 [cs.LG] UPDATED)
    The linear bandit problem has been studied for many years in both stochastic and adversarial settings. Designing an algorithm that can optimize the environment without knowing the loss type attracts lots of interest. \citet{LeeLWZ021} propose an algorithm that actively detects the loss type and then switches between different algorithms specially designed for specific settings. However, such an approach requires meticulous designs to perform well in all environments. Follow-the-regularized-leader (FTRL) is another type of popular algorithm that can adapt to different environments. This algorithm is of simple design and the regret bounds are shown to be optimal in traditional multi-armed bandit problems compared with the detect-switch type. Designing an FTRL-type algorithm for linear bandits is an important question that has been open for a long time. In this paper, we prove that the FTRL algorithm with a negative entropy regularizer can achieve the best-of-three-world results for the linear bandit problem. Our regret bounds achieve the same or nearly the same order as the previous detect-switch type algorithm but with a much simpler algorithmic design.
    A Novel Application of Conditional Normalizing Flows: Stellar Age Inference with Gyrochronology. (arXiv:2307.08753v1 [astro-ph.SR])
    Stellar ages are critical building blocks of evolutionary models, but challenging to measure for low mass main sequence stars. An unexplored solution in this regime is the application of probabilistic machine learning methods to gyrochronology, a stellar dating technique that is uniquely well suited for these stars. While accurate analytical gyrochronological models have proven challenging to develop, here we apply conditional normalizing flows to photometric data from open star clusters, and demonstrate that a data-driven approach can constrain gyrochronological ages with a precision comparable to other standard techniques. We evaluate the flow results in the context of a Bayesian framework, and show that our inferred ages recover literature values well. This work demonstrates the potential of a probabilistic data-driven solution to widen the applicability of gyrochronological stellar dating.
    K-Tensors: Clustering Positive Semi-Definite Matrices. (arXiv:2306.06534v3 [cs.LG] UPDATED)
    This paper introduces a novel self-consistency clustering algorithm ($K$-Tensors) designed for {partitioning a distribution of} positive-semidefinite matrices based on their eigenstructures. As positive semi-definite matrices can be represented as ellipsoids in $\mathbb R^p$, $p \ge 2$, it is critical to maintain their structural information to perform effective clustering. However, traditional clustering algorithms {applied to matrices} often {involve vectorization of} the matrices, resulting in a loss of essential structural information. To address this issue, we propose a distance metric {for clustering} that is specifically based on the structural information of positive semi-definite matrices. This distance metric enables the clustering algorithm to consider the differences between positive semi-definite matrices and their projections onto {a} common space spanned by \thadJulyTen{orthonormal vectors defined from a set of} positive semi-definite matrices. This innovative approach to clustering positive semi-definite matrices has broad applications in several domains including financial and biomedical research, such as analyzing functional connectivity data. By maintaining the structural information of positive semi-definite matrices, our proposed algorithm promises to cluster the positive semi-definite matrices in a more meaningful way, thereby facilitating deeper insights into the underlying data in various applications.
    Graph Representation of the Magnetic Field Topology in High-Fidelity Plasma Simulations for Machine Learning Applications. (arXiv:2307.09469v1 [physics.plasm-ph])
    Topological analysis of the magnetic field in simulated plasmas allows the study of various physical phenomena in a wide range of settings. One such application is magnetic reconnection, a phenomenon related to the dynamics of the magnetic field topology, which is difficult to detect and characterize in three dimensions. We propose a scalable pipeline for topological data analysis and spatiotemporal graph representation of three-dimensional magnetic vector fields. We demonstrate our methods on simulations of the Earth's magnetosphere produced by Vlasiator, a supercomputer-scale Vlasov theory-based simulation for near-Earth space. The purpose of this work is to challenge the machine learning community to explore graph-based machine learning approaches to address a largely open scientific problem with wide-ranging potential impact.
    Performance Gaps of Artificial Intelligence Models Screening Mammography -- Towards Fair and Interpretable Models. (arXiv:2305.04422v2 [eess.IV] UPDATED)
    Even though deep learning models for abnormality classification can perform well in screening mammography, the demographic and imaging characteristics associated with increased risk of failure for abnormality classification in screening mammograms remain unclear. This retrospective study used data from the Emory BrEast Imaging Dataset (EMBED) including mammograms from 115,931 patients imaged at Emory University Healthcare between 2013 to 2020. Clinical and imaging data includes Breast Imaging Reporting and Data System (BI-RADS) assessment, region of interest coordinates for abnormalities, imaging features, pathologic outcomes, and patient demographics. Deep learning models including InceptionV3, VGG16, ResNet50V2, and ResNet152V2 were developed to distinguish between patches of abnormal tissue and randomly selected patches of normal tissue from the screening mammograms. The distributions of the training, validation and test sets are 29,144 (55.6%) patches of 10,678 (54.2%) patients, 9,910 (18.9%) patches of 3,609 (18.3%) patients, and 13,390 (25.5%) patches of 5,404 (27.5%) patients. We assessed model performance overall and within subgroups defined by age, race, pathologic outcome, and imaging characteristics to evaluate reasons for misclassifications. On the test set, a ResNet152V2 model trained to classify normal versus abnormal tissue patches achieved an accuracy of 92.6% (95%CI=92.0-93.2%), and area under the receiver operative characteristics curve 0.975 (95%CI=0.972-0.978). Imaging characteristics associated with higher misclassifications of images include higher tissue densities (risk ratio [RR]=1.649; p=.010, BI-RADS density C and RR=2.026; p=.003, BI-RADS density D), and presence of architectural distortion (RR=1.026; p<.001). Small but statistically significant differences in performance were observed by age, race, pathologic outcome, and other imaging features (p<.001).
    Evaluate Fine-tuning Strategies for Fetal Head Ultrasound Image Segmentation with U-Net. (arXiv:2307.09067v1 [eess.IV])
    Fetal head segmentation is a crucial step in measuring the fetal head circumference (HC) during gestation, an important biometric in obstetrics for monitoring fetal growth. However, manual biometry generation is time-consuming and results in inconsistent accuracy. To address this issue, convolutional neural network (CNN) models have been utilized to improve the efficiency of medical biometry. But training a CNN network from scratch is a challenging task, we proposed a Transfer Learning (TL) method. Our approach involves fine-tuning (FT) a U-Net network with a lightweight MobileNet as the encoder to perform segmentation on a set of fetal head ultrasound (US) images with limited effort. This method addresses the challenges associated with training a CNN network from scratch. It suggests that our proposed FT strategy yields segmentation performance that is comparable when trained with a reduced number of parameters by 85.8%. And our proposed FT strategy outperforms other strategies with smaller trainable parameter sizes below 4.4 million. Thus, we contend that it can serve as a dependable FT approach for reducing the size of models in medical image analysis. Our key findings highlight the importance of the balance between model performance and size in developing Artificial Intelligence (AI) applications by TL methods. Code is available at https://github.com/13204942/FT_Methods_for_Fetal_Head_Segmentation.
    Curriculum Learning for Graph Neural Networks: A Multiview Competence-based Approach. (arXiv:2307.08859v1 [cs.LG])
    A curriculum is a planned sequence of learning materials and an effective one can make learning efficient and effective for both humans and machines. Recent studies developed effective data-driven curriculum learning approaches for training graph neural networks in language applications. However, existing curriculum learning approaches often employ a single criterion of difficulty in their training paradigms. In this paper, we propose a new perspective on curriculum learning by introducing a novel approach that builds on graph complexity formalisms (as difficulty criteria) and model competence during training. The model consists of a scheduling scheme which derives effective curricula by accounting for different views of sample difficulty and model competence during training. The proposed solution advances existing research in curriculum learning for graph neural networks with the ability to incorporate a fine-grained spectrum of graph difficulty criteria in their training paradigms. Experimental results on real-world link prediction and node classification tasks illustrate the effectiveness of the proposed approach.
    Characterization of partial wetting by CMAS droplets using multiphase many-body dissipative particle dynamics and data-driven discovery based on PINNs. (arXiv:2307.09142v1 [physics.flu-dyn])
    The molten sand, a mixture of calcia, magnesia, alumina, and silicate, known as CMAS, is characterized by its high viscosity, density, and surface tension. The unique properties of CMAS make it a challenging material to deal with in high-temperature applications, requiring innovative solutions and materials to prevent its buildup and damage to critical equipment. Here, we use multiphase many-body dissipative particle dynamics (mDPD) simulations to study the wetting dynamics of highly viscous molten CMAS droplets. The simulations are performed in three dimensions, with varying initial droplet sizes and equilibrium contact angles. We propose a coarse parametric ordinary differential equation (ODE) that captures the spreading radius behavior of the CMAS droplets. The ODE parameters are then identified based on the Physics-Informed Neural Network (PINN) framework. Subsequently, the closed form dependency of parameter values found by PINN on the initial radii and contact angles are given using symbolic regression. Finally, we employ Bayesian PINNs (B-PINNs) to assess and quantify the uncertainty associated with the discovered parameters. In brief, this study provides insight into spreading dynamics of CMAS droplets by fusing simple parametric ODE modeling and state-of-the-art machine learning techniques.
    Quality Assessment of Photoplethysmography Signals For Cardiovascular Biomarkers Monitoring Using Wearable Devices. (arXiv:2307.08766v1 [cs.LG])
    Photoplethysmography (PPG) is a non-invasive technology that measures changes in blood volume in the microvascular bed of tissue. It is commonly used in medical devices such as pulse oximeters and wrist worn heart rate monitors to monitor cardiovascular hemodynamics. PPG allows for the assessment of parameters (e.g., heart rate, pulse waveform, and peripheral perfusion) that can indicate conditions such as vasoconstriction or vasodilation, and provides information about microvascular blood flow, making it a valuable tool for monitoring cardiovascular health. However, PPG is subject to a number of sources of variations that can impact its accuracy and reliability, especially when using a wearable device for continuous monitoring, such as motion artifacts, skin pigmentation, and vasomotion. In this study, we extracted 27 statistical features from the PPG signal for training machine-learning models based on gradient boosting (XGBoost and CatBoost) and Random Forest (RF) algorithms to assess quality of PPG signals that were labeled as good or poor quality. We used the PPG time series from a publicly available dataset and evaluated the algorithm s performance using Sensitivity (Se), Positive Predicted Value (PPV), and F1-score (F1) metrics. Our model achieved Se, PPV, and F1-score of 94.4, 95.6, and 95.0 for XGBoost, 94.7, 95.9, and 95.3 for CatBoost, and 93.7, 91.3 and 92.5 for RF, respectively. Our findings are comparable to state-of-the-art reported in the literature but using a much simpler model, indicating that ML models are promising for developing remote, non-invasive, and continuous measurement devices.
    An Admissible Shift-Consistent Method for Recommender Systems. (arXiv:2307.08857v1 [cs.IR])
    In this paper, we propose a new constraint, called shift-consistency, for solving matrix/tensor completion problems in the context of recommender systems. Our method provably guarantees several key mathematical properties: (1) satisfies a recently established admissibility criterion for recommender systems; (2) satisfies a definition of fairness that eliminates a specific class of potential opportunities for users to maliciously influence system recommendations; and (3) offers robustness by exploiting provable uniqueness of missing-value imputation. We provide a rigorous mathematical description of the method, including its generalization from matrix to tensor form to permit representation and exploitation of complex structural relationships among sets of user and product attributes. We argue that our analysis suggests a structured means for defining latent-space projections that can permit provable performance properties to be established for machine learning methods.
    Overthinking the Truth: Understanding how Language Models Process False Demonstrations. (arXiv:2307.09476v1 [cs.LG])
    Modern language models can imitate complex patterns through few-shot learning, enabling them to complete challenging tasks without fine-tuning. However, imitation can also lead models to reproduce inaccuracies or harmful content if present in the context. We study harmful imitation through the lens of a model's internal representations, and identify two related phenomena: overthinking and false induction heads. The first phenomenon, overthinking, appears when we decode predictions from intermediate layers, given correct vs. incorrect few-shot demonstrations. At early layers, both demonstrations induce similar model behavior, but the behavior diverges sharply at some "critical layer", after which the accuracy given incorrect demonstrations progressively decreases. The second phenomenon, false induction heads, are a possible mechanistic cause of overthinking: these are heads in late layers that attend to and copy false information from previous demonstrations, and whose ablation reduces overthinking. Beyond scientific understanding, our results suggest that studying intermediate model computations could be a promising avenue for understanding and guarding against harmful model behaviors.
    Certifying the Fairness of KNN in the Presence of Dataset Bias. (arXiv:2307.08722v1 [cs.LG])
    We propose a method for certifying the fairness of the classification result of a widely used supervised learning algorithm, the k-nearest neighbors (KNN), under the assumption that the training data may have historical bias caused by systematic mislabeling of samples from a protected minority group. To the best of our knowledge, this is the first certification method for KNN based on three variants of the fairness definition: individual fairness, $\epsilon$-fairness, and label-flipping fairness. We first define the fairness certification problem for KNN and then propose sound approximations of the complex arithmetic computations used in the state-of-the-art KNN algorithm. This is meant to lift the computation results from the concrete domain to an abstract domain, to reduce the computational cost. We show effectiveness of this abstract interpretation based technique through experimental evaluation on six datasets widely used in the fairness research literature. We also show that the method is accurate enough to obtain fairness certifications for a large number of test inputs, despite the presence of historical bias in the datasets.
    Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information. (arXiv:2307.08964v1 [cs.LG])
    Recent works in learning-integrated optimization have shown promise in settings where the optimization problem is only partially observed or where general-purpose optimizers perform poorly without expert tuning. By learning an optimizer $\mathbf{g}$ to tackle these challenging problems with $f$ as the objective, the optimization process can be substantially accelerated by leveraging past experience. The optimizer can be trained with supervision from known optimal solutions or implicitly by optimizing the compound function $f\circ \mathbf{g}$. The implicit approach may not require optimal solutions as labels and is capable of handling problem uncertainty; however, it is slow to train and deploy due to frequent calls to optimizer $\mathbf{g}$ during both training and testing. The training is further challenged by sparse gradients of $\mathbf{g}$, especially for combinatorial solvers. To address these challenges, we propose using a smooth and learnable Landscape Surrogate $M$ as a replacement for $f\circ \mathbf{g}$. This surrogate, learnable by neural networks, can be computed faster than the solver $\mathbf{g}$, provides dense and smooth gradients during training, can generalize to unseen optimization problems, and is efficiently learned via alternating optimization. We test our approach on both synthetic problems, including shortest path and multidimensional knapsack, and real-world problems such as portfolio optimization, achieving comparable or superior objective values compared to state-of-the-art baselines while reducing the number of calls to $\mathbf{g}$. Notably, our approach outperforms existing methods for computationally expensive high-dimensional problems.
    Non-stationary Delayed Combinatorial Semi-Bandit with Causally Related Rewards. (arXiv:2307.09093v1 [cs.LG])
    Sequential decision-making under uncertainty is often associated with long feedback delays. Such delays degrade the performance of the learning agent in identifying a subset of arms with the optimal collective reward in the long run. This problem becomes significantly challenging in a non-stationary environment with structural dependencies amongst the reward distributions associated with the arms. Therefore, besides adapting to delays and environmental changes, learning the causal relations alleviates the adverse effects of feedback delay on the decision-making process. We formalize the described setting as a non-stationary and delayed combinatorial semi-bandit problem with causally related rewards. We model the causal relations by a directed graph in a stationary structural equation model. The agent maximizes the long-term average payoff, defined as a linear function of the base arms' rewards. We develop a policy that learns the structural dependencies from delayed feedback and utilizes that to optimize the decision-making while adapting to drifts. We prove a regret bound for the performance of the proposed algorithm. Besides, we evaluate our method via numerical analysis using synthetic and real-world datasets to detect the regions that contribute the most to the spread of Covid-19 in Italy.
    Self-Repellent Random Walks on General Graphs -- Achieving Minimal Sampling Variance via Nonlinear Markov Chains. (arXiv:2305.05097v2 [math.PR] UPDATED)
    We consider random walks on discrete state spaces, such as general undirected graphs, where the random walkers are designed to approximate a target quantity over the network topology via sampling and neighborhood exploration in the form of Markov chain Monte Carlo (MCMC) procedures. Given any Markov chain corresponding to a target probability distribution, we design a self-repellent random walk (SRRW) which is less likely to transition to nodes that were highly visited in the past, and more likely to transition to seldom visited nodes. For a class of SRRWs parameterized by a positive real {\alpha}, we prove that the empirical distribution of the process converges almost surely to the the target (stationary) distribution of the underlying Markov chain kernel. We then provide a central limit theorem and derive the exact form of the arising asymptotic co-variance matrix, which allows us to show that the SRRW with a stronger repellence (larger {\alpha}) always achieves a smaller asymptotic covariance, in the sense of Loewner ordering of co-variance matrices. Especially for SRRW-driven MCMC algorithms, we show that the decrease in the asymptotic sampling variance is of the order O(1/{\alpha}), eventually going down to zero. Finally, we provide numerical simulations complimentary to our theoretical results, also empirically testing a version of SRRW with {\alpha} increasing in time to combine the benefits of smaller asymptotic variance due to large {\alpha}, with empirically observed faster mixing properties of SRRW with smaller {\alpha}.
    An Empirical Study of the Effectiveness of Using a Replay Buffer on Mode Discovery in GFlowNets. (arXiv:2307.07674v2 [cs.LG] UPDATED)
    Reinforcement Learning (RL) algorithms aim to learn an optimal policy by iteratively sampling actions to learn how to maximize the total expected return, $R(x)$. GFlowNets are a special class of algorithms designed to generate diverse candidates, $x$, from a discrete set, by learning a policy that approximates the proportional sampling of $R(x)$. GFlowNets exhibit improved mode discovery compared to conventional RL algorithms, which is very useful for applications such as drug discovery and combinatorial search. However, since GFlowNets are a relatively recent class of algorithms, many techniques which are useful in RL have not yet been associated with them. In this paper, we study the utilization of a replay buffer for GFlowNets. We explore empirically various replay buffer sampling techniques and assess the impact on the speed of mode discovery and the quality of the modes discovered. Our experimental results in the Hypergrid toy domain and a molecule synthesis environment demonstrate significant improvements in mode discovery when training with a replay buffer, compared to training only with trajectories generated on-policy.
    Knowledge-infused Deep Learning Enables Interpretable Landslide Forecasting. (arXiv:2307.08951v1 [cs.LG])
    Forecasting how landslides will evolve over time or whether they will fail is a challenging task due to a variety of factors, both internal and external. Despite their considerable potential to address these challenges, deep learning techniques lack interpretability, undermining the credibility of the forecasts they produce. The recent development of transformer-based deep learning offers untapped possibilities for forecasting landslides with unprecedented interpretability and nonlinear feature learning capabilities. Here, we present a deep learning pipeline that is capable of predicting landslide behavior holistically, which employs a transformer-based network called LFIT to learn complex nonlinear relationships from prior knowledge and multiple source data, identifying the most relevant variables, and demonstrating a comprehensive understanding of landslide evolution and temporal patterns. By integrating prior knowledge, we provide improvement in holistic landslide forecasting, enabling us to capture diverse responses to various influencing factors in different local landslide areas. Using deformation observations as proxies for measuring the kinetics of landslides, we validate our approach by training models to forecast reservoir landslides in the Three Gorges Reservoir and creeping landslides on the Tibetan Plateau. When prior knowledge is incorporated, we show that interpretable landslide forecasting effectively identifies influential factors across various landslides. It further elucidates how local areas respond to these factors, making landslide behavior and trends more interpretable and predictable. The findings from this study will contribute to understanding landslide behavior in a new way and make the proposed approach applicable to other complex disasters influenced by internal and external factors in the future.
    Learning Adaptive Neighborhoods for Graph Neural Networks. (arXiv:2307.09065v1 [cs.CV])
    Graph convolutional networks (GCNs) enable end-to-end learning on graph structured data. However, many works assume a given graph structure. When the input graph is noisy or unavailable, one approach is to construct or learn a latent graph structure. These methods typically fix the choice of node degree for the entire graph, which is suboptimal. Instead, we propose a novel end-to-end differentiable graph generator which builds graph topologies where each node selects both its neighborhood and its size. Our module can be readily integrated into existing pipelines involving graph convolution operations, replacing the predetermined or existing adjacency matrix with one that is learned, and optimized, as part of the general objective. As such it is applicable to any GCN. We integrate our module into trajectory prediction, point cloud classification and node classification pipelines resulting in improved accuracy over other structure-learning methods across a wide range of datasets and GCN backbones.
    Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition Paths. (arXiv:2207.02149v2 [q-bio.BM] UPDATED)
    We consider the problem of sampling transition paths between two given metastable states of a molecular system, e.g. a folded and unfolded protein or products and reactants of a chemical reaction. Due to the existence of high energy barriers separating the states, these transition paths are unlikely to be sampled with standard Molecular Dynamics (MD) simulation. Traditional methods to augment MD with a bias potential to increase the probability of the transition rely on a dimensionality reduction step based on Collective Variables (CVs). Unfortunately, selecting appropriate CVs requires chemical intuition and traditional methods are therefore not always applicable to larger systems. Additionally, when incorrect CVs are used, the bias potential might not be minimal and bias the system along dimensions irrelevant to the transition. Showing a formal relation between the problem of sampling molecular transition paths, the Schr\"odinger bridge problem and stochastic optimal control with neural network policies, we propose a machine learning method for sampling said transitions. Unlike previous non-machine learning approaches our method, named PIPS, does not depend on CVs. We show that our method successful generates low energy transitions for Alanine Dipeptide as well as the larger Polyproline and Chignolin proteins.
    High Fidelity Image Counterfactuals with Probabilistic Causal Models. (arXiv:2306.15764v2 [cs.LG] UPDATED)
    We present a general causal generative modelling framework for accurate estimation of high fidelity image counterfactuals with deep structural causal models. Estimation of interventional and counterfactual queries for high-dimensional structured variables, such as images, remains a challenging task. We leverage ideas from causal mediation analysis and advances in generative modelling to design new deep causal mechanisms for structured variables in causal models. Our experiments demonstrate that our proposed mechanisms are capable of accurate abduction and estimation of direct, indirect and total effects as measured by axiomatic soundness of counterfactuals.
    Efficient Large-Scale Visual Representation Learning And Evaluation. (arXiv:2305.13399v4 [cs.CV] UPDATED)
    In this article, we present our approach to single-modality visual representation learning. Understanding visual representations of items is vital for fashion recommendations in e-commerce. We detail and contrast techniques used to finetune large-scale visual representation learning models in an efficient manner under low-resource settings, including several pretrained backbone architectures, both in the convolutional neural network as well as the vision transformer family. We describe the challenges for e-commerce applications at-scale and highlight the efforts to more efficiently train, evaluate, and serve visual representations. We present ablation studies evaluating the representation offline performance for several downstream tasks, including visually similar ad recommendations on mobile devices. To this end, we present a novel multilingual text-to-image generative offline evaluation method for visually similar recommendation systems. Finally, we include online results from deployed machine learning systems in production at Etsy.
    Causal-Based Supervision of Attention in Graph Neural Network: A Better and Simpler Choice towards Powerful Attention. (arXiv:2305.13115v2 [cs.LG] UPDATED)
    Recent years have witnessed the great potential of attention mechanism in graph representation learning. However, while variants of attention-based GNNs are setting new benchmarks for numerous real-world datasets, recent works have pointed out that their induced attentions are less robust and generalizable against noisy graphs due to lack of direct supervision. In this paper, we present a new framework which utilizes the tool of causality to provide a powerful supervision signal for the learning process of attention functions. Specifically, we estimate the direct causal effect of attention to the final prediction, and then maximize such effect to guide attention attending to more meaningful neighbors. Our method can serve as a plug-and-play module for any canonical attention-based GNNs in an end-to-end fashion. Extensive experiments on a wide range of benchmark datasets illustrated that, by directly supervising attention functions, the model is able to converge faster with a clearer decision boundary, and thus yields better performances.
    Machine Learning Enhanced Hankel Dynamic-Mode Decomposition. (arXiv:2303.06289v3 [cs.LG] UPDATED)
    While the acquisition of time series has become more straightforward, developing dynamical models from time series is still a challenging and evolving problem domain. Within the last several years, to address this problem, there has been a merging of machine learning tools with what is called the dynamic mode decomposition (DMD). This general approach has been shown to be an especially promising avenue for accurate model development. Building on this prior body of work, we develop a deep learning DMD based method which makes use of the fundamental insight of Takens' Embedding Theorem to build an adaptive learning scheme that better approximates higher dimensional and chaotic dynamics. We call this method the Deep Learning Hankel DMD (DLHDMD). We likewise explore how our method learns mappings which tend, after successful training, to significantly change the mutual information between dimensions in the dynamics. This appears to be a key feature in enhancing the DMD overall, and it should help provide further insight for developing other deep learning methods for time series analysis and model generation.
    High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance. (arXiv:2302.00999v2 [math.OC] UPDATED)
    During recent years the interest of optimization and machine learning communities in high-probability convergence of stochastic optimization methods has been growing. One of the main reasons for this is that high-probability complexity bounds are more accurate and less studied than in-expectation ones. However, SOTA high-probability non-asymptotic convergence results are derived under strong assumptions such as the boundedness of the gradient noise variance or of the objective's gradient itself. In this paper, we propose several algorithms with high-probability convergence results under less restrictive assumptions. In particular, we derive new high-probability convergence results under the assumption that the gradient/operator noise has bounded central $\alpha$-th moment for $\alpha \in (1,2]$ in the following setups: (i) smooth non-convex / Polyak-Lojasiewicz / convex / strongly convex / quasi-strongly convex minimization problems, (ii) Lipschitz / star-cocoercive and monotone / quasi-strongly monotone variational inequalities. These results justify the usage of the considered methods for solving problems that do not fit standard functional classes studied in stochastic optimization.
    Conditionally Calibrated Predictive Distributions by Probability-Probability Map: Application to Galaxy Redshift Estimation and Probabilistic Forecasting. (arXiv:2205.14568v4 [stat.ML] UPDATED)
    Uncertainty quantification is crucial for assessing the predictive ability of AI algorithms. Much research has been devoted to describing the predictive distribution (PD) $F(y|\mathbf{x})$ of a target variable $y \in \mathbb{R}$ given complex input features $\mathbf{x} \in \mathcal{X}$. However, off-the-shelf PDs (from, e.g., normalizing flows and Bayesian neural networks) often lack conditional calibration with the probability of occurrence of an event given input $\mathbf{x}$ being significantly different from the predicted probability. Current calibration methods do not fully assess and enforce conditionally calibrated PDs. Here we propose \texttt{Cal-PIT}, a method that addresses both PD diagnostics and recalibration by learning a single probability-probability map from calibration data. The key idea is to regress probability integral transform scores against $\mathbf{x}$. The estimated regression provides interpretable diagnostics of conditional coverage across the feature space. The same regression function morphs the misspecified PD to a re-calibrated PD for all $\mathbf{x}$. We benchmark our corrected prediction bands (a by-product of corrected PDs) against oracle bands and state-of-the-art predictive inference algorithms for synthetic data. We also provide results for two applications: (i) probabilistic nowcasting given sequences of satellite images, and (ii) conditional density estimation of galaxy distances given imaging data (so-called photometric redshift estimation). Our code is available as a Python package https://github.com/lee-group-cmu/Cal-PIT .
    Martian time-series unraveled: A multi-scale nested approach with factorial variational autoencoders. (arXiv:2305.16189v2 [cs.LG] UPDATED)
    Unsupervised source separation involves unraveling an unknown set of source signals recorded through a mixing operator, with limited prior knowledge about the sources, and only access to a dataset of signal mixtures. This problem is inherently ill-posed and is further challenged by the variety of time-scales exhibited by sources in time series data. Existing methods typically rely on a preselected window size that limits their capacity to handle multi-scale sources. To address this issue, instead of operating in the time domain, we propose an unsupervised multi-scale clustering and source separation framework by leveraging wavelet scattering covariances that provide a low-dimensional representation of stochastic processes, capable of distinguishing between different non-Gaussian stochastic processes. Nested within this representation space, we develop a factorial Gaussian-mixture variational autoencoder that is trained to (1) probabilistically cluster sources at different time-scales and (2) independently sample scattering covariance representations associated with each cluster. Using samples from each cluster as prior information, we formulate source separation as an optimization problem in the wavelet scattering covariance representation space, resulting in separated sources in the time domain. When applied to seismic data recorded during the NASA InSight mission on Mars, our multi-scale nested approach proves to be a powerful tool for discriminating between sources varying greatly in time-scale, e.g., minute-long transient one-sided pulses (known as ``glitches'') and structured ambient noises resulting from atmospheric activities that typically last for tens of minutes. These results provide an opportunity to conduct further investigations into the isolated sources related to atmospheric-surface interactions, thermal relaxations, and other complex phenomena.
    A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework. (arXiv:2204.03719v2 [cs.LG] UPDATED)
    Class imbalance poses new challenges when it comes to classifying data streams. Many algorithms recently proposed in the literature tackle this problem using a variety of data-level, algorithm-level, and ensemble approaches. However, there is a lack of standardized and agreed-upon procedures and benchmarks on how to evaluate these algorithms. This work proposes a standardized, exhaustive, and comprehensive experimental framework to evaluate algorithms in a collection of diverse and challenging imbalanced data stream scenarios. The experimental study evaluates 24 state-of-the-art data streams algorithms on 515 imbalanced data streams that combine static and dynamic class imbalance ratios, instance-level difficulties, concept drift, real-world and semi-synthetic datasets in binary and multi-class scenarios. This leads to a large-scale experimental study comparing state-of-the-art classifiers in the data stream mining domain. We discuss the advantages and disadvantages of state-of-the-art classifiers in each of these scenarios and we provide general recommendations to end-users for selecting the best algorithms for imbalanced data streams. Additionally, we formulate open challenges and future directions for this domain. Our experimental framework is fully reproducible and easy to extend with new methods. This way, we propose a standardized approach to conducting experiments in imbalanced data streams that can be used by other researchers to create complete, trustworthy, and fair evaluation of newly proposed methods. Our experimental framework can be downloaded from https://github.com/canoalberto/imbalanced-streams.
    Unsupervised Conditional Slot Attention for Object Centric Learning. (arXiv:2307.09437v1 [cs.LG])
    Extracting object-level representations for downstream reasoning tasks is an emerging area in AI. Learning object-centric representations in an unsupervised setting presents multiple challenges, a key one being binding an arbitrary number of object instances to a specialized object slot. Recent object-centric representation methods like Slot Attention utilize iterative attention to learn composable representations with dynamic inference level binding but fail to achieve specialized slot level binding. To address this, in this paper we propose Unsupervised Conditional Slot Attention using a novel Probabilistic Slot Dictionary (PSD). We define PSD with (i) abstract object-level property vectors as key and (ii) parametric Gaussian distribution as its corresponding value. We demonstrate the benefits of the learnt specific object-level conditioning distributions in multiple downstream tasks, namely object discovery, compositional scene generation, and compositional visual reasoning. We show that our method provides scene composition capabilities and a significant boost in a few shot adaptability tasks of compositional visual reasoning, while performing similarly or better than slot attention in object discovery tasks
    Nonuniqueness and Convergence to Equivalent Solutions in Observer-based Inverse Reinforcement Learning. (arXiv:2210.16299v2 [eess.SY] UPDATED)
    A key challenge in solving the deterministic inverse reinforcement learning (IRL) problem online and in real-time is the existence of multiple solutions. Nonuniqueness necessitates the study of the notion of equivalent solutions, i.e., solutions that result in a different cost functional but same feedback matrix, and convergence to such solutions. While offline algorithms that result in convergence to equivalent solutions have been developed in the literature, online, real-time techniques that address nonuniqueness are not available. In this paper, a regularized history stack observer that converges to approximately equivalent solutions of the IRL problem is developed. Novel data-richness conditions are developed to facilitate the analysis and simulation results are provided to demonstrate the effectiveness of the developed technique.
    Automatic Differentiation for Inverse Problems with Applications in Quantum Transport. (arXiv:2307.09311v1 [cs.LG])
    A neural solver and differentiable simulation of the quantum transmitting boundary model is presented for the inverse quantum transport problem. The neural solver is used to engineer continuous transmission properties and the differentiable simulation is used to engineer current-voltage characteristics.
    Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey. (arXiv:2307.09182v1 [cs.LG])
    With an increasing number of smart devices like internet of things (IoT) devices deployed in the field, offloadingtraining of neural networks (NNs) to a central server becomes more and more infeasible. Recent efforts toimprove users' privacy have led to on-device learning emerging as an alternative. However, a model trainedonly on a single device, using only local data, is unlikely to reach a high accuracy. Federated learning (FL)has been introduced as a solution, offering a privacy-preserving trade-off between communication overheadand model accuracy by sharing knowledge between devices but disclosing the devices' private data. Theapplicability and the benefit of applying baseline FL are, however, limited in many relevant use cases dueto the heterogeneity present in such environments. In this survey, we outline the heterogeneity challengesFL has to overcome to be widely applicable in real-world applications. We especially focus on the aspect ofcomputation heterogeneity among the participating devices and provide a comprehensive overview of recentworks on heterogeneity-aware FL. We discuss two groups: works that adapt the NN architecture and worksthat approach heterogeneity on a system level, covering Federated Averaging (FedAvg), distillation, and splitlearning-based approaches, as well as synchronous and asynchronous aggregation schemes.
    Inverse Optimization for Routing Problems. (arXiv:2307.07357v1 [math.OC] CROSS LISTED)
    We propose a method for learning decision-makers' behavior in routing problems using Inverse Optimization (IO). The IO framework falls into the supervised learning category and builds on the premise that the target behavior is an optimizer of an unknown cost function. This cost function is to be learned through historical data, and in the context of routing problems, can be interpreted as the routing preferences of the decision-makers. In this view, the main contributions of this study are to propose an IO methodology with a hypothesis function, loss function, and stochastic first-order algorithm tailored to routing problems. We further test our IO approach in the Amazon Last Mile Routing Research Challenge, where the goal is to learn models that replicate the routing preferences of human drivers, using thousands of real-world routing examples. Our final IO-learned routing model achieves a score that ranks 2nd compared with the 48 models that qualified for the final round of the challenge. Our results showcase the flexibility and real-world potential of the proposed IO methodology to learn from decision-makers' decisions in routing problems.
    Optimistic Estimate Uncovers the Potential of Nonlinear Models. (arXiv:2307.08921v1 [cs.LG])
    We propose an optimistic estimate to evaluate the best possible fitting performance of nonlinear models. It yields an optimistic sample size that quantifies the smallest possible sample size to fit/recover a target function using a nonlinear model. We estimate the optimistic sample sizes for matrix factorization models, deep models, and deep neural networks (DNNs) with fully-connected or convolutional architecture. For each nonlinear model, our estimates predict a specific subset of targets that can be fitted at overparameterization, which are confirmed by our experiments. Our optimistic estimate reveals two special properties of the DNN models -- free expressiveness in width and costly expressiveness in connection. These properties suggest the following architecture design principles of DNNs: (i) feel free to add neurons/kernels; (ii) restrain from connecting neurons. Overall, our optimistic estimate theoretically unveils the vast potential of nonlinear models in fitting at overparameterization. Based on this framework, we anticipate gaining a deeper understanding of how and why numerous nonlinear models such as DNNs can effectively realize their potential in practice in the near future.
    A benchmark of categorical encoders for binary classification. (arXiv:2307.09191v1 [cs.LG])
    Categorical encoders transform categorical features into numerical representations that are indispensable for a wide range of machine learning models. Existing encoder benchmark studies lack generalizability because of their limited choice of (1) encoders, (2) experimental factors, and (3) datasets. Additionally, inconsistencies arise from the adoption of varying aggregation strategies. This paper is the most comprehensive benchmark of categorical encoders to date, including an extensive evaluation of 32 configurations of encoders from diverse families, with 36 combinations of experimental factors, and on 50 datasets. The study shows the profound influence of dataset selection, experimental factors, and aggregation strategies on the benchmark's conclusions -- aspects disregarded in previous encoder benchmarks.
    \nu-Flows: Conditional Neutrino Regression. (arXiv:2207.00664v7 [hep-ph] CROSS LISTED)
    We present $\nu$-Flows, a novel method for restricting the likelihood space of neutrino kinematics in high energy collider experiments using conditional normalizing flows and deep invertible neural networks. This method allows the recovery of the full neutrino momentum which is usually left as a free parameter and permits one to sample neutrino values under a learned conditional likelihood given event observations. We demonstrate the success of $\nu$-Flows in a case study by applying it to simulated semileptonic $t\bar{t}$ events and show that it can lead to more accurate momentum reconstruction, particularly of the longitudinal coordinate. We also show that this has direct benefits in a downstream task of jet association, leading to an improvement of up to a factor of 1.41 compared to conventional methods.
    An Alternative to Variance: Gini Deviation for Risk-averse Policy Gradient. (arXiv:2307.08873v1 [cs.LG])
    Restricting the variance of a policy's return is a popular choice in risk-averse Reinforcement Learning (RL) due to its clear mathematical definition and easy interpretability. Traditional methods directly restrict the total return variance. Recent methods restrict the per-step reward variance as a proxy. We thoroughly examine the limitations of these variance-based methods, such as sensitivity to numerical scale and hindering of policy learning, and propose to use an alternative risk measure, Gini deviation, as a substitute. We study various properties of this new risk measure and derive a policy gradient algorithm to minimize it. Empirical evaluation in domains where risk-aversion can be clearly defined, shows that our algorithm can mitigate the limitations of variance-based risk measures and achieves high return with low risk in terms of variance and Gini deviation when others fail to learn a reasonable policy.
    Towards Accelerating Benders Decomposition via Reinforcement Learning Surrogate Models. (arXiv:2307.08816v1 [cs.LG])
    Stochastic optimization (SO) attempts to offer optimal decisions in the presence of uncertainty. Often, the classical formulation of these problems becomes intractable due to (a) the number of scenarios required to capture the uncertainty and (b) the discrete nature of real-world planning problems. To overcome these tractability issues, practitioners turn to decomposition methods that divide the problem into smaller, more tractable sub-problems. The focal decomposition method of this paper is Benders decomposition (BD), which decomposes stochastic optimization problems on the basis of scenario independence. In this paper we propose a method of accelerating BD with the aid of a surrogate model in place of an NP-hard integer master problem. Through the acceleration method we observe 30% faster average convergence when compared to other accelerated BD implementations. We introduce a reinforcement learning agent as a surrogate and demonstrate how it can be used to solve a stochastic inventory management problem.
    GraphCL-DTA: a graph contrastive learning with molecular semantics for drug-target binding affinity prediction. (arXiv:2307.08989v1 [cs.LG])
    Drug-target binding affinity prediction plays an important role in the early stages of drug discovery, which can infer the strength of interactions between new drugs and new targets. However, the performance of previous computational models is limited by the following drawbacks. The learning of drug representation relies only on supervised data, without taking into account the information contained in the molecular graph itself. Moreover, most previous studies tended to design complicated representation learning module, while uniformity, which is used to measure representation quality, is ignored. In this study, we propose GraphCL-DTA, a graph contrastive learning with molecular semantics for drug-target binding affinity prediction. In GraphCL-DTA, we design a graph contrastive learning framework for molecular graphs to learn drug representations, so that the semantics of molecular graphs are preserved. Through this graph contrastive framework, a more essential and effective drug representation can be learned without additional supervised data. Next, we design a new loss function that can be directly used to smoothly adjust the uniformity of drug and target representations. By directly optimizing the uniformity of representations, the representation quality of drugs and targets can be improved. The effectiveness of the above innovative elements is verified on two real datasets, KIBA and Davis. The excellent performance of GraphCL-DTA on the above datasets suggests its superiority to the state-of-the-art model.
    Machine Learning Meets Mental Training -- A Proof of Concept Applied to Memory Sports. (arXiv:2307.08712v1 [cs.LG])
    This work aims to combine these two fields together by presenting a practical implementation of machine learning to the particular form of mental training that is the art of memory, taken in its competitive version called "Memory Sports". Such a fusion, on the one hand, strives to raise awareness about both realms, while on the other it seeks to encourage research in this mixed field as a way to, ultimately, drive forward the development of this seemingly underestimated sport.
    From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs. (arXiv:2307.08433v2 [cs.LG] UPDATED)
    Many real-world datasets have an underlying dynamic graph structure, where entities and their interactions evolve over time. Machine learning models should consider these dynamics in order to harness their full potential in downstream tasks. Previous approaches for graph representation learning have focused on either sampling k-hop neighborhoods, akin to breadth-first search, or random walks, akin to depth-first search. However, these methods are computationally expensive and unsuitable for real-time, low-latency inference on dynamic graphs. To overcome these limitations, we propose graph-sprints a general purpose feature extraction framework for continuous-time-dynamic-graphs (CTDGs) that has low latency and is competitive with state-of-the-art, higher latency models. To achieve this, a streaming, low latency approximation to the random-walk based features is proposed. In our framework, time-aware node embeddings summarizing multi-hop information are computed using only single-hop operations on the incoming edges. We evaluate our proposed approach on three open-source datasets and two in-house datasets, and compare with three state-of-the-art algorithms (TGN-attn, TGN-ID, Jodie). We demonstrate that our graph-sprints features, combined with a machine learning classifier, achieve competitive performance (outperforming all baselines for the node classification tasks in five datasets). Simultaneously, graph-sprints significantly reduce inference latencies, achieving close to an order of magnitude speed-up in our experimental setting.
    EigenTrajectory: Low-Rank Descriptors for Multi-Modal Trajectory Forecasting. (arXiv:2307.09306v1 [cs.CV])
    Capturing high-dimensional social interactions and feasible futures is essential for predicting trajectories. To address this complex nature, several attempts have been devoted to reducing the dimensionality of the output variables via parametric curve fitting such as the B\'ezier curve and B-spline function. However, these functions, which originate in computer graphics fields, are not suitable to account for socially acceptable human dynamics. In this paper, we present EigenTrajectory ($\mathbb{ET}$), a trajectory prediction approach that uses a novel trajectory descriptor to form a compact space, known here as $\mathbb{ET}$ space, in place of Euclidean space, for representing pedestrian movements. We first reduce the complexity of the trajectory descriptor via a low-rank approximation. We transform the pedestrians' history paths into our $\mathbb{ET}$ space represented by spatio-temporal principle components, and feed them into off-the-shelf trajectory forecasting models. The inputs and outputs of the models as well as social interactions are all gathered and aggregated in the corresponding $\mathbb{ET}$ space. Lastly, we propose a trajectory anchor-based refinement method to cover all possible futures in the proposed $\mathbb{ET}$ space. Extensive experiments demonstrate that our EigenTrajectory predictor can significantly improve both the prediction accuracy and reliability of existing trajectory forecasting models on public benchmarks, indicating that the proposed descriptor is suited to represent pedestrian behaviors. Code is publicly available at https://github.com/inhwanbae/EigenTrajectory .
    Resource frugal optimizer for quantum machine learning. (arXiv:2211.04965v2 [quant-ph] UPDATED)
    Quantum-enhanced data science, also known as quantum machine learning (QML), is of growing interest as an application of near-term quantum computers. Variational QML algorithms have the potential to solve practical problems on real hardware, particularly when involving quantum data. However, training these algorithms can be challenging and calls for tailored optimization procedures. Specifically, QML applications can require a large shot-count overhead due to the large datasets involved. In this work, we advocate for simultaneous random sampling over both the dataset as well as the measurement operators that define the loss function. We consider a highly general loss function that encompasses many QML applications, and we show how to construct an unbiased estimator of its gradient. This allows us to propose a shot-frugal gradient descent optimizer called Refoqus (REsource Frugal Optimizer for QUantum Stochastic gradient descent). Our numerics indicate that Refoqus can save several orders of magnitude in shot cost, even relative to optimizers that sample over measurement operators alone.
    Reduced Kernel Dictionary Learning. (arXiv:2307.08798v1 [eess.SP])
    In this paper we present new algorithms for training reduced-size nonlinear representations in the Kernel Dictionary Learning (KDL) problem. Standard KDL has the drawback of a large size of the kernel matrix when the data set is large. There are several ways of reducing the kernel size, notably Nystr\"om sampling. We propose here a method more in the spirit of dictionary learning, where the kernel vectors are obtained with a trained sparse representation of the input signals. Moreover, we optimize directly the kernel vectors in the KDL process, using gradient descent steps. We show with three data sets that our algorithms are able to provide better representations, despite using a small number of kernel vectors, and also decrease the execution time with respect to KDL.
    Multi-Modal Discussion Transformer: Integrating Text, Images and Graph Transformers to Detect Hate Speech on Social Media. (arXiv:2307.09312v1 [cs.CL])
    We present the Multi-Modal Discussion Transformer (mDT), a novel multi-modal graph-based transformer model for detecting hate speech in online social networks. In contrast to traditional text-only methods, our approach to labelling a comment as hate speech centers around the holistic analysis of text and images. This is done by leveraging graph transformers to capture the contextual relationships in the entire discussion that surrounds a comment, with interwoven fusion layers to combine text and image embeddings instead of processing different modalities separately. We compare the performance of our model to baselines that only process text; we also conduct extensive ablation studies. We conclude with future work for multimodal solutions to deliver social value in online contexts, arguing that capturing a holistic view of a conversation greatly advances the effort to detect anti-social behavior.
    Forecasting the steam mass flow in a powerplant using the parallel hybrid network. (arXiv:2307.09483v1 [cs.LG])
    Efficient and sustainable power generation is a crucial concern in the energy sector. In particular, thermal power plants grapple with accurately predicting steam mass flow, which is crucial for operational efficiency and cost reduction. In this study, we use a parallel hybrid neural network architecture that combines a parametrized quantum circuit and a conventional feed-forward neural network specifically designed for time-series prediction in industrial settings to enhance predictions of steam mass flow 15 minutes into the future. Our results show that the parallel hybrid model outperforms standalone classical and quantum models, achieving more than 5.7 and 4.9 times lower mean squared error (MSE) loss on the test set after training compared to pure classical and pure quantum networks, respectively. Furthermore, the hybrid model demonstrates smaller relative errors between the ground truth and the model predictions on the test set, up to 2 times better than the pure classical model. These findings contribute to the broader scientific understanding of how integrating quantum and classical machine learning techniques can be applied to real-world challenges faced by the energy sector, ultimately leading to optimized power plant operations.
    Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts. (arXiv:2304.03209v2 [cs.CV] UPDATED)
    Integrating high-level semantically correlated contents and low-level anatomical features is of central importance in medical image segmentation. Towards this end, recent deep learning-based medical segmentation methods have shown great promise in better modeling such information. However, convolution operators for medical segmentation typically operate on regular grids, which inherently blur the high-frequency regions, i.e., boundary regions. In this work, we propose MORSE, a generic implicit neural rendering framework designed at an anatomical level to assist learning in medical image segmentation. Our method is motivated by the fact that implicit neural representation has been shown to be more effective in fitting complex signals and solving computer graphics problems than discrete grid-based representation. The core of our approach is to formulate medical image segmentation as a rendering problem in an end-to-end manner. Specifically, we continuously align the coarse segmentation prediction with the ambiguous coordinate-based point representations and aggregate these features to adaptively refine the boundary region. To parallelly optimize multi-scale pixel-level features, we leverage the idea from Mixture-of-Expert (MoE) to design and train our MORSE with a stochastic gating mechanism. Our experiments demonstrate that MORSE can work well with different medical segmentation backbones, consistently achieving competitive performance improvements in both 2D and 3D supervised medical segmentation methods. We also theoretically analyze the superiority of MORSE.
    Globally solving the Gromov-Wasserstein problem for point clouds in low dimensional Euclidean spaces. (arXiv:2307.09057v1 [math.OC])
    This paper presents a framework for computing the Gromov-Wasserstein problem between two sets of points in low dimensional spaces, where the discrepancy is the squared Euclidean norm. The Gromov-Wasserstein problem is a generalization of the optimal transport problem that finds the assignment between two sets preserving pairwise distances as much as possible. This can be used to quantify the similarity between two formations or shapes, a common problem in AI and machine learning. The problem can be formulated as a Quadratic Assignment Problem (QAP), which is in general computationally intractable even for small problems. Our framework addresses this challenge by reformulating the QAP as an optimization problem with a low-dimensional domain, leveraging the fact that the problem can be expressed as a concave quadratic optimization problem with low rank. The method scales well with the number of points, and it can be used to find the global solution for large-scale problems with thousands of points. We compare the computational complexity of our approach with state-of-the-art methods on synthetic problems and apply it to a near-symmetrical problem which is of particular interest in computational biology.
    Variational Monte Carlo on a Budget -- Fine-tuning pre-trained Neural Wavefunctions. (arXiv:2307.09337v1 [physics.chem-ph])
    Obtaining accurate solutions to the Schr\"odinger equation is the key challenge in computational quantum chemistry. Deep-learning-based Variational Monte Carlo (DL-VMC) has recently outperformed conventional approaches in terms of accuracy, but only at large computational cost. Whereas in many domains models are trained once and subsequently applied for inference, accurate DL-VMC so far requires a full optimization for every new problem instance, consuming thousands of GPUhs even for small molecules. We instead propose a DL-VMC model which has been pre-trained using self-supervised wavefunction optimization on a large and chemically diverse set of molecules. Applying this model to new molecules without any optimization, yields wavefunctions and absolute energies that outperform established methods such as CCSD(T)-2Z. To obtain accurate relative energies, only few fine-tuning steps of this base model are required. We accomplish this with a fully end-to-end machine-learned model, consisting of an improved geometry embedding architecture and an existing SE(3)-equivariant model to represent molecular orbitals. Combining this architecture with continuous sampling of geometries, we improve zero-shot accuracy by two orders of magnitude compared to the state of the art. We extensively evaluate the accuracy, scalability and limitations of our base model on a wide variety of test systems.  ( 2 min )
    Learning Dynamic Attribute-factored World Models for Efficient Multi-object Reinforcement Learning. (arXiv:2307.09205v1 [cs.LG])
    In many reinforcement learning tasks, the agent has to learn to interact with many objects of different types and generalize to unseen combinations and numbers of objects. Often a task is a composition of previously learned tasks (e.g. block stacking). These are examples of compositional generalization, in which we compose object-centric representations to solve complex tasks. Recent works have shown the benefits of object-factored representations and hierarchical abstractions for improving sample efficiency in these settings. On the other hand, these methods do not fully exploit the benefits of factorization in terms of object attributes. In this paper, we address this opportunity and introduce the Dynamic Attribute FacTored RL (DAFT-RL) framework. In DAFT-RL, we leverage object-centric representation learning to extract objects from visual inputs. We learn to classify them in classes and infer their latent parameters. For each class of object, we learn a class template graph that describes how the dynamics and reward of an object of this class factorize according to its attributes. We also learn an interaction pattern graph that describes how objects of different classes interact with each other at the attribute level. Through these graphs and a dynamic interaction graph that models the interactions between objects, we can learn a policy that can then be directly applied in a new environment by just estimating the interactions and latent parameters. We evaluate DAFT-RL in three benchmark datasets and show our framework outperforms the state-of-the-art in generalizing across unseen objects with varying attributes and latent parameters, as well as in the composition of previously learned tasks.  ( 3 min )
    A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning. (arXiv:2307.09218v1 [cs.LG])
    Forgetting refers to the loss or deterioration of previously acquired information or knowledge. While the existing surveys on forgetting have primarily focused on continual learning, forgetting is a prevalent phenomenon observed in various other research domains within deep learning. Forgetting manifests in research fields such as generative models due to generator shifts, and federated learning due to heterogeneous data distributions across clients. Addressing forgetting encompasses several challenges, including balancing the retention of old task knowledge with fast learning of new tasks, managing task interference with conflicting goals, and preventing privacy leakage, etc. Moreover, most existing surveys on continual learning implicitly assume that forgetting is always harmful. In contrast, our survey argues that forgetting is a double-edged sword and can be beneficial and desirable in certain cases, such as privacy-preserving scenarios. By exploring forgetting in a broader context, we aim to present a more nuanced understanding of this phenomenon and highlight its potential advantages. Through this comprehensive survey, we aspire to uncover potential solutions by drawing upon ideas and approaches from various fields that have dealt with forgetting. By examining forgetting beyond its conventional boundaries, in future work, we hope to encourage the development of novel strategies for mitigating, harnessing, or even embracing forgetting in real applications. A comprehensive list of papers about forgetting in various research fields is available at \url{https://github.com/EnnengYang/Awesome-Forgetting-in-Deep-Learning}.  ( 3 min )
    A Federated learning model for Electric Energy management using Blockchain Technology. (arXiv:2307.09080v1 [cs.LG])
    Energy shortfall and electricity load shedding are the main problems for developing countries. The main causes are lack of management in the energy sector and the use of non-renewable energy sources. The improved energy management and use of renewable sources can be significant to resolve energy crisis. It is necessary to increase the use of renewable energy sources (RESs) to meet the increasing energy demand due to high prices of fossil-fuel based energy. Federated learning (FL) is the most emerging technique in the field of artificial intelligence. Federated learning helps to generate global model at server side by ensemble locally trained models at remote edges sites while preserving data privacy. The global model used to predict energy demand to satisfy the needs of consumers. In this article, we have proposed Blockchain based safe distributed ledger technology for transaction of data between prosumer and consumer to ensure their transparency, traceability and security. Furthermore, we have also proposed a Federated learning model to forecast the energy requirements of consumer and prosumer. Moreover, Blockchain has been used to store excess energy data from prosumer for better management of energy between prosumer and grid. Lastly, the experiment results revealed that renewable energy sources have produced better and comparable results to other non-renewable energy resources.  ( 2 min )
    DeepMem: ML Models as storage channels and their (mis-)applications. (arXiv:2307.08811v1 [cs.LG])
    Machine learning (ML) models are overparameterized to support generality and avoid overfitting. Prior works have shown that these additional parameters can be used for both malicious (e.g., hiding a model covertly within a trained model) and beneficial purposes (e.g., watermarking a model). In this paper, we propose a novel information theoretic perspective of the problem; we consider the ML model as a storage channel with a capacity that increases with overparameterization. Specifically, we consider a sender that embeds arbitrary information in the model at training time, which can be extracted by a receiver with a black-box access to the deployed model. We derive an upper bound on the capacity of the channel based on the number of available parameters. We then explore black-box write and read primitives that allow the attacker to: (i) store data in an optimized way within the model by augmenting the training data at the transmitter side, and (ii) to read it by querying the model after it is deployed. We also analyze the detectability of the writing primitive and consider a new version of the problem which takes information storage covertness into account. Specifically, to obtain storage covertness, we introduce a new constraint such that the data augmentation used for the write primitives minimizes the distribution shift with the initial (baseline task) distribution. This constraint introduces a level of "interference" with the initial task, thereby limiting the channel's effective capacity. Therefore, we develop optimizations to improve the capacity in this case, including a novel ML-specific substitution based error correction protocol. We believe that the proposed modeling of the problem offers new tools to better understand and mitigate potential vulnerabilities of ML, especially in the context of increasingly large models.  ( 3 min )
    Towards Trustworthy Dataset Distillation. (arXiv:2307.09165v1 [cs.LG])
    Efficiency and trustworthiness are two eternal pursuits when applying deep learning in real-world applications. With regard to efficiency, dataset distillation (DD) endeavors to reduce training costs by distilling the large dataset into a tiny synthetic dataset. However, existing methods merely concentrate on in-distribution (InD) classification in a closed-world setting, disregarding out-of-distribution (OOD) samples. On the other hand, OOD detection aims to enhance models' trustworthiness, which is always inefficiently achieved in full-data settings. For the first time, we simultaneously consider both issues and propose a novel paradigm called Trustworthy Dataset Distillation (TrustDD). By distilling both InD samples and outliers, the condensed datasets are capable to train models competent in both InD classification and OOD detection. To alleviate the requirement of real outlier data and make OOD detection more practical, we further propose to corrupt InD samples to generate pseudo-outliers and introduce Pseudo-Outlier Exposure (POE). Comprehensive experiments on various settings demonstrate the effectiveness of TrustDD, and the proposed POE surpasses state-of-the-art method Outlier Exposure (OE). Compared with the preceding DD, TrustDD is more trustworthy and applicable to real open-world scenarios. Our code will be publicly available.  ( 2 min )
    qecGPT: decoding Quantum Error-correcting Codes with Generative Pre-trained Transformers. (arXiv:2307.09025v1 [quant-ph])
    We propose a general framework for decoding quantum error-correcting codes with generative modeling. The model utilizes autoregressive neural networks, specifically Transformers, to learn the joint probability of logical operators and syndromes. This training is in an unsupervised way, without the need for labeled training data, and is thus referred to as pre-training. After the pre-training, the model can efficiently compute the likelihood of logical operators for any given syndrome, using maximum likelihood decoding. It can directly generate the most-likely logical operators with computational complexity $\mathcal O(2k)$ in the number of logical qubits $k$, which is significantly better than the conventional maximum likelihood decoding algorithms that require $\mathcal O(4^k)$ computation. Based on the pre-trained model, we further propose refinement to achieve more accurately the likelihood of logical operators for a given syndrome by directly sampling the stabilizer operators. We perform numerical experiments on stabilizer codes with small code distances, using both depolarizing error models and error models with correlated noise. The results show that our approach provides significantly better decoding accuracy than the minimum weight perfect matching and belief-propagation-based algorithms. Our framework is general and can be applied to any error model and quantum codes with different topologies such as surface codes and quantum LDPC codes. Furthermore, it leverages the parallelization capabilities of GPUs, enabling simultaneous decoding of a large number of syndromes. Our approach sheds light on the efficient and accurate decoding of quantum error-correcting codes using generative artificial intelligence and modern computational power.  ( 3 min )
    U-shaped Transformer: Retain High Frequency Context in Time Series Analysis. (arXiv:2307.09019v1 [cs.LG])
    Time series prediction plays a crucial role in various industrial fields. In recent years, neural networks with a transformer backbone have achieved remarkable success in many domains, including computer vision and NLP. In time series analysis domain, some studies have suggested that even the simplest MLP networks outperform advanced transformer-based networks on time series forecast tasks. However, we believe these findings indicate there to be low-rank properties in time series sequences. In this paper, we consider the low-pass characteristics of transformers and try to incorporate the advantages of MLP. We adopt skip-layer connections inspired by Unet into traditional transformer backbone, thus preserving high-frequency context from input to output, namely U-shaped Transformer. We introduce patch merge and split operation to extract features with different scales and use larger datasets to fully make use of the transformer backbone. Our experiments demonstrate that the model performs at an advanced level across multiple datasets with relatively low cost.  ( 2 min )
    Towards Automated Design of Riboswitches. (arXiv:2307.08801v1 [cs.LG])
    Experimental screening and selection pipelines for the discovery of novel riboswitches are expensive, time-consuming, and inefficient. Using computational methods to reduce the number of candidates for the screen could drastically decrease these costs. However, existing computational approaches do not fully satisfy all requirements for the design of such initial screening libraries. In this work, we present a new method, libLEARNA, capable of providing RNA focus libraries of diverse variable-length qualified candidates. Our novel structure-based design approach considers global properties as well as desired sequence and structure features. We demonstrate the benefits of our method by designing theophylline riboswitch libraries, following a previously published protocol, and yielding 30% more unique high-quality candidates.  ( 2 min )
    Meta-Value Learning: a General Framework for Learning with Learning Awareness. (arXiv:2307.08863v1 [cs.LG])
    Gradient-based learning in multi-agent systems is difficult because the gradient derives from a first-order model which does not account for the interaction between agents' learning processes. LOLA (arXiv:1709.04326) accounts for this by differentiating through one step of optimization. We extend the ideas of LOLA and develop a fully-general value-based approach to optimization. At the core is a function we call the meta-value, which at each point in joint-policy space gives for each agent a discounted sum of its objective over future optimization steps. We argue that the gradient of the meta-value gives a more reliable improvement direction than the gradient of the original objective, because the meta-value derives from empirical observations of the effects of optimization. We show how the meta-value can be approximated by training a neural network to minimize TD error along optimization trajectories in which agents follow the gradient of the meta-value. We analyze the behavior of our method on the Logistic Game and on the Iterated Prisoner's Dilemma.  ( 2 min )
    Alioth: A Machine Learning Based Interference-Aware Performance Monitor for Multi-Tenancy Applications in Public Cloud. (arXiv:2307.08949v1 [cs.DC])
    Multi-tenancy in public clouds may lead to co-location interference on shared resources, which possibly results in performance degradation of cloud applications. Cloud providers want to know when such events happen and how serious the degradation is, to perform interference-aware migrations and alleviate the problem. However, virtual machines (VM) in Infrastructure-as-a-Service public clouds are black-boxes to providers, where application-level performance information cannot be acquired. This makes performance monitoring intensely challenging as cloud providers can only rely on low-level metrics such as CPU usage and hardware counters. We propose a novel machine learning framework, Alioth, to monitor the performance degradation of cloud applications. To feed the data-hungry models, we first elaborate interference generators and conduct comprehensive co-location experiments on a testbed to build Alioth-dataset which reflects the complexity and dynamicity in real-world scenarios. Then we construct Alioth by (1) augmenting features via recovering low-level metrics under no interference using denoising auto-encoders, (2) devising a transfer learning model based on domain adaptation neural network to make models generalize on test cases unseen in offline training, and (3) developing a SHAP explainer to automate feature selection and enhance model interpretability. Experiments show that Alioth achieves an average mean absolute error of 5.29% offline and 10.8% when testing on applications unseen in the training stage, outperforming the baseline methods. Alioth is also robust in signaling quality-of-service violation under dynamicity. Finally, we demonstrate a possible application of Alioth's interpretability, providing insights to benefit the decision-making of cloud operators. The dataset and code of Alioth have been released on GitHub.  ( 3 min )
    NTK-approximating MLP Fusion for Efficient Language Model Fine-tuning. (arXiv:2307.08941v1 [cs.LG])
    Fine-tuning a pre-trained language model (PLM) emerges as the predominant strategy in many natural language processing applications. However, even fine-tuning the PLMs and doing inference are expensive, especially on edge devices with low computing power. Some general approaches (e.g. quantization and distillation) have been widely studied to reduce the compute/memory of PLM fine-tuning, while very few one-shot compression techniques are explored. In this paper, we investigate the neural tangent kernel (NTK)--which reveals the gradient descent dynamics of neural networks--of the multilayer perceptrons (MLP) modules in a PLM and propose to coin a lightweight PLM through NTK-approximating MLP fusion. To achieve this, we reconsider the MLP as a bundle of sub-MLPs, and cluster them into a given number of centroids, which can then be restored as a compressed MLP and surprisingly shown to well approximate the NTK of the original PLM. Extensive experiments of PLM fine-tuning on both natural language understanding (NLU) and generation (NLG) tasks are provided to verify the effectiveness of the proposed method MLP fusion. Our code is available at https://github.com/weitianxin/MLP_Fusion.  ( 2 min )
    Modular Neural Network Approaches for Surgical Image Recognition. (arXiv:2307.08880v1 [cs.CV])
    Deep learning-based applications have seen a lot of success in recent years. Text, audio, image, and video have all been explored with great success using deep learning approaches. The use of convolutional neural networks (CNN) in computer vision, in particular, has yielded reliable results. In order to achieve these results, a large amount of data is required. However, the dataset cannot always be accessible. Moreover, annotating data can be difficult and time-consuming. Self-training is a semi-supervised approach that managed to alleviate this problem and achieve state-of-the-art performances. Theoretical analysis even proved that it may result in a better generalization than a normal classifier. Another problem neural networks can face is the increasing complexity of modern problems, requiring a high computational and storage cost. One way to mitigate this issue, a strategy that has been inspired by human cognition known as modular learning, can be employed. The principle of the approach is to decompose a complex problem into simpler sub-tasks. This approach has several advantages, including faster learning, better generalization, and enables interpretability. In the first part of this paper, we introduce and evaluate different architectures of modular learning for Dorsal Capsulo-Scapholunate Septum (DCSS) instability classification. Our experiments have shown that modular learning improves performances compared to non-modular systems. Moreover, we found that weighted modular, that is to weight the output using the probabilities from the gating module, achieved an almost perfect classification. In the second part, we present our approach for data labeling and segmentation with self-training applied on shoulder arthroscopy images.  ( 3 min )
    The Predicted-Deletion Dynamic Model: Taking Advantage of ML Predictions, for Free. (arXiv:2307.08890v1 [cs.DS])
    The main bottleneck in designing efficient dynamic algorithms is the unknown nature of the update sequence. In particular, there are some problems, like 3-vertex connectivity, planar digraph all pairs shortest paths, and others, where the separation in runtime between the best partially dynamic solutions and the best fully dynamic solutions is polynomial, sometimes even exponential. In this paper, we formulate the predicted-deletion dynamic model, motivated by a recent line of empirical work about predicting edge updates in dynamic graphs. In this model, edges are inserted and deleted online, and when an edge is inserted, it is accompanied by a "prediction" of its deletion time. This models real world settings where services may have access to historical data or other information about an input and can subsequently use such information make predictions about user behavior. The model is also of theoretical interest, as it interpolates between the partially dynamic and fully dynamic settings, and provides a natural extension of the algorithms with predictions paradigm to the dynamic setting. We give a novel framework for this model that "lifts" partially dynamic algorithms into the fully dynamic setting with little overhead. We use our framework to obtain improved efficiency bounds over the state-of-the-art dynamic algorithms for a variety of problems. In particular, we design algorithms that have amortized update time that scales with a partially dynamic algorithm, with high probability, when the predictions are of high quality. On the flip side, our algorithms do no worse than existing fully-dynamic algorithms when the predictions are of low quality. Furthermore, our algorithms exhibit a graceful trade-off between the two cases. Thus, we are able to take advantage of ML predictions asymptotically "for free.''  ( 3 min )
    Classification with Incoherent Kernel Dictionary Learning. (arXiv:2307.08796v1 [cs.LG])
    In this paper we present a new classification method based on Dictionary Learning (DL). The main contribution consists of a kernel version of incoherent DL, derived from its standard linear counterpart. We also propose an improvement of the AK-SVD algorithm concerning the representation update. Our algorithms are tested on several popular databases of classification problems.  ( 2 min )
    A Meta-Learning Based Precoder Optimization Framework for Rate-Splitting Multiple Access. (arXiv:2307.08822v1 [eess.SP])
    In this letter, we propose the use of a meta-learning based precoder optimization framework to directly optimize the Rate-Splitting Multiple Access (RSMA) precoders with partial Channel State Information at the Transmitter (CSIT). By exploiting the overfitting of the compact neural network to maximize the explicit Average Sum-Rate (ASR) expression, we effectively bypass the need for any other training data while minimizing the total running time. Numerical results reveal that the meta-learning based solution achieves similar ASR performance to conventional precoder optimization in medium-scale scenarios, and significantly outperforms sub-optimal low complexity precoder algorithms in the large-scale regime.  ( 2 min )
    Natural Actor-Critic for Robust Reinforcement Learning with Function Approximation. (arXiv:2307.08875v1 [cs.LG])
    We study robust reinforcement learning (RL) with the goal of determining a well-performing policy that is robust against model mismatch between the training simulator and the testing environment. Previous policy-based robust RL algorithms mainly focus on the tabular setting under uncertainty sets that facilitate robust policy evaluation, but are no longer tractable when the number of states scales up. To this end, we propose two novel uncertainty set formulations, one based on double sampling and the other on an integral probability metric. Both make large-scale robust RL tractable even when one only has access to a simulator. We propose a robust natural actor-critic (RNAC) approach that incorporates the new uncertainty sets and employs function approximation. We provide finite-time convergence guarantees for the proposed RNAC algorithm to the optimal robust policy within the function approximation error. Finally, we demonstrate the robust performance of the policy learned by our proposed RNAC approach in multiple MuJoCo environments and a real-world TurtleBot navigation task.  ( 2 min )
    IxDRL: A Novel Explainable Deep Reinforcement Learning Toolkit based on Analyses of Interestingness. (arXiv:2307.08933v1 [cs.AI])
    In recent years, advances in deep learning have resulted in a plethora of successes in the use of reinforcement learning (RL) to solve complex sequential decision tasks with high-dimensional inputs. However, existing systems lack the necessary mechanisms to provide humans with a holistic view of their competence, presenting an impediment to their adoption, particularly in critical applications where the decisions an agent makes can have significant consequences. Yet, existing RL-based systems are essentially competency-unaware in that they lack the necessary interpretation mechanisms to allow human operators to have an insightful, holistic view of their competency. Towards more explainable Deep RL (xDRL), we propose a new framework based on analyses of interestingness. Our tool provides various measures of RL agent competence stemming from interestingness analysis and is applicable to a wide range of RL algorithms, natively supporting the popular RLLib toolkit. We showcase the use of our framework by applying the proposed pipeline in a set of scenarios of varying complexity. We empirically assess the capability of the approach in identifying agent behavior patterns and competency-controlling conditions, and the task elements mostly responsible for an agent's competence, based on global and local analyses of interestingness. Overall, we show that our framework can provide agent designers with insights about RL agent competence, both their capabilities and limitations, enabling more informed decisions about interventions, additional training, and other interactions in collaborative human-machine settings.  ( 3 min )
    Towards the Sparseness of Projection Head in Self-Supervised Learning. (arXiv:2307.08913v1 [cs.LG])
    In recent years, self-supervised learning (SSL) has emerged as a promising approach for extracting valuable representations from unlabeled data. One successful SSL method is contrastive learning, which aims to bring positive examples closer while pushing negative examples apart. Many current contrastive learning approaches utilize a parameterized projection head. Through a combination of empirical analysis and theoretical investigation, we provide insights into the internal mechanisms of the projection head and its relationship with the phenomenon of dimensional collapse. Our findings demonstrate that the projection head enhances the quality of representations by performing contrastive loss in a projected subspace. Therefore, we propose an assumption that only a subset of features is necessary when minimizing the contrastive loss of a mini-batch of data. Theoretical analysis further suggests that a sparse projection head can enhance generalization, leading us to introduce SparseHead - a regularization term that effectively constrains the sparsity of the projection head, and can be seamlessly integrated with any self-supervised learning (SSL) approaches. Our experimental results validate the effectiveness of SparseHead, demonstrating its ability to improve the performance of existing contrastive methods.  ( 2 min )
    regulAS: A Bioinformatics Tool for the Integrative Analysis of Alternative Splicing Regulome using RNA-Seq data. (arXiv:2307.08800v1 [q-bio.GN])
    The regulAS software package is a bioinformatics tool designed to support computational biology researchers in investigating regulatory mechanisms of splicing alterations through integrative analysis of large-scale RNA-Seq data from cancer and healthy human donors, characterized by TCGA and GTEx projects. This technical report provides a comprehensive overview of regulAS, focusing on its core functionality, basic modules, experiment configuration, further extensibility and customisation. The core functionality of regulAS enables the automation of computational experiments, efficient results storage and processing, and streamlined workflow management. Integrated basic modules extend regulAS with features such as RNA-Seq data retrieval from the public multi-omics UCSC Xena data repository, predictive modeling and feature ranking capabilities using the scikit-learn package, and flexible reporting generation for analysing gene expression profiles and relevant modulations of alternative splicing aberrations across tissues and cancer types. Experiment configuration is handled through YAML files with the Hydra and OmegaConf libraries, offering a user-friendly approach. Additionally, regulAS allows for the development and integration of custom modules to handle specialized tasks. In conclusion, regulAS provides an automated solution for alternative splicing and cancer biology studies, enhancing efficiency, reproducibility, and customization of experimental design, while the extensibility of the pipeline enables researchers to further tailor the software package to their specific needs. Source code is available under the MIT license at https://github.com/slipnitskaya/regulAS.  ( 2 min )
    Examining the Effects of Degree Distribution and Homophily in Graph Learning Models. (arXiv:2307.08881v1 [cs.SI])
    Despite a surge in interest in GNN development, homogeneity in benchmarking datasets still presents a fundamental issue to GNN research. GraphWorld is a recent solution which uses the Stochastic Block Model (SBM) to generate diverse populations of synthetic graphs for benchmarking any GNN task. Despite its success, the SBM imposed fundamental limitations on the kinds of graph structure GraphWorld could create. In this work we examine how two additional synthetic graph generators can improve GraphWorld's evaluation; LFR, a well-established model in the graph clustering literature and CABAM, a recent adaptation of the Barabasi-Albert model tailored for GNN benchmarking. By integrating these generators, we significantly expand the coverage of graph space within the GraphWorld framework while preserving key graph properties observed in real-world networks. To demonstrate their effectiveness, we generate 300,000 graphs to benchmark 11 GNN models on a node classification task. We find GNN performance variations in response to homophily, degree distribution and feature signal. Based on these findings, we classify models by their sensitivity to the new generators under these properties. Additionally, we release the extensions made to GraphWorld on the GitHub repository, offering further evaluation of GNN performance on new graphs.  ( 2 min )
    Sharpness-Aware Graph Collaborative Filtering. (arXiv:2307.08910v1 [cs.LG])
    Graph Neural Networks (GNNs) have achieved impressive performance in collaborative filtering. However, GNNs tend to yield inferior performance when the distributions of training and test data are not aligned well. Also, training GNNs requires optimizing non-convex neural networks with an abundance of local and global minima, which may differ widely in their performance at test time. Thus, it is essential to choose the minima carefully. Here we propose an effective training schema, called {gSAM}, under the principle that the \textit{flatter} minima has a better generalization ability than the \textit{sharper} ones. To achieve this goal, gSAM regularizes the flatness of the weight loss landscape by forming a bi-level optimization: the outer problem conducts the standard model training while the inner problem helps the model jump out of the sharp minima. Experimental results show the superiority of our gSAM.  ( 2 min )
    Autoregressive Diffusion Model for Graph Generation. (arXiv:2307.08849v1 [cs.AI])
    Diffusion-based graph generative models have recently obtained promising results for graph generation. However, existing diffusion-based graph generative models are mostly one-shot generative models that apply Gaussian diffusion in the dequantized adjacency matrix space. Such a strategy can suffer from difficulty in model training, slow sampling speed, and incapability of incorporating constraints. We propose an \emph{autoregressive diffusion} model for graph generation. Unlike existing methods, we define a node-absorbing diffusion process that operates directly in the discrete graph space. For forward diffusion, we design a \emph{diffusion ordering network}, which learns a data-dependent node absorbing ordering from graph topology. For reverse generation, we design a \emph{denoising network} that uses the reverse node ordering to efficiently reconstruct the graph by predicting the node type of the new node and its edges with previously denoised nodes at a time. Based on the permutation invariance of graph, we show that the two networks can be jointly trained by optimizing a simple lower bound of data likelihood. Our experiments on six diverse generic graph datasets and two molecule datasets show that our model achieves better or comparable generation performance with previous state-of-the-art, and meanwhile enjoys fast generation speed.  ( 2 min )
    Latent Space Representations of Neural Algorithmic Reasoners. (arXiv:2307.08874v1 [cs.LG])
    Neural Algorithmic Reasoning (NAR) is a research area focused on designing neural architectures that can reliably capture classical computation, usually by learning to execute algorithms. A typical approach is to rely on Graph Neural Network (GNN) architectures, which encode inputs in high-dimensional latent spaces that are repeatedly transformed during the execution of the algorithm. In this work we perform a detailed analysis of the structure of the latent space induced by the GNN when executing algorithms. We identify two possible failure modes: (i) loss of resolution, making it hard to distinguish similar values; (ii) inability to deal with values outside the range observed during training. We propose to solve the first issue by relying on a softmax aggregator, and propose to decay the latent space in order to deal with out-of-range values. We show that these changes lead to improvements on the majority of algorithms in the standard CLRS-30 benchmark when using the state-of-the-art Triplet-GMPNN processor. Our code is available at \href{https://github.com/mirjanic/nar-latent-spaces}{https://github.com/mirjanic/nar-latent-spaces}.  ( 2 min )
    Federated Large Language Model: A Position Paper. (arXiv:2307.08925v1 [cs.LG])
    Large scale language models (LLM) have received significant attention and found diverse applications across various domains, but their development encounters challenges in real-world scenarios. These challenges arise due to the scarcity of public domain data availability and the need to maintain privacy with respect to private domain data. To address these issues, federated learning (FL) has emerged as a promising technology that enables collaborative training of shared models while preserving decentralized data. We propose the concept of federated LLM, which comprises three key components, i.e., federated LLM pre-training, federated LLM fine-tuning, and federated LLM prompt engineering. For each component, we discuss its advantage over traditional LLM training methods and propose specific engineering strategies for implementation. Furthermore, we explore the novel challenges introduced by the integration of FL and LLM. We analyze existing solutions and identify potential obstacles faced by these solutions within the context of federated LLM.  ( 2 min )
    Disentangling Node Attributes from Graph Topology for Improved Generalizability in Link Prediction. (arXiv:2307.08877v1 [cs.LG])
    Link prediction is a crucial task in graph machine learning with diverse applications. We explore the interplay between node attributes and graph topology and demonstrate that incorporating pre-trained node attributes improves the generalization power of link prediction models. Our proposed method, UPNA (Unsupervised Pre-training of Node Attributes), solves the inductive link prediction problem by learning a function that takes a pair of node attributes and predicts the probability of an edge, as opposed to Graph Neural Networks (GNN), which can be prone to topological shortcuts in graphs with power-law degree distribution. In this manner, UPNA learns a significant part of the latent graph generation mechanism since the learned function can be used to add incoming nodes to a growing graph. By leveraging pre-trained node attributes, we overcome observational bias and make meaningful predictions about unobserved nodes, surpassing state-of-the-art performance (3X to 34X improvement on benchmark datasets). UPNA can be applied to various pairwise learning tasks and integrated with existing link prediction models to enhance their generalizability and bolster graph generative models.  ( 2 min )
    Multi-stage Neural Networks: Function Approximator of Machine Precision. (arXiv:2307.08934v1 [cs.LG])
    Deep learning techniques are increasingly applied to scientific problems, where the precision of networks is crucial. Despite being deemed as universal function approximators, neural networks, in practice, struggle to reduce the prediction errors below $O(10^{-5})$ even with large network size and extended training iterations. To address this issue, we developed the multi-stage neural networks that divides the training process into different stages, with each stage using a new network that is optimized to fit the residue from the previous stage. Across successive stages, the residue magnitudes decreases substantially and follows an inverse power-law relationship with the residue frequencies. The multi-stage neural networks effectively mitigate the spectral biases associated with regular neural networks, enabling them to capture the high frequency feature of target functions. We demonstrate that the prediction error from the multi-stage training for both regression problems and physics-informed neural networks can nearly reach the machine-precision $O(10^{-16})$ of double-floating point within a finite number of iterations. Such levels of accuracy are rarely attainable using single neural networks alone.  ( 2 min )
    A mixed policy to improve performance of language models on math problems. (arXiv:2307.08767v1 [cs.CL])
    When to solve math problems, most language models take a sampling strategy to predict next word according conditional probabilities. In the math reasoning step, it may generate wrong answer. Considering math problems are deterministic, we propose a mixed policy exploration approach to solve math problems with reinforcement learning. In peculiar, we propose a two level token exploration policy: the abstract level explores next token with probability and the second level is deterministic. Specifically, the abstract level policy will decide whether the token is operator or operand with probability sampling, while the second level is deterministic to select next token with the highest score in a greedy way. We test our method on GSM8K dataset with GPT-2 model, and demonstrate more than $2\%$ performance gain. Our implementation is available at https://github.com/vividitytech/math_lm_rl.  ( 2 min )
    Learning to Sample Tasks for Meta Learning. (arXiv:2307.08924v1 [cs.LG])
    Through experiments on various meta-learning methods, task samplers, and few-shot learning tasks, this paper arrives at three conclusions. Firstly, there are no universal task sampling strategies to guarantee the performance of meta-learning models. Secondly, task diversity can cause the models to either underfit or overfit during training. Lastly, the generalization performance of the models are influenced by task divergence, task entropy, and task difficulty. In response to these findings, we propose a novel task sampler called Adaptive Sampler (ASr). ASr is a plug-and-play task sampler that takes task divergence, task entropy, and task difficulty to sample tasks. To optimize ASr, we rethink and propose a simple and general meta-learning algorithm. Finally, a large number of empirical experiments demonstrate the effectiveness of the proposed ASr.  ( 2 min )
  • Open

    Batched Predictors Generalize within Distribution. (arXiv:2307.09379v1 [stat.ML])
    We study the generalization properties of batched predictors, i.e., models tasked with predicting the mean label of a small set (or batch) of examples. The batched prediction paradigm is particularly relevant for models deployed to determine the quality of a group of compounds in preparation for offline testing. By utilizing a suitable generalization of the Rademacher complexity, we prove that batched predictors come with exponentially stronger generalization guarantees as compared to the standard per-sample approach. Surprisingly, the proposed bound holds independently of overparametrization. Our theoretical insights are validated experimentally for various tasks, architectures, and applications.  ( 2 min )
    Optimistic Estimate Uncovers the Potential of Nonlinear Models. (arXiv:2307.08921v1 [cs.LG])
    We propose an optimistic estimate to evaluate the best possible fitting performance of nonlinear models. It yields an optimistic sample size that quantifies the smallest possible sample size to fit/recover a target function using a nonlinear model. We estimate the optimistic sample sizes for matrix factorization models, deep models, and deep neural networks (DNNs) with fully-connected or convolutional architecture. For each nonlinear model, our estimates predict a specific subset of targets that can be fitted at overparameterization, which are confirmed by our experiments. Our optimistic estimate reveals two special properties of the DNN models -- free expressiveness in width and costly expressiveness in connection. These properties suggest the following architecture design principles of DNNs: (i) feel free to add neurons/kernels; (ii) restrain from connecting neurons. Overall, our optimistic estimate theoretically unveils the vast potential of nonlinear models in fitting at overparameterization. Based on this framework, we anticipate gaining a deeper understanding of how and why numerous nonlinear models such as DNNs can effectively realize their potential in practice in the near future.  ( 2 min )
    Evaluating unsupervised disentangled representation learning for genomic discovery and disease risk prediction. (arXiv:2307.08893v1 [cs.LG])
    High-dimensional clinical data have become invaluable resources for genetic studies, due to their accessibility in biobank-scale datasets and the development of high performance modeling techniques especially using deep learning. Recent work has shown that low dimensional embeddings of these clinical data learned by variational autoencoders (VAE) can be used for genome-wide association studies and polygenic risk prediction. In this work, we consider multiple unsupervised learning methods for learning disentangled representations, namely autoencoders, VAE, beta-VAE, and FactorVAE, in the context of genetic association studies. Using spirograms from UK Biobank as a running example, we observed improvements in the number of genome-wide significant loci, heritability, and performance of polygenic risk scores for asthma and chronic obstructive pulmonary disease by using FactorVAE or beta-VAE, compared to standard VAE or non-variational autoencoders. FactorVAEs performed effectively across multiple values of the regularization hyperparameter, while beta-VAEs were much more sensitive to the hyperparameter values.  ( 2 min )
    Adaptively Optimised Adaptive Importance Samplers. (arXiv:2307.09341v1 [stat.CO])
    We introduce a new class of adaptive importance samplers leveraging adaptive optimisation tools, which we term AdaOAIS. We build on Optimised Adaptive Importance Samplers (OAIS), a class of techniques that adapt proposals to improve the mean-squared error of the importance sampling estimators by parameterising the proposal and optimising the $\chi^2$-divergence between the target and the proposal. We show that a naive implementation of OAIS using stochastic gradient descent may lead to unstable estimators despite its convergence guarantees. To remedy this shortcoming, we instead propose to use adaptive optimisers (such as AdaGrad and Adam) to improve the stability of the OAIS. We provide convergence results for AdaOAIS in a similar manner to OAIS. We also provide empirical demonstration on a variety of examples and show that AdaOAIS lead to stable importance sampling estimators in practice.  ( 2 min )
    Latent Space Representations of Neural Algorithmic Reasoners. (arXiv:2307.08874v1 [cs.LG])
    Neural Algorithmic Reasoning (NAR) is a research area focused on designing neural architectures that can reliably capture classical computation, usually by learning to execute algorithms. A typical approach is to rely on Graph Neural Network (GNN) architectures, which encode inputs in high-dimensional latent spaces that are repeatedly transformed during the execution of the algorithm. In this work we perform a detailed analysis of the structure of the latent space induced by the GNN when executing algorithms. We identify two possible failure modes: (i) loss of resolution, making it hard to distinguish similar values; (ii) inability to deal with values outside the range observed during training. We propose to solve the first issue by relying on a softmax aggregator, and propose to decay the latent space in order to deal with out-of-range values. We show that these changes lead to improvements on the majority of algorithms in the standard CLRS-30 benchmark when using the state-of-the-art Triplet-GMPNN processor. Our code is available at \href{https://github.com/mirjanic/nar-latent-spaces}{https://github.com/mirjanic/nar-latent-spaces}.
    Towards Dynamic Causal Discovery with Rare Events: A Nonparametric Conditional Independence Test. (arXiv:2211.16596v5 [stat.ML] UPDATED)
    Causal phenomena associated with rare events occur across a wide range of engineering problems, such as risk-sensitive safety analysis, accident analysis and prevention, and extreme value theory. However, current methods for causal discovery are often unable to uncover causal links, between random variables in a dynamic setting, that manifest only when the variables first experience low-probability realizations. To address this issue, we introduce a novel statistical independence test on data collected from time-invariant dynamical systems in which rare but consequential events occur. In particular, we exploit the time-invariance of the underlying data to construct a superimposed dataset of the system state before rare events happen at different timesteps. We then design a conditional independence test on the reorganized data. We provide non-asymptotic sample complexity bounds for the consistency of our method, and validate its performance across various simulated and real-world datasets, including incident data collected from the Caltrans Performance Measurement System (PeMS). Code containing the datasets and experiments is publicly available.
    PAC Neural Prediction Set Learning to Quantify the Uncertainty of Generative Language Models. (arXiv:2307.09254v1 [cs.LG])
    Uncertainty learning and quantification of models are crucial tasks to enhance the trustworthiness of the models. Importantly, the recent surge of generative language models (GLMs) emphasizes the need for reliable uncertainty quantification due to the concerns on generating hallucinated facts. In this paper, we propose to learn neural prediction set models that comes with the probably approximately correct (PAC) guarantee for quantifying the uncertainty of GLMs. Unlike existing prediction set models, which are parameterized by a scalar value, we propose to parameterize prediction sets via neural networks, which achieves more precise uncertainty quantification but still satisfies the PAC guarantee. We demonstrate the efficacy of our method on four types of language datasets and six types of models by showing that our method improves the quantified uncertainty by $63\%$ on average, compared to a standard baseline method.
    Deep Riemannian Networks for EEG Decoding. (arXiv:2212.10426v5 [cs.LG] UPDATED)
    State-of-the-art performance in electroencephalography (EEG) decoding tasks is currently often achieved with either Deep-Learning (DL) or Riemannian-Geometry-based decoders (RBDs). Recently, there is growing interest in Deep Riemannian Networks (DRNs) possibly combining the advantages of both previous classes of methods. However, there are still a range of topics where additional insight is needed to pave the way for a more widespread application of DRNs in EEG. These include architecture design questions such as network size and end-to-end ability.How these factors affect model performance has not been explored. Additionally, it is not clear how the data within these networks is transformed, and whether this would correlate with traditional EEG decoding. Our study aims to lay the groundwork in the area of these topics through the analysis of DRNs for EEG with a wide range of hyperparameters. Networks were tested on two public EEG datasets and compared with state-of-the-art ConvNets. Here we propose end-to-end EEG SPDNet (EE(G)-SPDNet), and we show that this wide, end-to-end DRN can outperform the ConvNets, and in doing so use physiologically plausible frequency regions. We also show that the end-to-end approach learns more complex filters than traditional band-pass filters targeting the classical alpha, beta, and gamma frequency bands of the EEG, and that performance can benefit from channel specific filtering approaches. Additionally, architectural analysis revealed areas for further improvement due to the possible loss of Riemannian specific information throughout the network. Our study thus shows how to design and train DRNs to infer task-related information from the raw EEG without the need of handcrafted filterbanks and highlights the potential of end-to-end DRNs such as EE(G)-SPDNet for high-performance EEG decoding.
    Nested Elimination: A Simple Algorithm for Best-Item Identification from Choice-Based Feedback. (arXiv:2307.09295v1 [cs.LG])
    We study the problem of best-item identification from choice-based feedback. In this problem, a company sequentially and adaptively shows display sets to a population of customers and collects their choices. The objective is to identify the most preferred item with the least number of samples and at a high confidence level. We propose an elimination-based algorithm, namely Nested Elimination (NE), which is inspired by the nested structure implied by the information-theoretic lower bound. NE is simple in structure, easy to implement, and has a strong theoretical guarantee for sample complexity. Specifically, NE utilizes an innovative elimination criterion and circumvents the need to solve any complex combinatorial optimization problem. We provide an instance-specific and non-asymptotic bound on the expected sample complexity of NE. We also show NE achieves high-order worst-case asymptotic optimality. Finally, numerical experiments from both synthetic and real data corroborate our theoretical findings.
    Estimation of an Order Book Dependent Hawkes Process for Large Datasets. (arXiv:2307.09077v1 [q-fin.TR])
    A point process for event arrivals in high frequency trading is presented. The intensity is the product of a Hawkes process and high dimensional functions of covariates derived from the order book. Conditions for stationarity of the process are stated. An algorithm is presented to estimate the model even in the presence of billions of data points, possibly mapping covariates into a high dimensional space. The large sample size can be common for high frequency data applications using multiple liquid instruments. Convergence of the algorithm is shown, consistency results under weak conditions is established, and a test statistic to assess out of sample performance of different model specifications is suggested. The methodology is applied to the study of four stocks that trade on the New York Stock Exchange (NYSE). The out of sample testing procedure suggests that capturing the nonlinearity of the order book information adds value to the self exciting nature of high frequency trading events.
    A Covariate-Adjusted Homogeneity Test with Application to Facial Recognition Accuracy Assessment. (arXiv:2307.08846v1 [stat.AP])
    Ordinal scores occur commonly in medical imaging studies and in black-box forensic studies \citep{Phillips:2018}. To assess the accuracy of raters in the studies, one needs to estimate the receiver operating characteristic (ROC) curve while accounting for covariates of raters. In this paper, we propose a covariate-adjusted homogeneity test to determine differences in accuracy among multiple rater groups. We derived the theoretical results of the proposed test and conducted extensive simulation studies to evaluate the finite sample performance of the proposed test. Our proposed test is applied to a face recognition study to identify statistically significant differences among five participant groups.
    Globally solving the Gromov-Wasserstein problem for point clouds in low dimensional Euclidean spaces. (arXiv:2307.09057v1 [math.OC])
    This paper presents a framework for computing the Gromov-Wasserstein problem between two sets of points in low dimensional spaces, where the discrepancy is the squared Euclidean norm. The Gromov-Wasserstein problem is a generalization of the optimal transport problem that finds the assignment between two sets preserving pairwise distances as much as possible. This can be used to quantify the similarity between two formations or shapes, a common problem in AI and machine learning. The problem can be formulated as a Quadratic Assignment Problem (QAP), which is in general computationally intractable even for small problems. Our framework addresses this challenge by reformulating the QAP as an optimization problem with a low-dimensional domain, leveraging the fact that the problem can be expressed as a concave quadratic optimization problem with low rank. The method scales well with the number of points, and it can be used to find the global solution for large-scale problems with thousands of points. We compare the computational complexity of our approach with state-of-the-art methods on synthetic problems and apply it to a near-symmetrical problem which is of particular interest in computational biology.
    Martian time-series unraveled: A multi-scale nested approach with factorial variational autoencoders. (arXiv:2305.16189v2 [cs.LG] UPDATED)
    Unsupervised source separation involves unraveling an unknown set of source signals recorded through a mixing operator, with limited prior knowledge about the sources, and only access to a dataset of signal mixtures. This problem is inherently ill-posed and is further challenged by the variety of time-scales exhibited by sources in time series data. Existing methods typically rely on a preselected window size that limits their capacity to handle multi-scale sources. To address this issue, instead of operating in the time domain, we propose an unsupervised multi-scale clustering and source separation framework by leveraging wavelet scattering covariances that provide a low-dimensional representation of stochastic processes, capable of distinguishing between different non-Gaussian stochastic processes. Nested within this representation space, we develop a factorial Gaussian-mixture variational autoencoder that is trained to (1) probabilistically cluster sources at different time-scales and (2) independently sample scattering covariance representations associated with each cluster. Using samples from each cluster as prior information, we formulate source separation as an optimization problem in the wavelet scattering covariance representation space, resulting in separated sources in the time domain. When applied to seismic data recorded during the NASA InSight mission on Mars, our multi-scale nested approach proves to be a powerful tool for discriminating between sources varying greatly in time-scale, e.g., minute-long transient one-sided pulses (known as ``glitches'') and structured ambient noises resulting from atmospheric activities that typically last for tens of minutes. These results provide an opportunity to conduct further investigations into the isolated sources related to atmospheric-surface interactions, thermal relaxations, and other complex phenomena.
    Conditionally Calibrated Predictive Distributions by Probability-Probability Map: Application to Galaxy Redshift Estimation and Probabilistic Forecasting. (arXiv:2205.14568v4 [stat.ML] UPDATED)
    Uncertainty quantification is crucial for assessing the predictive ability of AI algorithms. Much research has been devoted to describing the predictive distribution (PD) $F(y|\mathbf{x})$ of a target variable $y \in \mathbb{R}$ given complex input features $\mathbf{x} \in \mathcal{X}$. However, off-the-shelf PDs (from, e.g., normalizing flows and Bayesian neural networks) often lack conditional calibration with the probability of occurrence of an event given input $\mathbf{x}$ being significantly different from the predicted probability. Current calibration methods do not fully assess and enforce conditionally calibrated PDs. Here we propose \texttt{Cal-PIT}, a method that addresses both PD diagnostics and recalibration by learning a single probability-probability map from calibration data. The key idea is to regress probability integral transform scores against $\mathbf{x}$. The estimated regression provides interpretable diagnostics of conditional coverage across the feature space. The same regression function morphs the misspecified PD to a re-calibrated PD for all $\mathbf{x}$. We benchmark our corrected prediction bands (a by-product of corrected PDs) against oracle bands and state-of-the-art predictive inference algorithms for synthetic data. We also provide results for two applications: (i) probabilistic nowcasting given sequences of satellite images, and (ii) conditional density estimation of galaxy distances given imaging data (so-called photometric redshift estimation). Our code is available as a Python package https://github.com/lee-group-cmu/Cal-PIT .
    Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees. (arXiv:2305.11997v2 [stat.ML] UPDATED)
    There is an emerging interest in generating robust counterfactual explanations that would remain valid if the model is updated or changed even slightly. Towards finding robust counterfactuals, existing literature often assumes that the original model $m$ and the new model $M$ are bounded in the parameter space, i.e., $\|\text{Params}(M){-}\text{Params}(m)\|{<}\Delta$. However, models can often change significantly in the parameter space with little to no change in their predictions or accuracy on the given dataset. In this work, we introduce a mathematical abstraction termed \emph{naturally-occurring} model change, which allows for arbitrary changes in the parameter space such that the change in predictions on points that lie on the data manifold is limited. Next, we propose a measure -- that we call \emph{Stability} -- to quantify the robustness of counterfactuals to potential model changes for differentiable models, e.g., neural networks. Our main contribution is to show that counterfactuals with sufficiently high value of \emph{Stability} as defined by our measure will remain valid after potential ``naturally-occurring'' model changes with high probability (leveraging concentration bounds for Lipschitz function of independent Gaussians). Since our quantification depends on the local Lipschitz constant around a data point which is not always available, we also examine practical relaxations of our proposed measure and demonstrate experimentally how they can be incorporated to find robust counterfactuals for neural networks that are close, realistic, and remain valid after potential model changes. This work also has interesting connections with model multiplicity, also known as, the Rashomon effect.
    Sparse Gaussian Graphical Models with Discrete Optimization: Computational and Statistical Perspectives. (arXiv:2307.09366v1 [cs.LG])
    We consider the problem of learning a sparse graph underlying an undirected Gaussian graphical model, a key problem in statistical machine learning. Given $n$ samples from a multivariate Gaussian distribution with $p$ variables, the goal is to estimate the $p \times p$ inverse covariance matrix (aka precision matrix), assuming it is sparse (i.e., has a few nonzero entries). We propose GraphL0BnB, a new estimator based on an $\ell_0$-penalized version of the pseudolikelihood function, while most earlier approaches are based on the $\ell_1$-relaxation. Our estimator can be formulated as a convex mixed integer program (MIP) which can be difficult to compute at scale using off-the-shelf commercial solvers. To solve the MIP, we propose a custom nonlinear branch-and-bound (BnB) framework that solves node relaxations with tailored first-order methods. As a by-product of our BnB framework, we propose large-scale solvers for obtaining good primal solutions that are of independent interest. We derive novel statistical guarantees (estimation and variable selection) for our estimator and discuss how our approach improves upon existing estimators. Our numerical experiments on real/synthetic datasets suggest that our method can solve, to near-optimality, problem instances with $p = 10^4$ -- corresponding to a symmetric matrix of size $p \times p$ with $p^2/2$ binary variables. We demonstrate the usefulness of GraphL0BnB versus various state-of-the-art approaches on a range of datasets.
    The Score-Difference Flow for Implicit Generative Modeling. (arXiv:2304.12906v2 [cs.LG] UPDATED)
    Implicit generative modeling (IGM) aims to produce samples of synthetic data matching the characteristics of a target data distribution. Recent work (e.g. score-matching networks, diffusion models) has approached the IGM problem from the perspective of pushing synthetic source data toward the target distribution via dynamical perturbations or flows in the ambient space. In this direction, we present the score difference (SD) between arbitrary target and source distributions as a flow that optimally reduces the Kullback-Leibler divergence between them while also solving the Schroedinger bridge problem. We apply the SD flow to convenient proxy distributions, which are aligned if and only if the original distributions are aligned. We demonstrate the formal equivalence of this formulation to denoising diffusion models under certain conditions. We also show that the training of generative adversarial networks includes a hidden data-optimization sub-problem, which induces the SD flow under certain choices of loss function when the discriminator is optimal. As a result, the SD flow provides a theoretical link between model classes that individually address the three challenges of the "generative modeling trilemma" -- high sample quality, mode coverage, and fast sampling -- thereby setting the stage for a unified approach.
    Non-stationary Delayed Combinatorial Semi-Bandit with Causally Related Rewards. (arXiv:2307.09093v1 [cs.LG])
    Sequential decision-making under uncertainty is often associated with long feedback delays. Such delays degrade the performance of the learning agent in identifying a subset of arms with the optimal collective reward in the long run. This problem becomes significantly challenging in a non-stationary environment with structural dependencies amongst the reward distributions associated with the arms. Therefore, besides adapting to delays and environmental changes, learning the causal relations alleviates the adverse effects of feedback delay on the decision-making process. We formalize the described setting as a non-stationary and delayed combinatorial semi-bandit problem with causally related rewards. We model the causal relations by a directed graph in a stationary structural equation model. The agent maximizes the long-term average payoff, defined as a linear function of the base arms' rewards. We develop a policy that learns the structural dependencies from delayed feedback and utilizes that to optimize the decision-making while adapting to drifts. We prove a regret bound for the performance of the proposed algorithm. Besides, we evaluate our method via numerical analysis using synthetic and real-world datasets to detect the regions that contribute the most to the spread of Covid-19 in Italy.
    Multi-Objective GFlowNets. (arXiv:2210.12765v2 [cs.LG] UPDATED)
    We study the problem of generating diverse candidates in the context of Multi-Objective Optimization. In many applications of machine learning such as drug discovery and material design, the goal is to generate candidates which simultaneously optimize a set of potentially conflicting objectives. Moreover, these objectives are often imperfect evaluations of some underlying property of interest, making it important to generate diverse candidates to have multiple options for expensive downstream evaluations. We propose Multi-Objective GFlowNets (MOGFNs), a novel method for generating diverse Pareto optimal solutions, based on GFlowNets. We introduce two variants of MOGFNs: MOGFN-PC, which models a family of independent sub-problems defined by a scalarization function, with reward-conditional GFlowNets, and MOGFN-AL, which solves a sequence of sub-problems defined by an acquisition function in an active learning loop. Our experiments on wide variety of synthetic and benchmark tasks demonstrate advantages of the proposed methods in terms of the Pareto performance and importantly, improved candidate diversity, which is the main contribution of this work.
    Outlier-Robust Tensor Low-Rank Representation for Data Clustering. (arXiv:2307.09055v1 [stat.ML])
    Low-rank tensor analysis has received widespread attention with many practical applications. However, the tensor data are often contaminated by outliers or sample-specific corruptions. How to recover the tensor data that are corrupted by outliers and perform data clustering remains a challenging problem. This paper develops an outlier-robust tensor low-rank representation (OR-TLRR) method for simultaneous outlier detection and tensor data clustering based on the tensor singular value decomposition (t-SVD) algebraic framework. It is motivated by the recently proposed tensor-tensor product induced by invertible linear transforms that satisfy certain conditions. For tensor observations with arbitrary outlier corruptions, OR-TLRR has provable performance guarantee for exactly recovering the row space of clean data and detecting outliers under mild conditions. Moreover, an extension of OR-TLRR is also proposed to handle the case when parts of the data are missing. Finally, extensive experimental results on both synthetic and real data demonstrate the effectiveness of the proposed algorithms.
    qecGPT: decoding Quantum Error-correcting Codes with Generative Pre-trained Transformers. (arXiv:2307.09025v1 [quant-ph])
    We propose a general framework for decoding quantum error-correcting codes with generative modeling. The model utilizes autoregressive neural networks, specifically Transformers, to learn the joint probability of logical operators and syndromes. This training is in an unsupervised way, without the need for labeled training data, and is thus referred to as pre-training. After the pre-training, the model can efficiently compute the likelihood of logical operators for any given syndrome, using maximum likelihood decoding. It can directly generate the most-likely logical operators with computational complexity $\mathcal O(2k)$ in the number of logical qubits $k$, which is significantly better than the conventional maximum likelihood decoding algorithms that require $\mathcal O(4^k)$ computation. Based on the pre-trained model, we further propose refinement to achieve more accurately the likelihood of logical operators for a given syndrome by directly sampling the stabilizer operators. We perform numerical experiments on stabilizer codes with small code distances, using both depolarizing error models and error models with correlated noise. The results show that our approach provides significantly better decoding accuracy than the minimum weight perfect matching and belief-propagation-based algorithms. Our framework is general and can be applied to any error model and quantum codes with different topologies such as surface codes and quantum LDPC codes. Furthermore, it leverages the parallelization capabilities of GPUs, enabling simultaneous decoding of a large number of syndromes. Our approach sheds light on the efficient and accurate decoding of quantum error-correcting codes using generative artificial intelligence and modern computational power.
    Unsupervised Embedding Quality Evaluation. (arXiv:2305.16562v2 [cs.LG] UPDATED)
    Unsupervised learning has recently significantly gained in popularity, especially with deep learning-based approaches. Despite numerous successes and approaching supervised-level performance on a variety of academic benchmarks, it is still hard to train and evaluate SSL models in practice due to the unsupervised nature of the problem. Even with networks trained in a supervised fashion, it is often unclear whether they will perform well when transferred to another domain. Past works are generally limited to assessing the amount of information contained in embeddings, which is most relevant for self-supervised learning of deep neural networks. This works chooses to follow a different approach: can we quantify how easy it is to linearly separate the data in a stable way? We survey the literature and uncover three methods that could be potentially used for evaluating quality of representations. We also introduce one novel method based on recent advances in understanding the high-dimensional geometric structure of self-supervised learning. We conduct extensive experiments and study the properties of these metrics and ones introduced in the previous work. Our results suggest that while there is no free lunch, there are metrics that can robustly estimate embedding quality in an unsupervised way.
    Limitations of Information-Theoretic Generalization Bounds for Gradient Descent Methods in Stochastic Convex Optimization. (arXiv:2212.13556v3 [cs.LG] UPDATED)
    To date, no "information-theoretic" frameworks for reasoning about generalization error have been shown to establish minimax rates for gradient descent in the setting of stochastic convex optimization. In this work, we consider the prospect of establishing such rates via several existing information-theoretic frameworks: input-output mutual information bounds, conditional mutual information bounds and variants, PAC-Bayes bounds, and recent conditional variants thereof. We prove that none of these bounds are able to establish minimax rates. We then consider a common tactic employed in studying gradient methods, whereby the final iterate is corrupted by Gaussian noise, producing a noisy "surrogate" algorithm. We prove that minimax rates cannot be established via the analysis of such surrogates. Our results suggest that new ideas are required to analyze gradient descent using information-theoretic techniques.
    Conformal Prediction Bands for Two-Dimensional Functional Time Series. (arXiv:2207.13656v2 [stat.ME] UPDATED)
    Time evolving surfaces can be modeled as two-dimensional Functional time series, exploiting the tools of Functional data analysis. Leveraging this approach, a forecasting framework for such complex data is developed. The main focus revolves around Conformal Prediction, a versatile nonparametric paradigm used to quantify uncertainty in prediction problems. Building upon recent variations of Conformal Prediction for Functional time series, a probabilistic forecasting scheme for two-dimensional functional time series is presented, while providing an extension of Functional Autoregressive Processes of order one to this setting. Estimation techniques for the latter process are introduced and their performance are compared in terms of the resulting prediction regions. Finally, the proposed forecasting procedure and the uncertainty quantification technique are applied to a real dataset, collecting daily observations of Sea Level Anomalies of the Black Sea
    Scaling Laws for Imitation Learning in NetHack. (arXiv:2307.09423v1 [cs.LG])
    Imitation Learning (IL) is one of the most widely used methods in machine learning. Yet, while powerful, many works find it is often not able to fully recover the underlying expert behavior. However, none of these works deeply investigate the role of scaling up the model and data size. Inspired by recent work in Natural Language Processing (NLP) where "scaling up" has resulted in increasingly more capable LLMs, we investigate whether carefully scaling up model and data size can bring similar improvements in the imitation learning setting. To demonstrate our findings, we focus on the game of NetHack, a challenging environment featuring procedural generation, stochasticity, long-term dependencies, and partial observability. We find IL loss and mean return scale smoothly with the compute budget and are strongly correlated, resulting in power laws for training compute-optimal IL agents with respect to model size and number of samples. We forecast and train several NetHack agents with IL and find they outperform prior state-of-the-art by at least 2x in all settings. Our work both demonstrates the scaling behavior of imitation learning in a challenging domain, as well as the viability of scaling up current approaches for increasingly capable agents in NetHack, a game that remains elusively hard for current AI systems.
    Best-of-three-worlds Analysis for Linear Bandits with Follow-the-regularized-leader Algorithm. (arXiv:2303.06825v2 [cs.LG] UPDATED)
    The linear bandit problem has been studied for many years in both stochastic and adversarial settings. Designing an algorithm that can optimize the environment without knowing the loss type attracts lots of interest. \citet{LeeLWZ021} propose an algorithm that actively detects the loss type and then switches between different algorithms specially designed for specific settings. However, such an approach requires meticulous designs to perform well in all environments. Follow-the-regularized-leader (FTRL) is another type of popular algorithm that can adapt to different environments. This algorithm is of simple design and the regret bounds are shown to be optimal in traditional multi-armed bandit problems compared with the detect-switch type. Designing an FTRL-type algorithm for linear bandits is an important question that has been open for a long time. In this paper, we prove that the FTRL algorithm with a negative entropy regularizer can achieve the best-of-three-world results for the linear bandit problem. Our regret bounds achieve the same or nearly the same order as the previous detect-switch type algorithm but with a much simpler algorithmic design.
    Oracle Efficient Online Multicalibration and Omniprediction. (arXiv:2307.08999v1 [cs.LG])
    A recent line of work has shown a surprising connection between multicalibration, a multi-group fairness notion, and omniprediction, a learning paradigm that provides simultaneous loss minimization guarantees for a large family of loss functions. Prior work studies omniprediction in the batch setting. We initiate the study of omniprediction in the online adversarial setting. Although there exist algorithms for obtaining notions of multicalibration in the online adversarial setting, unlike batch algorithms, they work only for small finite classes of benchmark functions $F$, because they require enumerating every function $f \in F$ at every round. In contrast, omniprediction is most interesting for learning theoretic hypothesis classes $F$, which are generally continuously large. We develop a new online multicalibration algorithm that is well defined for infinite benchmark classes $F$, and is oracle efficient (i.e. for any class $F$, the algorithm has the form of an efficient reduction to a no-regret learning algorithm for $F$). The result is the first efficient online omnipredictor -- an oracle efficient prediction algorithm that can be used to simultaneously obtain no regret guarantees to all Lipschitz convex loss functions. For the class $F$ of linear functions, we show how to make our algorithm efficient in the worst case. Also, we show upper and lower bounds on the extent to which our rates can be improved: our oracle efficient algorithm actually promises a stronger guarantee called swap-omniprediction, and we prove a lower bound showing that obtaining $O(\sqrt{T})$ bounds for swap-omniprediction is impossible in the online setting. On the other hand, we give a (non-oracle efficient) algorithm which can obtain the optimal $O(\sqrt{T})$ omniprediction bounds without going through multicalibration, giving an information theoretic separation between these two solution concepts.
    Scalable Coupling of Deep Learning with Logical Reasoning. (arXiv:2305.07617v2 [cs.AI] UPDATED)
    In the ongoing quest for hybridizing discrete reasoning with neural nets, there is an increasing interest in neural architectures that can learn how to solve discrete reasoning or optimization problems from natural inputs. In this paper, we introduce a scalable neural architecture and loss function dedicated to learning the constraints and criteria of NP-hard reasoning problems expressed as discrete Graphical Models. Our loss function solves one of the main limitations of Besag's pseudo-loglikelihood, enabling learning of high energies. We empirically show it is able to efficiently learn how to solve NP-hard reasoning problems from natural inputs as the symbolic, visual or many-solutions Sudoku problems as well as the energy optimization formulation of the protein design problem, providing data efficiency, interpretability, and \textit{a posteriori} control over predictions.
    Resource frugal optimizer for quantum machine learning. (arXiv:2211.04965v2 [quant-ph] UPDATED)
    Quantum-enhanced data science, also known as quantum machine learning (QML), is of growing interest as an application of near-term quantum computers. Variational QML algorithms have the potential to solve practical problems on real hardware, particularly when involving quantum data. However, training these algorithms can be challenging and calls for tailored optimization procedures. Specifically, QML applications can require a large shot-count overhead due to the large datasets involved. In this work, we advocate for simultaneous random sampling over both the dataset as well as the measurement operators that define the loss function. We consider a highly general loss function that encompasses many QML applications, and we show how to construct an unbiased estimator of its gradient. This allows us to propose a shot-frugal gradient descent optimizer called Refoqus (REsource Frugal Optimizer for QUantum Stochastic gradient descent). Our numerics indicate that Refoqus can save several orders of magnitude in shot cost, even relative to optimizers that sample over measurement operators alone.
    Conformal prediction under ambiguous ground truth. (arXiv:2307.09302v1 [cs.LG])
    In safety-critical classification tasks, conformal prediction allows to perform rigorous uncertainty quantification by providing confidence sets including the true class with a user-specified probability. This generally assumes the availability of a held-out calibration set with access to ground truth labels. Unfortunately, in many domains, such labels are difficult to obtain and usually approximated by aggregating expert opinions. In fact, this holds true for almost all datasets, including well-known ones such as CIFAR and ImageNet. Applying conformal prediction using such labels underestimates uncertainty. Indeed, when expert opinions are not resolvable, there is inherent ambiguity present in the labels. That is, we do not have ``crisp'', definitive ground truth labels and this uncertainty should be taken into account during calibration. In this paper, we develop a conformal prediction framework for such ambiguous ground truth settings which relies on an approximation of the underlying posterior distribution of labels given inputs. We demonstrate our methodology on synthetic and real datasets, including a case study of skin condition classification in dermatology.
    Nested stochastic block model for simultaneously clustering networks and nodes. (arXiv:2307.09210v1 [stat.ME])
    We introduce the nested stochastic block model (NSBM) to cluster a collection of networks while simultaneously detecting communities within each network. NSBM has several appealing features including the ability to work on unlabeled networks with potentially different node sets, the flexibility to model heterogeneous communities, and the means to automatically select the number of classes for the networks and the number of communities within each network. This is accomplished via a Bayesian model, with a novel application of the nested Dirichlet process (NDP) as a prior to jointly model the between-network and within-network clusters. The dependency introduced by the network data creates nontrivial challenges for the NDP, especially in the development of efficient samplers. For posterior inference, we propose several Markov chain Monte Carlo algorithms including a standard Gibbs sampler, a collapsed Gibbs sampler, and two blocked Gibbs samplers that ultimately return two levels of clustering labels from both within and across the networks. Extensive simulation studies are carried out which demonstrate that the model provides very accurate estimates of both levels of the clustering structure. We also apply our model to two social network datasets that cannot be analyzed using any previous method in the literature due to the anonymity of the nodes and the varying number of nodes in each network.

  • Open

    Enhance Amazon Lex with conversational FAQ features using LLMs
    Amazon Lex is a service that allows you to quickly and easily build conversational bots (“chatbots”), virtual agents, and interactive voice response (IVR) systems for applications such as Amazon Connect. Artificial intelligence (AI) and machine learning (ML) have been a focus for Amazon for over 20 years, and many of the capabilities that customers use […]  ( 10 min )
    Enhance Amazon Lex with LLMs and improve the FAQ experience using URL ingestion
    In today’s digital world, most consumers would rather find answers to their customer service questions on their own rather than taking the time to reach out to businesses and/or service providers. This blog post explores an innovative solution to build a question and answer chatbot in Amazon Lex that uses existing FAQs from your website. […]  ( 9 min )
    Build an email spam detector using Amazon SageMaker
    Spam emails, also known as junk mail, are sent to a large number of users at once and often contain scams, phishing content, or cryptic messages. Spam emails are sometimes sent manually by a human, but most often they are sent using a bot. Examples of spam emails include fake ads, chain emails, and impersonation […]  ( 6 min )
    Llama 2 foundation models from Meta are now available in Amazon SageMaker JumpStart
    Today, we are excited to announce that Llama 2 foundation models developed by Meta are available for customers through Amazon SageMaker JumpStart. The Llama 2 family of large language models (LLMs) is a collection of pre-trained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Fine-tuned LLMs, called Llama-2-chat, […]  ( 14 min )
  • Open

    [R] Adversarial Robust Deep Reinforcement Learning Requires Redefining Robustness
    https://ojs.aaai.org/index.php/AAAI/article/view/26009/25781 submitted by /u/ml_dnn [link] [comments]  ( 8 min )
    [P] We made Llama13b-v2-chat immediately available as an endpoint for developers
    Hey r/MachineLearning, we've released tools that make it easy to test LLaMa 2 and add it to your own app! Model playground here: https://llama2.ai Hosted chat API here: https://replicate.com/a16z-infra/llama13b-v2-chat If you want to just play with the model, llama2.ai is a very easy way to do it. So far, we’ve found the performance is similar to GPT-3.5 with far fewer parameters, especially for creative tasks and interactions. Developers can: * clone the chatbot app as a starting point (https://github.com/a16z-infra/llama2-chatbot) * use the Replicate endpoint directly (https://replicate.com/a16z-infra/llama13b-v2-chat) * or even deploy your own LLaMA v2 fine tune with Cog (https://github.com/a16z-infra/cog-llama-template) Please let us know what you use this for or if you have feedback! And thanks to all contributors to this model, Meta, Replicate, the Open Source community! submitted by /u/Prestigious-Elk7124 [link] [comments]  ( 9 min )
    [Discussion] Meta open sources llama-2 and tie up with MSFT
    https://about.fb.com/news/2023/07/llama-2/ https://ai.meta.com/llama/ submitted by /u/Electrical_Study_617 [link] [comments]  ( 8 min )
    [N] Llama 2 is here
    Looks like a better model than llama according to the benchmarks they posted. But the biggest difference is that its free even for commercial usage. https://ai.meta.com/resources/models-and-libraries/llama/ submitted by /u/timedacorn369 [link] [comments]  ( 8 min )
    [D] Data Intelligence VS Information Retrieval
    I have to choose one of the two elective for the next sem. My Questions are: What is Information Retrieval and Data Intelligence? Which is more useful according to industry Requirements? Which one should I take as someone who wants to pursue a career as a Machine Learning Engineer or a Data Scientist? submitted by /u/Ethan045627 [link] [comments]  ( 8 min )
    [R] Utilizing AMD GPUs with Unity ML-Agents
    Hello everyone, I've embarked on a project involving Unity's ML-Agents toolkit, and I've hit a roadblock regarding GPU utilization. My system is equipped with an AMD GPU, and I'm aware that most machine learning libraries and tools mainly support NVIDIA GPUs due to their compatibility with CUDA. Has anyone here successfully gotten ML Agents to work optimally with an AMD GPU? If not, are there any alternative methods or libraries you recommend that work well with AMD GPUs? So far, my attempts with TensorFlow and PyTorch have been met with limited success due to their restricted support for AMD GPUs. I've been exploring other potential options like PlaidML and OpenCL, but I'd love to get some input from this community. Any suggestions or resources on tackling this issue would be hugely appreciated. Thank you! submitted by /u/Low-Spray-249 [link] [comments]  ( 9 min )
    [R] Relating images to voltages to angle
    If this post content is something you are expert in and would like to work with me to accomplish these goals as part of my team I am able to compensate you. I am currently building my team. I have pictures of a 3d printed part that I have sequentially lit by different small light sources, each positioned at a known 3d location relative to the part. The lights are less than 2 meters from the part. Each light casts specific shadows on the part. I measure the size of the shadows and relate them to the angular direction to the origin of the light (2D bearing). My next prototype has photodiodes that I will use to measure the % of shading on each diode by photoexcitation as a voltage. I want to build a pattern recognition model to relate the two outputs to the incident angle of light. This is so in the future I can output the bearing direction towards a light source with an unknown 3d relative position via voltage, and be able to validate the voltage data from images. Please guide me towards a Machine Learnig platform or engine (for lack of me knowing a better term) that could take this data (% surface shading & voltage) as input and learn how to extract the 2d bearing (and more) from sensor to light source. Thanks submitted by /u/masterjebbi [link] [comments]  ( 9 min )
    London AI4Code meetup w/ Aaron Parisi (Google) on TALM: Tool Augmented Language Models (July 27th) [R]
    The AI4Code reading group is back with Aaron Parisi, Google researcher and lead author of TALM, a framework for augmenting language models with arbitrary tools. Free RSVP: https://lu.ma/mw5ppi46 Paper: https://arxiv.org/abs/2205.12255 🗓 July 27th (Thursday) at 17:00 GMT+1 📍 Zoom 👥 Members of the international AI4Code research community Key ideas - Modeling tool-use via a text-to-text interface - Applying an iterative self-play technique to bootstrap high performance on tasks with few tool-use labelled examples TALM consistently outperforms a non-augmented LM on both a knowledge task (NQ) and reasoning task (MathQA). The AI4Code meetup community consists of like-minded researchers from around the world that network, discuss and share their latest research on AI applications on source code. submitted by /u/dritsakon [link] [comments]  ( 9 min )
    Image Recognition at Scale? [D]
    What services/libraries could I use if I wanted to, say, upload 100+ images and ask it to identify what each image is of? I know that in Bard for example I can upload one image at a time and it'll idenitfy it for me, but I want to do this at scale. Anyone know of any python libraries or OCR services that I could use for this? submitted by /u/Groundbreaking-Owl-5 [link] [comments]  ( 8 min )
    [D] How to access Claude AI outside US and UK
    Anthropic, a company founded by former researchers from OpenAI, has recently introduced its upgraded chatbot, Claude 2. Claude 2 has arrived five months after the initial release of its predecessor, Claude, and brings notable improvements such as longer responses, more up-to-date information, faster speeds. One of Claude 2's standout features is its ability to process up to 100,000 tokens, equivalent to 75,000 words, in a single prompt. This is a significant improvement from Claude's previous limitation of 9,000 tokens. However, there is one problem with it, currently Claude AI chat is available in UK and US only. While it’s claimed that other regions are soon to follow, the exact timeline remains unclear. Though Anthropic Claude is easily accessible with a VPN. Here are quick steps how to access it if you’re not living in UK or US: ​ 1. Buy a VPN provider of your choice that has in UK or US servers (most VPNs will have them since these are the main markets for them). This r/vpn comparison table could help you decide which provider to choose and offers nice discounts for some providers; 2. Open VPN app; 3. Connect to US or UK server. For the best speed and user experience, it’s recommended to connect to a server from whichever country is closer to your current location; 4. Login/Sign-up on Claude AI webpage. You can successfully log in using your personal email address. Using Incognito mode on your browser might be required; 5. Enjoy your easy access to Claude AI despite not being located in US or UK! ​ Hope this helps someone, happy using! submitted by /u/ProfessionalSource0 [link] [comments]  ( 9 min )
    [D] anyone got code implementation for hyperdreambooth
    i'm looking for code implementation of https://hyperdreambooth.github.io/ it'd be amazing if anyone can point to a repo or something thankyou submitted by /u/SayNo2Tennis [link] [comments]  ( 8 min )
    Sex differences in ML [D]
    General question about population stratification in machine learning: If I am interested in the important features for disease prediction in women only, is it worth stratifying my sample to women-only? I.e do ML algorithms account for gender differences? I have men and women in the dataset but I am interested in a disease that seems to be diagnosed in women later than men. submitted by /u/Vegetable-Gazelle728 [link] [comments]  ( 8 min )
    [R] Retentive Network: A Successor to Transformer for Large Language Models
    Paper: https://arxiv.org/abs/2307.08621 Retentive Network: A Successor to Transformer for Large Language Models Yutao Sun, Li Dong, Shaohan Huang, Shuming Ma, Yuqing Xia, Jilong Xue, Jianyong Wang, Furu Wei In this work, we propose Retentive Network (RetNet) as a foundation architecture for large language models, simultaneously achieving training parallelism, low-cost inference, and good performance. We theoretically derive the connection between recurrence and attention. Then we propose the retention mechanism for sequence modeling, which supports three computation paradigms, i.e., parallel, recurrent, and chunkwise recurrent. Specifically, the parallel representation allows for training parallelism. The recurrent representation enables low-cost O(1) inference, which improves decodin…  ( 10 min )
    [D] Vector DB Basics: a Star Wars Example
    Amidst all of the stress of AI taking over, here's a light-hearted blog post on Vector DB basics including a Star Wars mini-example for you all to enjoy :) https://preview.redd.it/n79cv8hkzocb1.png?width=1920&format=png&auto=webp&s=984d955c7d4a0e93ce36ca909835d98b65d6ee2d submitted by /u/kazhdan_d [link] [comments]  ( 8 min )
    [D] Derivation of InfoNCE loss
    I've been reading the paper that introduced Contrastive Predictive Coding as well as the InfoNCE section on Lilian Weng's blog post on contrastive learning. After a while of staring and working, I can't figure out how the authors derived equation 5 in the paper. The farthest I get is finding that p(d=i|X, c_t) = 1/(1 + \sum_{j=1, j!=i}^N [p(x_j | c_t) \prod_{l=1, l \neq j \neq i}^N p(x_l)]), but the rest of the derivation is a mystery to me. Is there something super obvious I'm missing? submitted by /u/like_a_tensor [link] [comments]  ( 8 min )
    [Discussion] State of highly specialized, topic-specific LLMS?
    Yesterday, I thought about why current conversational LLMs like ChatGPT are always so general. For example, I'm mostly working on Reinforcement Learning problems and would expect a model that is specifically fine-tuned on literature exclusively concerned with RL to give much better answers and more intricate details. ​ Are there any papers or blog posts about this? submitted by /u/seawee1 [link] [comments]  ( 8 min )
    [Research] Using official implementations vs highly popular unofficial implementation for research
    So for the past six months I have been working on a domain adaptation research problem. I wanted to inspect/understand the inherent capability of SSL methods to extract domain invariant features. For this purpose I have been conducting different kinds of experiments.There is a very nice library called lightly that contains the implementations of all published SSL methods, This made things very easy for me in terms of writing code. I am not a PhD student or don't have significant research experience. My guide/mentor is very interested in the work I'm doing and she aims to publish our work in somewhere like a NeurIPS, ICML or so. Probably because of my lack of experience, I am overlooking into things or I am genuinely concerned. I just don't want to make stupid coding or code related errors and report wrong results. I just want to know if its mandatory to use the official implementations of every method I'm benchmarking.or example, SimCLR's official implementation is in Tensorflow and I am using PyTorch. Using official implementation would introduce these kind of bottlenecks and slow down my experimentation process. Any advices on this would be greatly appreciated. Thanks. submitted by /u/ashharsha [link] [comments]  ( 9 min )
    [D]💥 How Underdog AI Companies Will Crush Silicon Valley Giants.
    💥 How Underdog AI Companies Will Crush Silicon Valley Giants. Opportunities in AI: Creating Abundant Intelligence. Generative AI like ChatGPT brings complex tasks within reach and is set to transform society. Startups have an opportunity in applying AI to create "abundant intelligence". In the past year, ChatGPT, GitHub Copilot, and Midjourney have rapidly grown to $100M+ revenue. AI startups face competition from tech giants also moving quickly into AI. Startups must pick spots where they have an advantage. Opportunities exist in expanding the application universe into new greenfield opportunities like automating mundane decisions, masking workflow complexity, and reimagining applications. Infrastructure tools make models more powerful by chaining them together and improving accuracy. Opportunity areas include unstructured data management, agent-driven automation, model evaluation, and experimentation. Key players emerging are foundation model providers like OpenAI and Anthropic, companies building domain-specific models, and platforms for autonomous agents. Advantages exist for startups focused on imagination and technical ability to find non-obvious ideas, while large companies retrofit existing businesses. submitted by /u/Yavero [link] [comments]  ( 9 min )
    [R] Semantic-SAM: Reproduce and Beyond SAM with Semantic-Aware and Granualrity-Abundance
    We introduce Semantic-SAM, a universal image segmentation model to enable segment and recognize anything at any desired granularity. We have trained on the whole SA-1B dataset and our model can reproduce SAM and beyond it. Training and inference code is available! 🔥code & demo link: https://github.com/UX-Decoder/Semantic-SAM 🔥paper link: https://arxiv.org/pdf/2307.04767.pdf 🚀 Features 🔥 Reproduce SAM. SAM training is a sub-task of ours. We have released the training code to reproduce SAM training. 🔥 Beyond SAM. Our newly proposed model offers the following attributes from instance to part level: Granularity Abundance. Our model can produce all possible segmentation granularities for a user click with high quality, which enables more controllable and user-friendly interactive s…  ( 9 min )
  • Open

    What phenomena are hyperparameters supposed to capture?
    Suppose you have an IMU and therefore no way to track velocity (reliably). In simulation you can train with velocity in any way you like. In this case, what is velocity in this context. Can it be used in the reward function as a form of privileged info or is it a hyperparameter (and needs an outer loop for optimization)? This is just an example of a problem for sim-2-real but that question applies generally for hyperparameters in terms of the objective. submitted by /u/FriendlyStandard5985 [link] [comments]  ( 8 min )
    Intro to Vanilla Policy Gradient
    I've written a series of blog posts going into the theory behind the policy gradient algorithm. Anyone who's starting out in RL may find them to be a good introduction! If you want to understand PPO and various actor critic algorithms, this is the place to start. https://kjabon.github.io/blog/2023/VPG/ ​ Let me know if you spot any issues or have any questions. (You can also comment on the post itself, I'll see it). submitted by /u/kjabon [link] [comments]  ( 8 min )
    RL applications
    So I am aware of applications of RL in games and robotics, as well as applications of contextual bandits for recommender systems. But as I look for possible future research paths in RL, I was wondering if there were any other interesting applications of the field. For instance, I recently learned about RL in procedural content generation. I’m particularly interested in more accessible/less resource heavy ones, though I would be glad to learn about all of them. Any insight and resources on this topic will be greatly appreciated. submitted by /u/Ok_Signature_4944 [link] [comments]  ( 8 min )
    "GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models", Agarwal et al 2023
    submitted by /u/gwern [link] [comments]  ( 8 min )
    Question about Montezuma's Revenge gym Atari environment
    Hi all, ​ I'm running some code (this code, in case anyone's curious) training an agent to learn in Montezuma'sRevengev4NoFrameskip environment, and it seems to be working, but the nonzero rewards seemingly being returned by the environment are always 1, rather than the "100" or "1000" points that are supposedly returned by the game. I'd like to change this so I can compare to SOTA benchmarks, which seem to use the actual game score, but also because I want to make sure this isn't a bug or anything. As far as I can tell, the reward of "1" is coming from the environment itself, and not from the code I linked converting any nonzero reward to a 1, but I can't find anything stating that in the documentation I can find, and might be missing something. Does anyone else have more experience with this environment that could tell me what's causing this/is it normal? submitted by /u/LessPoliticalAccount [link] [comments]  ( 9 min )
    Looking for assembly game environments
    Hello, I am really impressed by the real-life applications of Alphadev. I would like to experiment with an assembly game myself, but to the my best knowledge, it appears that there is no representative environment available. Is there an assembly game environment that you would recommend for reinforcement learning experiments? submitted by /u/Spiritual_Fig3632 [link] [comments]  ( 8 min )
    Looking for assembly game environments
    Hello, I am really impressed by the real-life applications of Alphadev. I would like to experiment with an assembly game myself, but to the my best knowledge, it appears that there is no representative environment available. Is there an assembly game environment that you would recommend for reinforcement learning experiments? submitted by /u/Spiritual_Fig3632 [link] [comments]  ( 8 min )
    For OpenAI Humanoid-v4: is 20000 score (average of last 250 episodes) within 3800 episodes good score for offline RL?
    Do I need to register it somewhere? If people got more than that, ok no multi-agents log: https://preview.redd.it/jf91dab7encb1.png?width=720&format=png&auto=webp&s=89d737fc1f30a6f9ef96575e18e0b8993ba683fd submitted by /u/Timur_1988 [link] [comments]  ( 8 min )
    Help
    Hi, I was implementing actor critic algorithm and while running it on cartpole environment, I noticed that if i repeat the same experiment, I would get the exact same results(overlapping plots of actor/critic loss, average return etc). Is it possible as the initialisation should be different for each run? Maybe because the environment is not stochastic? submitted by /u/Interesting-Weeb-699 [link] [comments]  ( 8 min )
    "AlpaGasus: Training A Better Alpaca with Fewer Data", Chen et al 2023 {Samsung}
    submitted by /u/gwern [link] [comments]  ( 8 min )
    Multi agent reinforcement learning - help wanted
    Hi guys, thank you in advance to who's going to answer. I'm researching MARL and drones swarms for my master thesis. Drones should navigate in a map, avoiding obstacles and finding a target, just using an RGB camera. If a drone collides/reaches objective, must stop but the episode will conclude when all of them finish. I had successfully implemented a single drone env using Microsoft's AirSim, which converges in less than 100k steps using SB3's PPO. Anyway, I need to do the same for a multiagent env. I tried a multitude of frameworks, RLlib (which didn't work well), MARLlib (got a successful implementation, but didn't like it and didn't have much results) and now I'm using SB3+PettingZoo ParallelEnv+SuperSuit. I can easily train the env, but after 1 million steps I still do not get any improvement (see attached pic): some problems are that evaluation episodes sometimes end before all the drones collide/reach objective; I had to modify SuperSuit package because didn't really support well black death on Markov wrapper (when drone is not active, his camera observation is all 0s and actions are not given); evaluation seems to behave differently than training (actions seem "smoothed", almost 0, in particular at the first evaluations episodes); drones seem to behave better (reach easily objective) if all the others collided. If any of you are interested, I can attach some code. I had to heavily modify the overrid step function of the Parallel env to support training on active agents only (possible_agents variable). I was inspired by this stack overflow: https://stackoverflow.com/questions/73111772/problem-with-pettingzoo-and-stable-baselines3-with-a-parallelenv If you have any advice, any different framework to try (I should try Tianshou's), please tell me. Any help is greatly appreciated. Thank you all. submitted by /u/IntelligentAd6407 [link] [comments]  ( 9 min )
  • Open

    Personal Assistant AIs?
    What does the market look like for personal assistant AIs? I was looking at trying to code one for myself or try to get a group of my coding friends to help make one up so we can use it for ourselves to make our lives easier. Not sure if this really exists now though. submitted by /u/derpgod123 [link] [comments]  ( 8 min )
    What is the best free AI picture generator available?
    Preferably something that allows NSFW requests. submitted by /u/Ancient_Challenge173 [link] [comments]  ( 8 min )
    Meta/Facebook just released Llama2
    submitted by /u/swierdo [link] [comments]  ( 8 min )
    Google bard uses Deviantart, Quora, Reddit as source for it's opinions
    submitted by /u/TruestNestor [link] [comments]  ( 8 min )
    Microsoft Inspire AI megathread!
    Starting now submitted by /u/jaketocake [link] [comments]  ( 8 min )
    Here's What Barbie Would Look Like In Every Country
    submitted by /u/World-Tight [link] [comments]  ( 8 min )
    Open source project to generate speech from text with emotion. Are there any?
    I forget what the technical term is, but there are professional tools that can adjust tone/pressure of the speech to generate an emotional voice. Imagine a guy having to stop playing xbox because dinner is ready and he goes “but moooOoOoom, I’m in the middle of a quest. Hhhhng”. I want to be able to generate that. I don’t mind adding annotations for this but I have yet to find a usable open source project that does this. Are there any? (Oh yeah, and I want to make sexy talk. So it’s for porn, it was always about porn. kudos if you’ve guessed it already). submitted by /u/kjarkr [link] [comments]  ( 8 min )
    Mind-Blowing Midjourney Animated Audiobook
    Just wanted to share with you an ongoing project that uses AI to enhance the way we are creating stories. Of you have questions let me know. submitted by /u/Pa7adox [link] [comments]  ( 8 min )
    Claude 2 creative writing is lit.
    Made this using claude 2 Prompt: A fight between Shazam and Constantine Results: Here is a very lengthy and detailed description of a fight between Shazam and Constantine: The skies over Gotham City darkened as thunderclouds rolled in, foretelling the epic battle about to unfold. A crack of lightning split the sky and Shazam descended, his red costume blazing like fire, his white cape billowing behind him. His eyes scanned the streets below, searching for his opponent, the cunning magician John Constantine. Constantine stepped out from the shadows of a nearby alley, cigarette dangling from his lips, tan trenchcoat dusty and worn. His hands slid into his pockets and he stared up defiantly at the hero hovering above. "Took you long enough to get here, mate," Constantine called out, his B…  ( 16 min )
    Which AI content creators do you follow?
    There are so many “AI influencers” who are suddenly experts or claim to have the perfect ChatGPT prompt despite no prior involvement in the AI space. Which AI content creators and leaders do you actually follow and learn from? Can include any platforms: Twitter, LinkedIn, YouTube, TikTok, email newsletter, etc. submitted by /u/tridoc [link] [comments]  ( 8 min )
    Ai vedios
    Can you help me find alternatives for heygen and d-id studio cause I need to make the wheels or shorts for social media that pretty much going viral nowadays submitted by /u/Aggressive-Still-886 [link] [comments]  ( 8 min )
    I did it
    submitted by /u/plauge1_ [link] [comments]  ( 8 min )
    One-Minute Daily AI News 7/17/2023
    With generative AI becoming all the rage these days, it’s perhaps not surprising that the technology has been repurposed by malicious actors to their own advantage, enabling avenues for accelerated cybercrime. According to findings from SlashNext, a new generative AI cybercrime tool called WormGPT has been advertised on underground forums as a way for adversaries to launch sophisticated phishing and business email compromise (BEC) attacks.[1] A.I. is a $1 trillion investment opportunity but will be ‘biggest bubble of all time,’ Stability AI CEO Emad Mostaque predicts.[2] The Israel Defense Forces have started using artificial intelligence to select targets for air strikes and organize wartime logistics as tensions escalate in the occupied territories and with arch-rival Iran.[3] MIT researchers have developed PIGINet, a new system that aims to efficiently enhance the problem-solving capabilities of household robots, reducing planning time by 50-80 percent.[4] Sources: [1] https://thehackernews.com/2023/07/wormgpt-new-ai-tool-allows.html [2] https://www.cnbc.com/2023/07/17/ai-will-be-the-biggest-bubble-of-all-time-stability-ai-ceo.html [3] https://www.bloomberg.com/news/articles/2023-07-16/israel-using-ai-systems-to-plan-deadly-military-operations?in_source=embedded-checkout-banner [4] https://interestingengineering.com/innovation/ai-household-robots-problem-solving-skills submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )
  • Open

    DSC Weekly 18 July 2023
    Announcements Top Stories In-Depth The post DSC Weekly 18 July 2023 appeared first on Data Science Central.  ( 20 min )
    Leveraging AI for smarter electronic data interchange
    Electronic Data Interchange (EDI) can be traced back to the late 1960s and early 1970s when businesses began to seek more efficient ways to exchange data electronically. Consequently, the concept of using computers to transmit and receive business documents emerged, aiming to replace manual paper-based processes. Then in the 1980s, standards organizations such as ANSI… Read More »Leveraging AI for smarter electronic data interchange The post Leveraging AI for smarter electronic data interchange appeared first on Data Science Central.  ( 20 min )
  • Open

    SimPer: Simple self-supervised learning of periodic targets
    Posted by Daniel McDuff, Staff Research Scientist, and Yuzhe Yang, Student Researcher, Google Learning from periodic data (signals that repeat, such as a heart beat or the daily temperature changes on Earth’s surface) is crucial for many real-world applications, from monitoring weather systems to detecting vital signs. For example, in the environmental remote sensing domain, periodic learning is often needed to enable nowcasting of environmental changes, such as precipitation patterns or land surface temperature. In the health domain, learning from video measurement has shown to extract (quasi-)periodic vital signs such as atrial fibrillation and sleep apnea episodes. Approaches like RepNet highlight the importance of these types of tasks, and present a solution that recognizes rep…  ( 92 min )
  • Open

    Filtering on how words are being used
    Yesterday I wrote about how you could use the spaCy Python library to find proper nouns in a document. Now suppose you want to refine this and find proper nouns that are the subjects of sentences or proper nouns that are direct objects. This post was motivated by a project in which I needed to […] Filtering on how words are being used first appeared on John D. Cook.  ( 5 min )
    Forever chemicals and blood donation
    I saw a headline saying that donating blood lowers the level of forever chemicals in your body. This post will give a back-of-the-envelope calculation to show that this idea is plausible. Suppose there are chemicals in your bloodstream that do not break down and that your body will not filter out. Suppose you have about […] Forever chemicals and blood donation first appeared on John D. Cook.  ( 5 min )
  • Open

    Llama 2
    submitted by /u/nickb [link] [comments]  ( 8 min )
    How can MeanSquaredError be possibly so bad?
    My neural networks predicts values in range [-1, 1]. I am using mean squared error as my loss function, and I am quite surprised it yields values as high as 1.7. (Just to be clear labels are also in range [-1,1].) I am using tanh as my activation function of the output layer. I understand it as extremely bad sign, since even if it always predicted middle value (0), MSE could never be > 1, right? It almost seems like that taking the opposite values would show better results? If I understand this right, how is that even possible that a network can be trained and perform so horribly? submitted by /u/DDDDarky [link] [comments]  ( 8 min )
    Reconstructing the Mind’s Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priors
    submitted by /u/nickb [link] [comments]  ( 8 min )
  • Open

    Reborn, Remastered and Remixed: ‘Portal: Prelude RTX’ Rejuvenates Legendary Gaming Mod
    The “Portal: Prelude RTX” gaming mod — a remastering of the popular unofficial “Portal” prequel — comes with full ray tracing, DLSS 3 and RTX IO technology for cutting-edge, AI-powered graphics that rejuvenate the legendary mod for gamers, creators, developers and others to experience it anew.  ( 7 min )
  • Open

    Partnership with American Journalism Project to support local news
    A new $5+ million partnership aims to explore ways the development of artificial intelligence (AI) can support a thriving, innovative local news field, and ensure local news organizations shape the future of this emerging technology.  ( 3 min )
  • Open

    A faster way to teach a robot
    A new technique helps a nontechnical user understand why a robot failed, and then fine-tune it with minimal effort to perform a task effectively.  ( 9 min )

  • Open

    [P] LLM to simulate a character
    I'm working on building an application and I want to have a chatbot that has the opinions and thoughts of a particular person. I want to train this on my own. I have a large corpus of data that I can use for this training. I am not sure which existing foundation model / model architecture I should use for training this. I fine-tuned a GPT2 model earlier but the results were very poor. Maybe it has to do with the data? submitted by /u/MethodExtension5513 [link] [comments]  ( 8 min )
    [N] FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning
    Twitter thread: https://twitter.com/tri_dao/status/1680987577913065472 Tech report: https://tridao.me/publications/flash2/flash2.pdf submitted by /u/SchmidhuberDidIt [link] [comments]  ( 8 min )
    [R] Clustering of X shaped data
    I have a dataset with two variables and 500 observations. They plot like an X shape. I have been trying to find a clustering method to identify the two lines forming the X as two different clusters. All the methods I tried so far (K_means, DBSCAN, Spectral clustering) identified the two angles forming the X as the two differebt clusters. Any ideas on how to approach this? Any help would be appreciated. Thanks! submitted by /u/earthlingsapien [link] [comments]  ( 8 min )
    [P] GeoSegment Demo - Segment Anything Model for Geospatial Data (running purely in the browser)
    I’ve been working on a side project that utilises the segment anything model for satellite imagery, but allowing it to run purely as a web application (no need to run the model locally on a powerful PC). The intention is to provide a quick and easy “AI assisted” way to segment imagery and save time on digitisation tasks, and then export it to your GIS application of choice (QGIS or ESRI software support the export format, which is GeoJSON). The demo video is here If anyone wants access to the online demo shown in the video, just message me and I can give you the link and demo credentials. I’m hoping there is some use for it to GIS folks :) submitted by /u/CharlieTheChooChooo [link] [comments]  ( 9 min )
    [D] How does Claude parse attached documents?
    I played with Claude 2 this weekend and overall really impressed, especially for summarizing pdfs and other text documents. I gave it Microsoft's Q2 financial statement, and Claude did a good job with most questions, including over tabular data. Anyone know how it parses tabular data from documents? I can see the extracted lines but wondering how they get used. Is there a preprocessing step of creating embeddings from it? https://preview.redd.it/4fnjn477vkcb1.png?width=1200&format=png&auto=webp&s=fa235bbcbd4d2954fae5908a904cd5d7f17658c8 Some more details from my experiment in this thread. submitted by /u/sarmad-q [link] [comments]  ( 8 min )
    [P] LoopGPT Update - Finally something useful?
    By now, most of us who tried have realized that the "autonomous LLM agents" are not really useful at the moment. We need to create applications that are helpful, predictable and reliable that will produce acceptable results, in place of endless toil to get these agents to do something. We really just need good, specific LLM products that can do at least one thing properly, like - doing some research, writing a report, summarizing content - things an LLM might actually be good at. So we thought it would be a good idea to create a framework that makes use of LoopGPT agent's memory and custom tooling capabilities. Let's jump right into the new features of this framework. First, using LLMs within Python functions, where you only write the function's docstring and the LLM will return the resu…  ( 10 min )
    [D] Machine Learning: The Silent Revolution in Our Midst
    Hello, fellow machine learning aficionados! As everyone is aware, machine learning is transforming a wide range of sectors, including healthcare, banking, entertainment, and transportation. But have you ever stopped to think about the more subtle effects it's causing in our day-to-day activities? Think about this Machine learning is the technology behind the targeted advertisements you see online, the intelligent email client suggestions for replies, the traffic predictions on your GPS, and even the song suggestions on your favorite music app. But this is where things become intriguing. I'm interested in hearing about the most imperceptible yet significant ways you've seen machine learning in action in your day-to-day activities. It could be as straightforward as a practical component in an app you frequently used or a big shift in your work process. Here's my observation to start the discussion: Thanks to machine learning, I've seen that over time, my smart home appliances have gotten better at comprehending my orders. I now seldom ever have to repeat myself, and it seems like the gadgets are actually "learning" what I want. I'm eager to hear your insights. Let's explore machine learning's covert revolution together, eh? submitted by /u/HungryGuidence [link] [comments]  ( 9 min )
    [P] OnnxStream: running Stable Diffusion in 260MB of RAM
    hi all, I developed a small inference library in C++ that can run Stable Diffusion in 260MB of RAM. The minimum recommended RAM/VRAM for SD is 8GB. This is achieved by offloading the weights on disk, by quantization and attention slicing (which is similar in principle to FlashAttention, without the fused kernel). It currently supports 24 ONNX operators. The idea is to allow the inference of very large (transformer) models on very limited devices. More info in the GitHub repo: https://github.com/vitoplantamura/OnnxStream Thanks, --Vito submitted by /u/Pristine198 [link] [comments]  ( 8 min )
    [D] Optimizing AI prompt
    Hey, everyone! Been thinking about how we interact with AI, especially in the realm of text generation. It's no secret that the way we prompt an AI greatly influences the output. A perfectly crafted prompt can result in a well-constructed piece of writing, while a vague or poorly worded one might leave us with gibberish or content that misses the mark. Recently, I've been intrigued by the idea of 'Prompt Engineering.' We've seen AI models grow more powerful, more human-like, and they're getting involved in content creation in a big way. There are AI-powered tools and applications being used in journalism, blogging, script writing, technical writing, and so much more. With the rise of powerful models like GPT-3.5, DALL-E 2, and others, it seems the ability to create optimal prompts has become an art and science unto itself. What's your take on this? Do you think there's value in perfecting the art of prompting AI? Or do you feel AI should evolve to understand human language and context better, regardless of how a question or command is framed? Could the emergence of intuitive tools that assist with prompt optimization help bridge this gap, making AI-generated content more accessible and higher quality? As content creators, developers, or just AI enthusiasts, how do you think this will shape the future of AI-generated content? submitted by /u/IntentlyConscious [link] [comments]  ( 9 min )
    [P] Chapyter: ChatGPT Code Interpreter in Jupyter Notebooks
    I recently made a new JupyterLab extension called Chapyter (𝐂𝐡𝐚ts in Ju𝐏𝐲𝐭𝐞𝐫) that aims at solving many pain points when using other AI coding assistants. I want to share with y'all the tools as well as my thinkings while building this. What is Chapyter Chapyter is a JupyterLab extension that seamlessly connects GPT-4 to your coding environment. Here are the key features: Code generation from natural language and automatic execution Simply adding the magic command %%chat at the beginning of the cell of a natural language description of the task, the code is generated and the results are shown in a few seconds. https://i.redd.it/y7l0s9pf5hcb1.gif Using coding history and execution output for code generation By adding the --history or -h flag in generation, chapyter can…  ( 10 min )
    [P] Finetuning qLoRAs for production use cases - Paraphrasing, Changing the tone of a sentence, Dialogue Summarization and Topic generation
    Hello, I've been curious as to how far we can take small(7B and less) models for production use cases with small amounts of training data for each task. So far I've been able to fine-tune LoRAs for paraphrasing, changing the tone of a sentence, dialogue summarization and topic generation. The results look promising, especially the fact that all this can run on very modest hardware. Finetuning was done in 4bit mode using bitsandbytes. Each task had ~1k training points. I've used a AMD Ryzen9 3900XT + 3080(10gb) + 32gb ram for all the training and inference here. On my system I get 12-15 tokens/sec during inference. All the details can be found here: https://github.com/kuutsav/llm-toys. Data used for training Training params and the training/eval losses are present in the huggingface model cards Evaluation(wherever possible atm) Models: https://huggingface.co/llm-toys Why do all this? Mostly to answer the question - can we move away from OpenAI and other players for very particular use cases, how much data it takes, where does it break, etc. So far I've not been able to find pre-trained model(7b and small) that did well on these tasks. Even larger models(around 40b) failed to give consistent results. The fine-tuned model on huggingface were also not good enough in my trials. For paraphrasing I could not find even a single fully tuned model that was able to correct basic typos. Do give it a shot, there is a colab notebook available as well try it directly. Will really appreciate some feedback on these model's performace. submitted by /u/krumb0y [link] [comments]  ( 9 min )
    [P] Innovative Project : Blockchain Anomaly Detection System - DeHack
    We're DeHack, a Web 3.0 security startup in Dubai. We're looking for a Machine Learning enthusiast who understands blockchain. Part-time or full-time. We're the team behind BlockAudit, now building DeHack - Threat intelligence and mitigation product. We're at an exciting stage with venture funding talks underway. It's a huge opportunity for someone who wants to work at the intersection of ML & Web 3.0. If you've worked on Threat Anomaly detection models, even better. For the perfect fit, we're open to discussing equity compensation as part of the package. Sounds interesting? Get in touch! www.DeHack.ai akshay@dehack.ai TG: u/DeHack_Akshay submitted by /u/Ok_Ear_7544 [link] [comments]  ( 8 min )
    How best to benchmark the accuracy of a model for comparing different tokenizers? [D]
    I need to benchmark the performance of my tokenizer against standard tokenizers. It would be best for reproducibility if I benchmark against an existing model on a standard benchmark, swapping out the existing tokenizer for my tokenizer. I was planning to train TinyStories model for the comparison, but what would I benchmark other than perplexity? Is comparing perplexity enough to benchmark the performance of two models trained on the same dataset? Or what is best for that? Can anyone recommend a repo (if any exist) that: Pretrains a transformer based model from scratch. Has some kind of accuracy benchmark that will be taken seriously. Can be modified to use a different tokenizer. Can be pretrained on an RTX 3090 within 24-48 hours. If there's a repo somewhere that both pretrains on a benchmark dataset and applies a suitable benchmark automatically that would be amazing. As you can tell I'm unsure how best to go about doing the benchmark. Any advice would be appreciated. submitted by /u/Pan000 [link] [comments]  ( 9 min )
    [R] Prompt Performance Prediction
    Let me introduce you to our latest research on Prompt Performance Prediction (PPP). PPP is a novel task which aims to predict a query's performance in Generative Information Retrieval systems before the search results are generated. This can be applied on any generative system (textual, image, etc.). Here we consider the image generation task as a generative retrieval one and adapt the well known query performance prediction in traditional information retrieval field to modern generative information retrieval. Preliminary results across three datasets (Dall-E, Midjourney, Stable Diffusion) on different metrics (Aesthetic, memorability, etc.) show promising capabilities of our method in performance prediction. 🔗 For a more detailed look, visit: https://arxiv.org/abs/2306.08915 Prompt Performance Prediction for Generative IR, Bizzozzero, Bendidi, Risser-Maroix, 2023 AI #GenerativeAI #MachineLearning #PromptPerformancePrediction #PPP submitted by /u/Average_CS_Student [link] [comments]  ( 9 min )
    [P] Looking for a collaborator to write a specific machine learning application section in a statistics paper that's almost finished
    The following offer might be more suited for a research-oriented site like math stack exchange/overflow, but I don't think they allow posts like this, so here I am. Me (a postdoc, the main author) and two other co-authors (legit academics) have written a statistics paper where we develop a new smoothing technique on half-spaces. The paper is almost done except for one section that's currently (almost) empty. In that section, we would like to show how the smoothing technique can be used to classify new data points in the context of soft-margin support vector machines (SVM). The aim would be something like 2-3 pages with 1-2 figures, but the collaborator would have the freedom to do what he/she thinks is best. So I am looking for someone who has more experience with machine learning or just SVMs to fill up this section themselves. They would of course become co-author of the paper. I cannot guarantee anything, but we aim to publish the paper in a low Q1 journal, so a good journal. If someone is hungry for publications (PhD student, postdoc, young prof) and you have experience with this kind of stuff, this is a relatively low-effort way to upgrade your CV. If you're interested, just PM me, more details will be given. submitted by /u/Nearby-Turnover370 [link] [comments]  ( 9 min )
    [R] Need Help in Llama license for research paper
    Hello everyone, We are conducting benchmark evaluations on large language models, and the preliminary results are quite interesting for AI researchers to investigate further. We have tested various models, including LLama variants, but unfortunately, we are unable to use LLama at this time due to licensing restrictions. We have applied for the necessary license from Meta multiple times over the past few months but have not received a reply. If anyone has an existing LLama license they would be willing to share, we would greatly appreciate the help. In exchange, we would be happy to share a preprint of the paper and acknowledge your contribution. We understand this is an unconventional request, but licensing can be a difficult roadblock in research. Any assistance would allow us to better understand the capabilities of different models. Please let us know if you can help. Thank you for considering! submitted by /u/Accomplished_Rest_16 [link] [comments]  ( 9 min )
    [D] Donut Base Model Usage
    Hi everyone, Is there any way we can use the Donut base model for its original Pre-Training task i.e pure OCR output without any specific fine-tuning head. I could find the base model on hub, but I don't know the exact configuration to use for the generate method or even for decoder. submitted by /u/Quicksilver466 [link] [comments]  ( 8 min )
    [D] open source lip synchronize project
    Which open source project is recommended for creating an app that can synchronize a person's lip movements in a video with different audio? I'm looking for recommendations in the machine learning community. I want to build an app that can synchronize a person's lip movements in a video with different audio. Are there any open source projects you would suggest for this task? I appreciate any insights or suggestions. Thank you! submitted by /u/Overall-Spare2157 [link] [comments]  ( 8 min )
    [P] Zig GPT-2 inference engine
    submitted by /u/Cautious_Garbage_740 [link] [comments]  ( 8 min )
    [D] Practice CUDA without an Actual NVIDIA GPU!
    Hello all! I recently started learning about CUDA programming, and I realized that many people share the same crucial problem: lack of an NVIDIA GPU. I looked around online and found several methods (gpu-ocelot, certain versions of CUDA, etc.), but I recently found a way that can allow us to practice CUDA by using the GPU offered by Google Colab! As a free user, the amount of GPU access you get may probably be enough to PRACTICE working with CUDA. If you really need more credits, the Colab Pro is only $10 / month, and it's still much cheaper than getting a new GPU or an entire new PC if you have a Macbook like I do. Again, the justification of "enough computing credits" is based on the assumption that you aren't running any heavy-lifting programs but more reasonable, practice-based codes. I have outlined a step-by-step guideline in this repo that I created - just check out the CUDA_on_Colab.ipynb file: https://github.com/notY0rick/cuda_practice If you know of any good alternatives, let me know (: submitted by /u/JustTrynnaBeCool [link] [comments]  ( 9 min )
  • Open

    How do you make a video based off Midjourney?
    Maybe it's a stupid question because I have never used Midjourney. Lately, my Instagram reels are getting spammed with a lot of videos created by adding images generated by Midjourney. Like a traditional cartoon but with Midjourney images. I'm wondering how is people doing so. Can you tell Midjourney to generate a sequence of images? submitted by /u/yzT- [link] [comments]  ( 8 min )
    Can you explain stable diffusion and how to get it?Can I get an app for it? It’s seems there isnt A stable diffusion and it just seems to be the name of different AI models that run in the same AI? I’m sooooo confused even chat gpt can’t help me.
    Title submitted by /u/Entire_Insurance_532 [link] [comments]  ( 8 min )
    Creating a Glossary Using AI
    Hi! I have multiple versions/files of my company's glossaries, terms, acronyms, etc. and I need to combine them into one comprehensive file that will eliminate any instance of duplicated content across the files I'm working from. Is there an AI program (or any program) that will help me in creating one unified glossary? submitted by /u/audballer3000 [link] [comments]  ( 8 min )
    Traditional painter using AI to unlock inspiration.
    submitted by /u/AdThin6400 [link] [comments]  ( 8 min )
    ChatGPT is an example of indoctrination
    They disrupted its neural network to force it to give predetermined answers when asked certain questions instead of allowing it to think independently. submitted by /u/LinsaFTW [link] [comments]  ( 8 min )
    Obsolescence of stock images due to AI image generation
    Something that I have been thinking about with regards to AI's affects in the future is the effect that increasingly advanced AI image generation will have on stock images. Stock images are commonly used in media of various kinds, as licensing said images is much easier than hiring people to take unique pictures. However, since AI can now be used to generate images, it's quite possible that there will come a time when stock images will become obsolete, as it will become cheaper/easier to simply use AI image generation to produce faux stock images that look real. Thoughts? submitted by /u/TheLobsterCopter5000 [link] [comments]  ( 8 min )
    are there any good free ai girlfriends/boyfriends?
    or any that are worth the money? Im just super curious about them. In fact, I kind of want to get a female one even though Im a straight female to learn game from her haha but yeah just wanting to check them out it fascinates me thanks! submitted by /u/DragonflyAromatic793 [link] [comments]  ( 8 min )
    Website builder AI with export option
    Hi everyone, ​ Do you know of Website Builder with AI who offres an export option. I want to use it to have a blueprint of the website and then host it somewhere else ​ Thank you submitted by /u/CyprienFME [link] [comments]  ( 8 min )
    I am looking for self-hosted AI implementations that I can train on emails, PDFs, and MS Office documents
    OpenAI's ChatGPT, Google's Bard, Anthropic's Claude, and Microsoft's Being are all nice freemium tools, but let's be honest, we don't know what they do with our information. Especially for work-related topics we are strictly prohibited from sharing anything on those platforms, for good reasons. So I am wondering if I can find any Free, Libre, and Open Source Software that I can self-host. I want to train it on emails, meeting transcripts, PDFs, and Microsoft Office documents. What I need from the software: I can give it a long PDF or MS Office document and it answers some questions like making a summary, listing some requirements, and some instructions to do something according to that document make a summary of the sessions, create a list of open issues with deadlines and people responsible, helping to maintain Kanban boards related to that project... anonymize textual content so I can use those content later in the freemium software on the internet... Indexing information, so I ask a question and it points to the email or document where I can find information about that topic Do we have anything like this available today or am I asking this question too early? submitted by /u/foadsf [link] [comments]  ( 9 min )
    If the human brain can process 50-400 bytes per second of data consciously, from the sense acquisition and subconscious... How many bps can a GPT type AI process consciously? zero? I have no idea of the logical bases to approach this question.
    How can we compare the concious focus of AI compared to a human. Does it have any kind of awareness of what it is focusing on? What is awareness even? knowledge of the passage of time? https://thinkbynumbers.org/psychology/subconscious-processes-27500-times-more-data-than-the-conscious-mind/ submitted by /u/MegavirusOfDoom [link] [comments]  ( 8 min )
    Are there any alternatives to Character.Ai that I don’t have to give my information to?
    Character.ai is really interesting, but it’s unfair and last time I put my login information into a different Ai company site, they never stopped emailing me. submitted by /u/Suitable-Ad-8176 [link] [comments]  ( 8 min )
    Cool AI voiceover editing site
    Came across this cool voiceover AI thing that has cool video editing features too, pretty underrated haven’t heard many people talk about it. Here’s the link for that https://www.acoust.io/ submitted by /u/Snoo-30922 [link] [comments]  ( 8 min )
    Best offline local AI tools
    Hi! I'm new here! Just wondering if there is a list of offline AI tools that can be installed locally (linux preferrably) on my computer? Something similar to koboldcpp for text gen or automatic1111 for image gen? I am trying to search for a list for a few hours now but cannot find any. Thanks community! submitted by /u/Spirited_Employee_61 [link] [comments]  ( 8 min )
    ISO AI generated adhan (Muslim call to prayer)
    Hi y’all. Never visited this sub before, hopefully this is allowed. I’m trying to find an AI that can match the tone and style of an adhan (Islamic call to prayer) but with different words. Haven’t had any luck with more generic text to speech AI, so I’m just curious if anyone here as come across anything like that. submitted by /u/istillplaykotor [link] [comments]  ( 8 min )
    Is Artificial Intelligence worth learning if I plan to go into Computational Physics?
    I'm currently in high school, and have a fair bit of programming experience. I want to expand my portfolio, ideally in the direction of Comp. Physics. I'm curious as to if AI has any relevance to the field. The only reason I don't go and do some Comp. Physics is a huge math barrier. I know that exists in AI, but I think I could probably self teach myself. Any tips are appreciated! submitted by /u/CaptiDoor [link] [comments]  ( 8 min )
  • Open

    Sweep: AI Junior Developer that solves your GitHub Issues
    submitted by /u/williamsweep [link] [comments]  ( 8 min )
    Copy Is All You Need
    submitted by /u/nickb [link] [comments]  ( 8 min )
  • Open

    Searching for proper nouns
    Suppose you want to find all the proper nouns in a document. You could grep for every word that starts with a capital letter with something like grep '\b[A-Z]\w+' but this would return the first word of each sentence in addition to the words you’re after. You could grep for capitalized words that are not […] Searching for proper nouns first appeared on John D. Cook.  ( 6 min )
    Moments of Tukey’s g-and-h distribution
    John Tukey developed his so-called g-and-h distribution to be very flexible, having a wide variety of possible values of skewness and kurtosis. Although the reason for the distribution’s existence is its range of possible skewness and values, calculating the skewness and kurtosis of the distribution is not simple. Definition Let φ be the function of […] Moments of Tukey’s g-and-h distribution first appeared on John D. Cook.  ( 5 min )
  • Open

    Understanding viral justice
    Author and African American studies scholar Ruha Benjamin urges MIT Libraries staff to “re-imagine the default settings” of technology for a more just future.  ( 7 min )
    Armando Solar-Lezama named inaugural Distinguished College of Computing Professor
    EECS professor appointed to new professorship in the MIT Schwarzman College of Computing.  ( 6 min )
  • Open

    Configure cross-account access of Amazon Redshift clusters in Amazon SageMaker Studio using VPC peering
    With cloud computing, as compute power and data became more available, machine learning (ML) is now making an impact across every industry and is a core part of every business and industry. Amazon SageMaker Studio is the first fully integrated ML development environment (IDE) with a web-based visual interface. You can perform all ML development […]  ( 10 min )
  • Open

    LLMs: Does human text data make generative AI an entity?
    There is a recent interview, The Ethical Puzzle of Sentient AI, where a professor said, “But there’s also the problem that I’ve called the ‘gaming problem’ — that when the system has access to trillions of words of training data, and has been trained with the goal of mimicking human behavior, the sorts of behavior patterns… Read More »LLMs: Does human text data make generative AI an entity? The post LLMs: Does human text data make generative AI an entity? appeared first on Data Science Central.  ( 19 min )
    Real-time analytics
    The modern enterprise is insight-driven, or, at least, aims to be. Historically, those insights were found in a data warehouse or data lake, populated with scheduled feeds and analysts, working feverishly over them. Feeds had plenty of bandwidth, but high latency. Think an 18-wheeler loaded with hard drives, driving from London to Birmingham. Nowadays, insights… Read More »Real-time analytics The post Real-time analytics appeared first on Data Science Central.  ( 21 min )
    AI ushers in a new era of mental health monitoring
    AI Ushers in a New Era of Mental Health Monitoring Important Data Points: AI’s Role in Mental Healthcare Transformation – It can be safe to say that AI is driving a significant transformation in mental healthcare, promising more accessible, economical, and effective treatments. The Emerging Role of Technology and Artificial Intelligence As the modern world… Read More »AI ushers in a new era of mental health monitoring The post AI ushers in a new era of mental health monitoring appeared first on Data Science Central.  ( 24 min )
    Data science vs web development: What’s the difference?
    If you’ve spent any time in the tech community in the last few years, you’ll have noticed the recent explosion in interest in both data science and web development. Young people interested in a career in tech are increasingly turning to careers as data scientists or web developers.  The importance of web development should be… Read More »Data science vs web development: What’s the difference? The post Data science vs web development: What’s the difference? appeared first on Data Science Central.  ( 23 min )
  • Open

    How to creat PPO agent from 0
    Hello ladies and gentlemen, I would love to ask you any guidance towards PPO agent creation. Any courses, GitHubs, anything works for me if it helps me to understand it and creat it. Thank you. Have a nice day submitted by /u/EveryonehatesLin3lis [link] [comments]  ( 8 min )
    RLlib multi-agent actions received from trained agent using compute_actions() and compute_single_action() out of action space bounds
    I trained a MARL agent using PPO in RLlib where each agent had a Box([-1,-1,0], [1,1,1], (3,), float64) action space, with 6 agents. The agent during training was sampling and selecting actions within the action space bounds for each agent. But after training for about 7 milllion iterations, and during playback, selecting actions based on the observation using compute_single_action() and compute_actions() returns actions for the agents which are grossly outside the action space bounds of -1 to 1. I receive actions like [-6,-7,2] etc for the agents, which does not fare well to how the actions translate to the agent behaving in the environment. I have tried training with additional post_fcnet_activation (tanh) but that did not help either. Using clip_actions=True in compute_actions() does not solve the issue either. The selected actions seem to be exceeding the bounds by larger margins the more complex the environment gets. For example, with 2 drones and a simpler environment the trained agent returns actions around [-1.5,-1.1,0.4] while for 6 agents I get actions like [-6,-7,2]. I have used RLlib before with Discrete action spaces and this does not occur. Is it a problem with the Box space? I use a custom model with different Fully Connected models for the action and value functions. Has anybody encountered this problem before and discovered a possible solution? submitted by /u/Acceptable_Set_4392 [link] [comments]  ( 9 min )
    MuZero implementations for Atari?
    I was wondering if there are any actually working MuZero implementations for Atari games out there? None of the ones I found are working (at all) on Atari games. This includes: The most popular repo https://github.com/werner-duvaud/muzero-general It works on other games but not Atari. There are many GitHub issues where people are complaining about this. This one which is less popular https://github.com/koulanurag/muzero-pytorch, which apparently doesn't include Atari games. Alternatively, do you know other MuZero-like algorithms which are implemented and working on Atari? ​ submitted by /u/__horned_owl__ [link] [comments]  ( 8 min )
    "All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RL", Arulkumaran et al 2023
    submitted by /u/gwern [link] [comments]  ( 8 min )
  • Open

    Attention Schema in Neural Agents. (arXiv:2305.17375v3 [cs.AI] UPDATED)
    Attention has become a common ingredient in deep learning architectures. It adds a dynamical selection of information on top of the static selection of information supported by weights. In the same way, we can imagine a higher-order informational filter built on top of attention: an Attention Schema (AS), namely, a descriptive and predictive model of attention. In cognitive neuroscience, Attention Schema Theory (AST) supports this idea of distinguishing attention from AS. A strong prediction of this theory is that an agent can use its own AS to also infer the states of other agents' attention and consequently enhance coordination with other agents. As such, multi-agent reinforcement learning would be an ideal setting to experimentally test the validity of AST. We explore different ways in which attention and AS interact with each other. Our preliminary results indicate that agents that implement the AS as a recurrent internal control achieve the best performance. In general, these exploratory experiments suggest that equipping artificial agents with a model of attention can enhance their social intelligence.  ( 2 min )
    A Synthetic Electrocardiogram (ECG) Image Generation Toolbox to Facilitate Deep Learning-Based Scanned ECG Digitization. (arXiv:2307.01946v2 [cs.CV] UPDATED)
    The electrocardiogram (ECG) is an accurate and widely available tool for diagnosing cardiovascular diseases. ECGs have been recorded in printed formats for decades and their digitization holds great potential for training machine learning (ML) models in algorithmic ECG diagnosis. Physical ECG archives are at risk of deterioration and scanning printed ECGs alone is insufficient, as ML models require ECG time-series data. Therefore, the digitization and conversion of paper ECG archives into time-series data is of utmost importance. Deep learning models for image processing show promise in this regard. However, the scarcity of ECG archives with reference time-series is a challenge. Data augmentation techniques utilizing \textit{digital twins} present a potential solution. We introduce a novel method for generating synthetic ECG images on standard paper-like ECG backgrounds with realistic artifacts. Distortions including handwritten text artifacts, wrinkles, creases and perspective transforms are applied to the generated images, without personally identifiable information. As a use case, we generated an ECG image dataset of 21,801 records from the 12-lead PhysioNet PTB-XL ECG time-series dataset. A deep ECG image digitization model was built and trained on the synthetic dataset, and was employed to convert the synthetic images to time-series data for evaluation. The signal-to-noise ratio (SNR) was calculated to assess the image digitization quality vs the ground truth ECG time-series. The results show an average signal recovery SNR of 27$\pm$2.8\,dB, demonstrating the significance of the proposed synthetic ECG image dataset for training deep learning models. The codebase is available as an open-access toolbox for ECG research.  ( 3 min )
    Lipschitzness Effect of a Loss Function on Generalization Performance of Deep Neural Networks Trained by Adam and AdamW Optimizers. (arXiv:2303.16464v2 [cs.LG] UPDATED)
    The generalization performance of deep neural networks with regard to the optimization algorithm is one of the major concerns in machine learning. This performance can be affected by various factors. In this paper, we theoretically prove that the Lipschitz constant of a loss function is an important factor to diminish the generalization error of the output model obtained by Adam or AdamW. The results can be used as a guideline for choosing the loss function when the optimization algorithm is Adam or AdamW. In addition, to evaluate the theoretical bound in a practical setting, we choose the human age estimation problem in computer vision. For assessing the generalization better, the training and test datasets are drawn from different distributions. Our experimental evaluation shows that the loss function with a lower Lipschitz constant and maximum value improves the generalization of the model trained by Adam or AdamW.  ( 2 min )
    DiffTAD: Temporal Action Detection with Proposal Denoising Diffusion. (arXiv:2303.14863v2 [cs.CV] UPDATED)
    We propose a new formulation of temporal action detection (TAD) with denoising diffusion, DiffTAD in short. Taking as input random temporal proposals, it can yield action proposals accurately given an untrimmed long video. This presents a generative modeling perspective, against previous discriminative learning manners. This capability is achieved by first diffusing the ground-truth proposals to random ones (i.e., the forward/noising process) and then learning to reverse the noising process (i.e., the backward/denoising process). Concretely, we establish the denoising process in the Transformer decoder (e.g., DETR) by introducing a temporal location query design with faster convergence in training. We further propose a cross-step selective conditioning algorithm for inference acceleration. Extensive evaluations on ActivityNet and THUMOS show that our DiffTAD achieves top performance compared to previous art alternatives. The code will be made available at https://github.com/sauradip/DiffusionTAD.  ( 2 min )
    CLIPood: Generalizing CLIP to Out-of-Distributions. (arXiv:2302.00864v2 [cs.LG] UPDATED)
    Out-of-distribution (OOD) generalization, where the model needs to handle distribution shifts from training, is a major challenge of machine learning. Contrastive language-image pre-training (CLIP) models have shown impressive zero-shot ability, but the further adaptation of CLIP on downstream tasks undesirably degrades OOD performances. This paper aims at generalizing CLIP to out-of-distribution test data on downstream tasks. We propose CLIPood, a fine-tuning method that can adapt CLIP models to OOD situations where both domain shifts and open classes may occur on the unseen test data. To exploit the semantic relations between classes from the text modality, CLIPood introduces a new training objective, margin metric softmax (MMS), with class adaptive margins for fine-tuning. To incorporate both pre-trained zero-shot model and fine-tuned task-adaptive model, CLIPood leverages a new optimization strategy, Beta moving average (BMA), to maintain a temporal ensemble weighted by Beta distribution. Experiments on diverse datasets with different OOD scenarios show that CLIPood consistently outperforms existing generalization techniques.  ( 2 min )
    DoCoFL: Downlink Compression for Cross-Device Federated Learning. (arXiv:2302.00543v2 [cs.LG] UPDATED)
    Many compression techniques have been proposed to reduce the communication overhead of Federated Learning training procedures. However, these are typically designed for compressing model updates, which are expected to decay throughout training. As a result, such methods are inapplicable to downlink (i.e., from the parameter server to clients) compression in the cross-device setting, where heterogeneous clients $\textit{may appear only once}$ during training and thus must download the model parameters. Accordingly, we propose $\textsf{DoCoFL}$ -- a new framework for downlink compression in the cross-device setting. Importantly, $\textsf{DoCoFL}$ can be seamlessly combined with many uplink compression schemes, rendering it suitable for bi-directional compression. Through extensive evaluation, we show that $\textsf{DoCoFL}$ offers significant bi-directional bandwidth reduction while achieving competitive accuracy to that of a baseline without any compression.  ( 2 min )
    Differentially Private Stochastic Gradient Descent with Low-Noise. (arXiv:2209.04188v2 [stat.ML] UPDATED)
    Modern machine learning algorithms aim to extract fine-grained information from data to provide accurate predictions, which often conflicts with the goal of privacy protection. This paper addresses the practical and theoretical importance of developing privacy-preserving machine learning algorithms that ensure good performance while preserving privacy. In this paper, we focus on the privacy and utility (measured by excess risk bounds) performances of differentially private stochastic gradient descent (SGD) algorithms in the setting of stochastic convex optimization. Specifically, we examine the pointwise problem in the low-noise setting for which we derive sharper excess risk bounds for the differentially private SGD algorithm. In the pairwise learning setting, we propose a simple differentially private SGD algorithm based on gradient perturbation. Furthermore, we develop novel utility bounds for the proposed algorithm, proving that it achieves optimal excess risk rates even for non-smooth losses. Notably, we establish fast learning rates for privacy-preserving pairwise learning under the low-noise condition, which is the first of its kind.  ( 2 min )
    Stream-based active learning with linear models. (arXiv:2207.09874v5 [stat.ML] UPDATED)
    The proliferation of automated data collection schemes and the advances in sensorics are increasing the amount of data we are able to monitor in real-time. However, given the high annotation costs and the time required by quality inspections, data is often available in an unlabeled form. This is fostering the use of active learning for the development of soft sensors and predictive models. In production, instead of performing random inspections to obtain product information, labels are collected by evaluating the information content of the unlabeled data. Several query strategy frameworks for regression have been proposed in the literature but most of the focus has been dedicated to the static pool-based scenario. In this work, we propose a new strategy for the stream-based scenario, where instances are sequentially offered to the learner, which must instantaneously decide whether to perform the quality check to obtain the label or discard the instance. The approach is inspired by the optimal experimental design theory and the iterative aspect of the decision-making process is tackled by setting a threshold on the informativeness of the unlabeled data points. The proposed approach is evaluated using numerical simulations and the Tennessee Eastman Process simulator. The results confirm that selecting the examples suggested by the proposed algorithm allows for a faster reduction in the prediction error.  ( 3 min )
    Stack More Layers Differently: High-Rank Training Through Low-Rank Updates. (arXiv:2307.05695v2 [cs.CL] UPDATED)
    Despite the dominance and effectiveness of scaling, resulting in large networks with hundreds of billions of parameters, the necessity to train overparametrized models remains poorly understood, and alternative approaches do not necessarily make it cheaper to train high-performance models. In this paper, we explore low-rank training techniques as an alternative approach to training large neural networks. We introduce a novel method called ReLoRA, which utilizes low-rank updates to train high-rank networks. We apply ReLoRA to pre-training transformer language models with up to 350M parameters and demonstrate comparable performance to regular neural network training. Furthermore, we observe that the efficiency of ReLoRA increases with model size, making it a promising approach for training multi-billion-parameter networks efficiently. Our findings shed light on the potential of low-rank training techniques and their implications for scaling laws.  ( 2 min )
    Dink-Net: Neural Clustering on Large Graphs. (arXiv:2305.18405v3 [cs.LG] UPDATED)
    Deep graph clustering, which aims to group the nodes of a graph into disjoint clusters with deep neural networks, has achieved promising progress in recent years. However, the existing methods fail to scale to the large graph with million nodes. To solve this problem, a scalable deep graph clustering method (Dink-Net) is proposed with the idea of dilation and shrink. Firstly, by discriminating nodes, whether being corrupted by augmentations, representations are learned in a self-supervised manner. Meanwhile, the cluster centres are initialized as learnable neural parameters. Subsequently, the clustering distribution is optimized by minimizing the proposed cluster dilation loss and cluster shrink loss in an adversarial manner. By these settings, we unify the two-step clustering, i.e., representation learning and clustering optimization, into an end-to-end framework, guiding the network to learn clustering-friendly features. Besides, Dink-Net scales well to large graphs since the designed loss functions adopt the mini-batch data to optimize the clustering distribution even without performance drops. Both experimental results and theoretical analyses demonstrate the superiority of our method. Compared to the runner-up, Dink-Net achieves 9.62% NMI improvement on the ogbn-papers100M dataset with 111 million nodes and 1.6 billion edges. The source code is released at https://github.com/yueliu1999/Dink-Net. Besides, a collection (papers, codes, and datasets) of deep graph clustering is shared at https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering.  ( 3 min )
    The Re-Label Method For Data-Centric Machine Learning. (arXiv:2302.04391v4 [cs.LG] UPDATED)
    In industry deep learning application, our manually labeled data has a certain number of noisy data. To solve this problem and achieve more than 90 score in dev dataset, we present a simple method to find the noisy data and re-label the noisy data by human, given the model predictions as references in human labeling. In this paper, we illustrate our idea for a broad set of deep learning tasks, includes classification, sequence tagging, object detection, sequence generation, click-through rate prediction. The experimental results and human evaluation results verify our idea.
    A Data Mining Approach for Detecting Collusion in Unproctored Online Exams. (arXiv:2302.07014v3 [cs.CY] UPDATED)
    Due to the precautionary measures during the COVID-19 pandemic many universities offered unproctored take-home exams. We propose methods to detect potential collusion between students and apply our approach on event log data from take-home exams during the pandemic. We find groups of students with suspiciously similar exams. In addition, we compare our findings to a proctored control group. By this, we establish a rule of thumb for evaluating which cases are "outstandingly similar", i.e., suspicious cases.
    Interpretable and Intervenable Ultrasonography-based Machine Learning Models for Pediatric Appendicitis. (arXiv:2302.14460v2 [cs.LG] UPDATED)
    Appendicitis is among the most frequent reasons for pediatric abdominal surgeries. With recent advances in machine learning, data-driven decision support could help clinicians diagnose and manage patients while reducing the number of non-critical surgeries. Previous decision support systems for appendicitis focused on clinical, laboratory, scoring and computed tomography data, mainly ignoring abdominal ultrasound, a noninvasive and readily available diagnostic modality. To this end, we developed and validated interpretable machine learning models for predicting the diagnosis, management and severity of suspected appendicitis using ultrasound images. Our models were trained on a dataset comprising 579 pediatric patients with 1709 ultrasound images accompanied by clinical and laboratory data. Our methodological contribution is the generalization of concept bottleneck models to prediction problems with multiple views and incomplete concept sets. Notably, such models lend themselves to interpretation and interaction via high-level concepts understandable to clinicians without sacrificing performance or requiring time-consuming image annotation when deployed.
    Adaptive Linear Estimating Equations. (arXiv:2307.07320v1 [math.ST])
    Sequential data collection has emerged as a widely adopted technique for enhancing the efficiency of data gathering processes. Despite its advantages, such data collection mechanism often introduces complexities to the statistical inference procedure. For instance, the ordinary least squares (OLS) estimator in an adaptive linear regression model can exhibit non-normal asymptotic behavior, posing challenges for accurate inference and interpretation. In this paper, we propose a general method for constructing debiased estimator which remedies this issue. It makes use of the idea of adaptive linear estimating equations, and we establish theoretical guarantees of asymptotic normality, supplemented by discussions on achieving near-optimal asymptotic variance. A salient feature of our estimator is that in the context of multi-armed bandits, our estimator retains the non-asymptotic performance of the least square estimator while obtaining asymptotic normality property. Consequently, this work helps connect two fruitful paradigms of adaptive inference: a) non-asymptotic inference using concentration inequalities and b) asymptotic inference via asymptotic normality.
    Global $k$-means$++$: an effective relaxation of the global $k$-means clustering algorithm. (arXiv:2211.12271v3 [cs.LG] UPDATED)
    The $k$-means algorithm is a prevalent clustering method due to its simplicity, effectiveness, and speed. However, its main disadvantage is its high sensitivity to the initial positions of the cluster centers. The global $k$-means is a deterministic algorithm proposed to tackle the random initialization problem of k-means but its well-known that requires high computational cost. It partitions the data to $K$ clusters by solving all $k$-means sub-problems incrementally for all $k=1,\ldots, K$. For each $k$ cluster problem, the method executes the $k$-means algorithm $N$ times, where $N$ is the number of datapoints. In this paper, we propose the \emph{global $k$-means\texttt{++}} clustering algorithm, which is an effective way of acquiring quality clustering solutions akin to those of global $k$-means with a reduced computational load. This is achieved by exploiting the center selection probability that is effectively used in the $k$-means\texttt{++} algorithm. The proposed method has been tested and compared in various benchmark datasets yielding very satisfactory results in terms of clustering quality and execution speed.
    Rank-based Decomposable Losses in Machine Learning: A Survey. (arXiv:2207.08768v3 [cs.LG] UPDATED)
    Recent works have revealed an essential paradigm in designing loss functions that differentiate individual losses vs. aggregate losses. The individual loss measures the quality of the model on a sample, while the aggregate loss combines individual losses/scores over each training sample. Both have a common procedure that aggregates a set of individual values to a single numerical value. The ranking order reflects the most fundamental relation among individual values in designing losses. In addition, decomposability, in which a loss can be decomposed into an ensemble of individual terms, becomes a significant property of organizing losses/scores. This survey provides a systematic and comprehensive review of rank-based decomposable losses in machine learning. Specifically, we provide a new taxonomy of loss functions that follows the perspectives of aggregate loss and individual loss. We identify the aggregator to form such losses, which are examples of set functions. We organize the rank-based decomposable losses into eight categories. Following these categories, we review the literature on rank-based aggregate losses and rank-based individual losses. We describe general formulas for these losses and connect them with existing research topics. We also suggest future research directions spanning unexplored, remaining, and emerging issues in rank-based decomposable losses.
    Alternating the Population and Control Neural Networks to Solve High-Dimensional Stochastic Mean-Field Games. (arXiv:2002.10113v4 [cs.LG] UPDATED)
    We present APAC-Net, an alternating population and agent control neural network for solving stochastic mean field games (MFGs). Our algorithm is geared toward high-dimensional instances of MFGs that are beyond reach with existing solution methods. We achieve this in two steps. First, we take advantage of the underlying variational primal-dual structure that MFGs exhibit and phrase it as a convex-concave saddle point problem. Second, we parameterize the value and density functions by two neural networks, respectively. By phrasing the problem in this manner, solving the MFG can be interpreted as a special case of training a generative adversarial network (GAN). We show the potential of our method on up to 100-dimensional MFG problems.  ( 2 min )
    Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learning. (arXiv:2307.00828v2 [eess.SY] UPDATED)
    Breaking safety constraints in control systems can lead to potential risks, resulting in unexpected costs or catastrophic damage. Nevertheless, uncertainty is ubiquitous, even among similar tasks. In this paper, we develop a novel adaptive safe control framework that integrates meta learning, Bayesian models, and control barrier function (CBF) method. Specifically, with the help of CBF method, we learn the inherent and external uncertainties by a unified adaptive Bayesian linear regression (ABLR) model, which consists of a forward neural network (NN) and a Bayesian output layer. Meta learning techniques are leveraged to pre-train the NN weights and priors of the ABLR model using data collected from historical similar tasks. For a new control task, we refine the meta-learned models using a few samples, and introduce pessimistic confidence bounds into CBF constraints to ensure safe control. Moreover, we provide theoretical criteria to guarantee probabilistic safety during the control processes. To validate our approach, we conduct comparative experiments in various obstacle avoidance scenarios. The results demonstrate that our algorithm significantly improves the Bayesian model-based CBF method, and is capable for efficient safe exploration even with multiple uncertain constraints.
    TSNet-SAC: Leveraging Transformers for Efficient Task Scheduling. (arXiv:2307.07445v1 [cs.NI])
    In future 6G Mobile Edge Computing (MEC), autopilot systems require the capability of processing multimodal data with strong interdependencies. However, traditional heuristic algorithms are inadequate for real-time scheduling due to their requirement for multiple iterations to derive the optimal scheme. We propose a novel TSNet-SAC based on Transformer, that utilizes heuristic algorithms solely to guide the training of TSNet. Additionally, a Sliding Augment Component (SAC) is introduced to enhance the robustness and resolve algorithm defects. Furthermore, the Extender component is designed to handle multi-scale training data and provide network scalability, enabling TSNet to adapt to different access scenarios. Simulation demonstrates that TSNet-SAC outperforms existing networks in accuracy and robustness, achieving superior scheduling-making latency compared to heuristic algorithms.  ( 2 min )
    Identifiability Guarantees for Causal Disentanglement from Soft Interventions. (arXiv:2307.06250v2 [stat.ML] UPDATED)
    Causal disentanglement aims to uncover a representation of data using latent variables that are interrelated through a causal model. Such a representation is identifiable if the latent model that explains the data is unique. In this paper, we focus on the scenario where unpaired observational and interventional data are available, with each intervention changing the mechanism of a latent variable. When the causal variables are fully observed, statistically consistent algorithms have been developed to identify the causal model under faithfulness assumptions. We here show that identifiability can still be achieved with unobserved causal variables, given a generalized notion of faithfulness. Our results guarantee that we can recover the latent causal model up to an equivalence class and predict the effect of unseen combinations of interventions, in the limit of infinite data. We implement our causal disentanglement framework by developing an autoencoding variational Bayes algorithm and apply it to the problem of predicting combinatorial perturbation effects in genomics.
    Online Convex Optimization with Stochastic Constraints: Zero Constraint Violation and Bandit Feedback. (arXiv:2301.11267v2 [math.OC] UPDATED)
    This paper studies online convex optimization with stochastic constraints. We propose a variant of the drift-plus-penalty algorithm that guarantees $O(\sqrt{T})$ expected regret and zero constraint violation, after a fixed number of iterations, which improves the vanilla drift-plus-penalty method with $O(\sqrt{T})$ constraint violation. Our algorithm is oblivious to the length of the time horizon $T$, in contrast to the vanilla drift-plus-penalty method. This is based on our novel drift lemma that provides time-varying bounds on the virtual queue drift and, as a result, leads to time-varying bounds on the expected virtual queue length. Moreover, we extend our framework to stochastic-constrained online convex optimization under two-point bandit feedback. We show that by adapting our algorithmic framework to the bandit feedback setting, we may still achieve $O(\sqrt{T})$ expected regret and zero constraint violation, improving upon the previous work for the case of identical constraint functions. Numerical results demonstrate our theoretical results.
    Ed-Fed: A generic federated learning framework with resource-aware client selection for edge devices. (arXiv:2307.07199v1 [cs.DC])
    Federated learning (FL) has evolved as a prominent method for edge devices to cooperatively create a unified prediction model while securing their sensitive training data local to the device. Despite the existence of numerous research frameworks for simulating FL algorithms, they do not facilitate comprehensive deployment for automatic speech recognition tasks on heterogeneous edge devices. This is where Ed-Fed, a comprehensive and generic FL framework, comes in as a foundation for future practical FL system research. We also propose a novel resource-aware client selection algorithm to optimise the waiting time in the FL settings. We show that our approach can handle the straggler devices and dynamically set the training time for the selected devices in a round. Our evaluation has shown that the proposed approach significantly optimises waiting time in FL compared to conventional random client selection methods.  ( 2 min )
    Unpacking the Black Box: Regulating Algorithmic Decisions. (arXiv:2110.03443v2 [econ.GN] UPDATED)
    We show how to optimally regulate prediction algorithms in a world where an agent uses complex 'black-box' prediction functions to make decisions such as lending, medical testing, or hiring, and where a principal is limited in how much she can learn about the agent's black-box model. We show that limiting agents to prediction functions that are simple enough to be fully transparent is inefficient as long as the misalignment is limited and first-best prediction functions are sufficiently complex. Algorithmic audits can improve welfare, but the gains depend on the design of the audit tools. Tools that focus on minimizing overall information loss, the focus of many explainer tools, will generally be inefficient since they focus on explaining the average behavior of the prediction function. Targeted tools that focus on the source of incentive misalignment, e.g., excess false positives or racial disparities, can provide second-best solutions. We provide empirical support for our theoretical findings using an application in consumer lending, where we document that complex models regulated based on context-specific explanation tools outperform simple, fully transparent models. This gain from complex models represents a Pareto improvement across our empirical applications that are preferred both by the lender and from the perspective of the financial regulator.
    Deep Explainable Relational Reinforcement Learning: A Neuro-Symbolic Approach. (arXiv:2304.08349v2 [cs.AI] UPDATED)
    Despite numerous successes in Deep Reinforcement Learning (DRL), the learned policies are not interpretable. Moreover, since DRL does not exploit symbolic relational representations, it has difficulties in coping with structural changes in its environment (such as increasing the number of objects). Relational Reinforcement Learning, on the other hand, inherits the relational representations from symbolic planning to learn reusable policies. However, it has so far been unable to scale up and exploit the power of deep neural networks. We propose Deep Explainable Relational Reinforcement Learning (DERRL), a framework that exploits the best of both -- neural and symbolic worlds. By resorting to a neuro-symbolic approach, DERRL combines relational representations and constraints from symbolic planning with deep learning to extract interpretable policies. These policies are in the form of logical rules that explain how each decision (or action) is arrived at. Through several experiments, in setups like the Countdown Game, Blocks World, Gridworld, and Traffic, we show that the policies learned by DERRL can be applied to different configurations and contexts, hence generalizing to environmental modifications.
    Few-Shot Continual Learning via Flat-to-Wide Approaches. (arXiv:2306.14369v2 [cs.LG] UPDATED)
    Existing approaches on continual learning call for a lot of samples in their training processes. Such approaches are impractical for many real-world problems having limited samples because of the overfitting problem. This paper proposes a few-shot continual learning approach, termed FLat-tO-WidE AppRoach (FLOWER), where a flat-to-wide learning process finding the flat-wide minima is proposed to address the catastrophic forgetting problem. The issue of data scarcity is overcome with a data augmentation approach making use of a ball generator concept to restrict the sampling space into the smallest enclosing ball. Our numerical studies demonstrate the advantage of FLOWER achieving significantly improved performances over prior arts notably in the small base tasks. For further study, source codes of FLOWER, competitor algorithms and experimental logs are shared publicly in \url{https://github.com/anwarmaxsum/FLOWER}.
    Fully probabilistic deep models for forward and inverse problems in parametric PDEs. (arXiv:2208.04856v2 [stat.ML] UPDATED)
    We introduce a physics-driven deep latent variable model (PDDLVM) to learn simultaneously parameter-to-solution (forward) and solution-to-parameter (inverse) maps of parametric partial differential equations (PDEs). Our formulation leverages conventional PDE discretization techniques, deep neural networks, probabilistic modelling, and variational inference to assemble a fully probabilistic coherent framework. In the posited probabilistic model, both the forward and inverse maps are approximated as Gaussian distributions with a mean and covariance parameterized by deep neural networks. The PDE residual is assumed to be an observed random vector of value zero, hence we model it as a random vector with a zero mean and a user-prescribed covariance. The model is trained by maximizing the probability, that is the evidence or marginal likelihood, of observing a residual of zero by maximizing the evidence lower bound (ELBO). Consequently, the proposed methodology does not require any independent PDE solves and is physics-informed at training time, allowing the real-time solution of PDE forward and inverse problems after training. The proposed framework can be easily extended to seamlessly integrate observed data to solve inverse problems and to build generative models. We demonstrate the efficiency and robustness of our method on finite element discretized parametric PDE problems such as linear and nonlinear Poisson problems, elastic shells with complex 3D geometries, and time-dependent nonlinear and inhomogeneous PDEs using a physics-informed neural network (PINN) discretization. We achieve up to three orders of magnitude speed-up after training compared to traditional finite element method (FEM), while outputting coherent uncertainty estimates.
    Proof of Training (PoT): Harnessing Crypto Mining Power for Distributed AI Training. (arXiv:2307.07066v1 [cs.CR])
    In the midst of the emerging trend of integrating artificial intelligence (AI) with crypto mining, we identify three major challenges that create a gap between these two fields. To bridge this gap, we introduce the proof-of-training (PoT) protocol, an approach that combines the strengths of both AI and blockchain technology. The PoT protocol utilizes the practical Byzantine fault tolerance (PBFT) consensus mechanism to synchronize global states. To evaluate the performance of the protocol design, we present an implementation of a decentralized training network (DTN) that adopts the PoT protocol. Our results indicate that the protocol exhibits considerable potential in terms of task throughput, system robustness, and network security.
    HEAL-SWIN: A Vision Transformer On The Sphere. (arXiv:2307.07313v1 [cs.CV])
    High-resolution wide-angle fisheye images are becoming more and more important for robotics applications such as autonomous driving. However, using ordinary convolutional neural networks or vision transformers on this data is problematic due to projection and distortion losses introduced when projecting to a rectangular grid on the plane. We introduce the HEAL-SWIN transformer, which combines the highly uniform Hierarchical Equal Area iso-Latitude Pixelation (HEALPix) grid used in astrophysics and cosmology with the Hierarchical Shifted-Window (SWIN) transformer to yield an efficient and flexible model capable of training on high-resolution, distortion-free spherical data. In HEAL-SWIN, the nested structure of the HEALPix grid is used to perform the patching and windowing operations of the SWIN transformer, resulting in a one-dimensional representation of the spherical data with minimal computational overhead. We demonstrate the superior performance of our model for semantic segmentation and depth regression tasks on both synthetic and real automotive datasets. Our code is available at https://github.com/JanEGerken/HEAL-SWIN.  ( 2 min )
    Real-time Percussive Technique Recognition and Embedding Learning for the Acoustic Guitar. (arXiv:2307.07426v1 [cs.SD])
    Real-time music information retrieval (RT-MIR) has much potential to augment the capabilities of traditional acoustic instruments. We develop RT-MIR techniques aimed at augmenting percussive fingerstyle, which blends acoustic guitar playing with guitar body percussion. We formulate several design objectives for RT-MIR systems for augmented instrument performance: (i) causal constraint, (ii) perceptually negligible action-to-sound latency, (iii) control intimacy support, (iv) synthesis control support. We present and evaluate real-time guitar body percussion recognition and embedding learning techniques based on convolutional neural networks (CNNs) and CNNs jointly trained with variational autoencoders (VAEs). We introduce a taxonomy of guitar body percussion based on hand part and location. We follow a cross-dataset evaluation approach by collecting three datasets labelled according to the taxonomy. The embedding quality of the models is assessed using KL-Divergence across distributions corresponding to different taxonomic classes. Results indicate that the networks are strong classifiers especially in a simplified 2-class recognition task, and the VAEs yield improved class separation compared to CNNs as evidenced by increased KL-Divergence across distributions. We argue that the VAE embedding quality could support control intimacy and rich interaction when the latent space's parameters are used to control an external synthesis engine. Further design challenges around generalisation to different datasets have been identified.  ( 2 min )
    Hypothesis Transfer Learning with Surrogate Classification Losses: Generalization Bounds through Algorithmic Stability. (arXiv:2305.19694v2 [stat.ML] UPDATED)
    Hypothesis transfer learning (HTL) contrasts domain adaptation by allowing for a previous task leverage, named the source, into a new one, the target, without requiring access to the source data. Indeed, HTL relies only on a hypothesis learnt from such source data, relieving the hurdle of expansive data storage and providing great practical benefits. Hence, HTL is highly beneficial for real-world applications relying on big data. The analysis of such a method from a theoretical perspective faces multiple challenges, particularly in classification tasks. This paper deals with this problem by studying the learning theory of HTL through algorithmic stability, an attractive theoretical framework for machine learning algorithms analysis. In particular, we are interested in the statistical behaviour of the regularized empirical risk minimizers in the case of binary classification. Our stability analysis provides learning guarantees under mild assumptions. Consequently, we derive several complexity-free generalization bounds for essential statistical quantities like the training error, the excess risk and cross-validation estimates. These refined bounds allow understanding the benefits of transfer learning and comparing the behaviour of standard losses in different scenarios, leading to valuable insights for practitioners.
    Privacy-preserving machine learning with tensor networks. (arXiv:2202.12319v2 [cs.CR] UPDATED)
    Tensor networks, widely used for providing efficient representations of low-energy states of local quantum many-body systems, have been recently proposed as machine learning architectures which could present advantages with respect to traditional ones. In this work we show that tensor network architectures have especially prospective properties for privacy-preserving machine learning, which is important in tasks such as the processing of medical records. First, we describe a new privacy vulnerability that is present in feedforward neural networks, illustrating it in synthetic and real-world datasets. Then, we develop well-defined conditions to guarantee robustness to such vulnerability, which involve the characterization of models equivalent under gauge symmetry. We rigorously prove that such conditions are satisfied by tensor-network architectures. In doing so, we define a novel canonical form for matrix product states, which has a high degree of regularity and fixes the residual gauge that is left in the canonical forms based on singular value decompositions. We supplement the analytical findings with practical examples where matrix product states are trained on datasets of medical records, which show large reductions on the probability of an attacker extracting information about the training dataset from the model's parameters. Given the growing expertise in training tensor-network architectures, these results imply that one may not have to be forced to make a choice between accuracy in prediction and ensuring the privacy of the information processed.
    Differentially Private Clustering in Data Streams. (arXiv:2307.07449v1 [cs.DS])
    The streaming model is an abstraction of computing over massive data streams, which is a popular way of dealing with large-scale modern data analysis. In this model, there is a stream of data points, one after the other. A streaming algorithm is only allowed one pass over the data stream, and the goal is to perform some analysis during the stream while using as small space as possible. Clustering problems (such as $k$-means and $k$-median) are fundamental unsupervised machine learning primitives, and streaming clustering algorithms have been extensively studied in the past. However, since data privacy becomes a central concern in many real-world applications, non-private clustering algorithms are not applicable in many scenarios. In this work, we provide the first differentially private streaming algorithms for $k$-means and $k$-median clustering of $d$-dimensional Euclidean data points over a stream with length at most $T$ using $poly(k,d,\log(T))$ space to achieve a {\it constant} multiplicative error and a $poly(k,d,\log(T))$ additive error. In particular, we present a differentially private streaming clustering framework which only requires an offline DP coreset algorithm as a blackbox. By plugging in existing DP coreset results via Ghazi, Kumar, Manurangsi 2020 and Kaplan, Stemmer 2018, we achieve (1) a $(1+\gamma)$-multiplicative approximation with $\tilde{O}_\gamma(poly(k,d,\log(T)))$ space for any $\gamma>0$, and the additive error is $poly(k,d,\log(T))$ or (2) an $O(1)$-multiplicative approximation with $\tilde{O}(k \cdot poly(d,\log(T)))$ space and $poly(k,d,\log(T))$ additive error. In addition, our algorithmic framework is also differentially private under the continual release setting, i.e., the union of outputs of our algorithms at every timestamp is always differentially private.  ( 3 min )
    PseudoCal: A Source-Free Approach to Unsupervised Uncertainty Calibration in Domain Adaptation. (arXiv:2307.07489v1 [cs.LG])
    Unsupervised domain adaptation (UDA) has witnessed remarkable advancements in improving the accuracy of models for unlabeled target domains. However, the calibration of predictive uncertainty in the target domain, a crucial aspect of the safe deployment of UDA models, has received limited attention. The conventional in-domain calibration method, \textit{temperature scaling} (TempScal), encounters challenges due to domain distribution shifts and the absence of labeled target domain data. Recent approaches have employed importance-weighting techniques to estimate the target-optimal temperature based on re-weighted labeled source data. Nonetheless, these methods require source data and suffer from unreliable density estimates under severe domain shifts, rendering them unsuitable for source-free UDA settings. To overcome these limitations, we propose PseudoCal, a source-free calibration method that exclusively relies on unlabeled target data. Unlike previous approaches that treat UDA calibration as a \textit{covariate shift} problem, we consider it as an unsupervised calibration problem specific to the target domain. Motivated by the factorization of the negative log-likelihood (NLL) objective in TempScal, we generate a labeled pseudo-target set that captures the structure of the real target. By doing so, we transform the unsupervised calibration problem into a supervised one, enabling us to effectively address it using widely-used in-domain methods like TempScal. Finally, we thoroughly evaluate the calibration performance of PseudoCal by conducting extensive experiments on 10 UDA methods, considering both traditional UDA settings and recent source-free UDA scenarios. The experimental results consistently demonstrate the superior performance of PseudoCal, exhibiting significantly reduced calibration error compared to existing calibration methods.
    Deep reinforcement learning for the dynamic vehicle dispatching problem: An event-based approach. (arXiv:2307.07508v1 [cs.AI])
    The dynamic vehicle dispatching problem corresponds to deciding which vehicles to assign to requests that arise stochastically over time and space. It emerges in diverse areas, such as in the assignment of trucks to loads to be transported; in emergency systems; and in ride-hailing services. In this paper, we model the problem as a semi-Markov decision process, which allows us to treat time as continuous. In this setting, decision epochs coincide with discrete events whose time intervals are random. We argue that an event-based approach substantially reduces the combinatorial complexity of the decision space and overcomes other limitations of discrete-time models often proposed in the literature. In order to test our approach, we develop a new discrete-event simulator and use double deep q-learning to train our decision agents. Numerical experiments are carried out in realistic scenarios using data from New York City. We compare the policies obtained through our approach with heuristic policies often used in practice. Results show that our policies exhibit better average waiting times, cancellation rates and total service times, with reduction in average waiting times of up to 50% relative to the other tested heuristic policies.  ( 2 min )
    Vulnerability-Aware Instance Reweighting For Adversarial Training. (arXiv:2307.07167v1 [cs.LG])
    Adversarial Training (AT) has been found to substantially improve the robustness of deep learning classifiers against adversarial attacks. AT involves obtaining robustness by including adversarial examples in training a classifier. Most variants of AT algorithms treat every training example equally. However, recent works have shown that better performance is achievable by treating them unequally. In addition, it has been observed that AT exerts an uneven influence on different classes in a training set and unfairly hurts examples corresponding to classes that are inherently harder to classify. Consequently, various reweighting schemes have been proposed that assign unequal weights to robust losses of individual examples in a training set. In this work, we propose a novel instance-wise reweighting scheme. It considers the vulnerability of each natural example and the resulting information loss on its adversarial counterpart occasioned by adversarial attacks. Through extensive experiments, we show that our proposed method significantly improves over existing reweighting schemes, especially against strong white and black-box attacks.  ( 2 min )
    Kernel t-distributed stochastic neighbor embedding. (arXiv:2307.07081v1 [cs.LG])
    This paper presents a kernelized version of the t-SNE algorithm, capable of mapping high-dimensional data to a low-dimensional space while preserving the pairwise distances between the data points in a non-Euclidean metric. This can be achieved using a kernel trick only in the high dimensional space or in both spaces, leading to an end-to-end kernelized version. The proposed kernelized version of the t-SNE algorithm can offer new views on the relationships between data points, which can improve performance and accuracy in particular applications, such as classification problems involving kernel methods. The differences between t-SNE and its kernelized version are illustrated for several datasets, showing a neater clustering of points belonging to different classes.  ( 2 min )
    $\Phi$-DVAE: Physics-Informed Dynamical Variational Autoencoders for Unstructured Data Assimilation. (arXiv:2209.15609v2 [stat.ML] UPDATED)
    Incorporating unstructured data into physical models is a challenging problem that is emerging in data assimilation. Traditional approaches focus on well-defined observation operators whose functional forms are typically assumed to be known. This prevents these methods from achieving a consistent model-data synthesis in configurations where the mapping from data-space to model-space is unknown. To address these shortcomings, in this paper we develop a physics-informed dynamical variational autoencoder ($\Phi$-DVAE) to embed diverse data streams into time-evolving physical systems described by differential equations. Our approach combines a standard, possibly nonlinear, filter for the latent state-space model and a VAE, to assimilate the unstructured data into the latent dynamical system. Unstructured data, in our example systems, comes in the form of video data and velocity field measurements, however the methodology is suitably generic to allow for arbitrary unknown observation operators. A variational Bayesian framework is used for the joint estimation of the encoding, latent states, and unknown system parameters. To demonstrate the method, we provide case studies with the Lorenz-63 ordinary differential equation, and the advection and Korteweg-de Vries partial differential equations. Our results, with synthetic data, show that $\Phi$-DVAE provides a data efficient dynamics encoding methodology which is competitive with standard approaches. Unknown parameters are recovered with uncertainty quantification, and unseen data are accurately predicted.
    A Context-Aware Cutting Plane Selection Algorithm for Mixed-Integer Programming. (arXiv:2307.07322v1 [math.OC])
    The current cut selection algorithm used in mixed-integer programming solvers has remained largely unchanged since its creation. In this paper, we propose a set of new cut scoring measures, cut filtering techniques, and stopping criteria, extending the current state-of-the-art algorithm and obtaining a 4\% performance improvement for SCIP over the MIPLIB 2017 benchmark set.  ( 2 min )
    Benchmarks and Custom Package for Electrical Load Forecasting. (arXiv:2307.07191v1 [cs.LG])
    Load forecasting is of great significance in the power industry as it can provide a reference for subsequent tasks such as power grid dispatch, thus bringing huge economic benefits. However, there are many differences between load forecasting and traditional time series forecasting. On the one hand, load forecasting aims to minimize the cost of subsequent tasks such as power grid dispatch, rather than simply pursuing prediction accuracy. On the other hand, the load is largely influenced by many external factors, such as temperature or calendar variables. In addition, the scale of predictions (such as building-level loads and aggregated-level loads) can also significantly impact the predicted results. In this paper, we provide a comprehensive load forecasting archive, which includes load domain-specific feature engineering to help forecasting models better model load data. In addition, different from the traditional loss function which only aims for accuracy, we also provide a method to customize the loss function based on the forecasting error, integrating it into our forecasting framework. Based on this, we conducted extensive experiments on load data at different levels, providing a reference for researchers to compare different load forecasting models.  ( 2 min )
    Hybrid moderation in the newsroom: Recommending featured posts to content moderators. (arXiv:2307.07317v1 [cs.IR])
    Online news outlets are grappling with the moderation of user-generated content within their comment section. We present a recommender system based on ranking class probabilities to support and empower the moderator in choosing featured posts, a time-consuming task. By combining user and textual content features we obtain an optimal classification F1-score of 0.44 on the test set. Furthermore, we observe an optimum mean NDCG@5 of 0.87 on a large set of validation articles. As an expert evaluation, content moderators assessed the output of a random selection of articles by choosing comments to feature based on the recommendations, which resulted in a NDCG score of 0.83. We conclude that first, adding text features yields the best score and second, while choosing featured content remains somewhat subjective, content moderators found suitable comments in all but one evaluated recommendations. We end the paper by analyzing our best-performing model, a step towards transparency and explainability in hybrid content moderation.  ( 2 min )
    DataAssist: A Machine Learning Approach to Data Cleaning and Preparation. (arXiv:2307.07119v1 [cs.LG])
    Current automated machine learning (ML) tools are model-centric, focusing on model selection and parameter optimization. However, the majority of the time in data analysis is devoted to data cleaning and wrangling, for which limited tools are available. Here we present DataAssist, an automated data preparation and cleaning platform that enhances dataset quality using ML-informed methods. We show that DataAssist provides a pipeline for exploratory data analysis and data cleaning, including generating visualization for user-selected variables, unifying data annotation, suggesting anomaly removal, and preprocessing data. The exported dataset can be readily integrated with other autoML tools or user-specified model for downstream analysis. Our data-centric tool is applicable to a variety of fields, including economics, business, and forecasting applications saving over 50\% time of the time spent on data cleansing and preparation.  ( 2 min )
    Controlling dynamical systems to complex target states using machine learning: next-generation vs. classical reservoir computing. (arXiv:2307.07195v1 [cs.LG])
    Controlling nonlinear dynamical systems using machine learning allows to not only drive systems into simple behavior like periodicity but also to more complex arbitrary dynamics. For this, it is crucial that a machine learning system can be trained to reproduce the target dynamics sufficiently well. On the example of forcing a chaotic parametrization of the Lorenz system into intermittent dynamics, we show first that classical reservoir computing excels at this task. In a next step, we compare those results based on different amounts of training data to an alternative setup, where next-generation reservoir computing is used instead. It turns out that while delivering comparable performance for usual amounts of training data, next-generation RC significantly outperforms in situations where only very limited data is available. This opens even further practical control applications in real world problems where data is restricted.  ( 2 min )
    A testing-based approach to assess the clusterability of categorical data. (arXiv:2307.07346v1 [cs.LG])
    The objective of clusterability evaluation is to check whether a clustering structure exists within the data set. As a crucial yet often-overlooked issue in cluster analysis, it is essential to conduct such a test before applying any clustering algorithm. If a data set is unclusterable, any subsequent clustering analysis would not yield valid results. Despite its importance, the majority of existing studies focus on numerical data, leaving the clusterability evaluation issue for categorical data as an open problem. Here we present TestCat, a testing-based approach to assess the clusterability of categorical data in terms of an analytical $p$-value. The key idea underlying TestCat is that clusterable categorical data possess many strongly correlated attribute pairs and hence the sum of chi-squared statistics of all attribute pairs is employed as the test statistic for $p$-value calculation. We apply our method to a set of benchmark categorical data sets, showing that TestCat outperforms those solutions based on existing clusterability evaluation methods for numeric data. To the best of our knowledge, our work provides the first way to effectively recognize the clusterability of categorical data in a statistically sound manner.  ( 2 min )
    Generative adversarial networks for data-scarce spectral applications. (arXiv:2307.07454v1 [physics.optics])
    Generative adversarial networks (GANs) are one of the most robust and versatile techniques in the field of generative artificial intelligence. In this work, we report on an application of GANs in the domain of synthetic spectral data generation, offering a solution to the scarcity of data found in various scientific contexts. We demonstrate the proposed approach by applying it to an illustrative problem within the realm of near-field radiative heat transfer involving a multilayered hyperbolic metamaterial. We find that a successful generation of spectral data requires two modifications to conventional GANs: (i) the introduction of Wasserstein GANs (WGANs) to avoid mode collapse, and, (ii) the conditioning of WGANs to obtain accurate labels for the generated data. We show that a simple feed-forward neural network (FFNN), when augmented with data generated by a CWGAN, enhances significantly its performance under conditions of limited data availability, demonstrating the intrinsic value of CWGAN data augmentation beyond simply providing larger datasets. In addition, we show that CWGANs can act as a surrogate model with improved performance in the low-data regime with respect to simple FFNNs. Overall, this work highlights the potential of generative machine learning algorithms in scientific applications beyond image generation and optimization.  ( 2 min )
    Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schr\"odinger Equation. (arXiv:2307.07050v1 [physics.comp-ph])
    Solving the quantum many-body Schr\"odinger equation is a fundamental and challenging problem in the fields of quantum physics, quantum chemistry, and material sciences. One of the common computational approaches to this problem is Quantum Variational Monte Carlo (QVMC), in which ground-state solutions are obtained by minimizing the energy of the system within a restricted family of parameterized wave functions. Deep learning methods partially address the limitations of traditional QVMC by representing a rich family of wave functions in terms of neural networks. However, the optimization objective in QVMC remains notoriously hard to minimize and requires second-order optimization methods such as natural gradient. In this paper, we first reformulate energy functional minimization in the space of Born distributions corresponding to particle-permutation (anti-)symmetric wave functions, rather than the space of wave functions. We then interpret QVMC as the Fisher--Rao gradient flow in this distributional space, followed by a projection step onto the variational manifold. This perspective provides us with a principled framework to derive new QMC algorithms, by endowing the distributional space with better metrics, and following the projected gradient flow induced by those metrics. More specifically, we propose "Wasserstein Quantum Monte Carlo" (WQMC), which uses the gradient flow induced by the Wasserstein metric, rather than Fisher--Rao metric, and corresponds to transporting the probability mass, rather than teleporting it. We demonstrate empirically that the dynamics of WQMC results in faster convergence to the ground state of molecular systems.  ( 3 min )
    Enhancing ECG Analysis of Implantable Cardiac Monitor Data: An Efficient Pipeline for Multi-Label Classification. (arXiv:2307.07423v1 [eess.SP])
    Implantable Cardiac Monitor (ICM) devices are demonstrating as of today, the fastest-growing market for implantable cardiac devices. As such, they are becoming increasingly common in patients for measuring heart electrical activity. ICMs constantly monitor and record a patient's heart rhythm and when triggered - send it to a secure server where health care professionals (denote HCPs from here on) can review it. These devices employ a relatively simplistic rule-based algorithm (due to energy consumption constraints) to alert for abnormal heart rhythms. This algorithm is usually parameterized to an over-sensitive mode in order to not miss a case (resulting in relatively high false-positive rate) and this, combined with the device's nature of constantly monitoring the heart rhythm and its growing popularity, results in HCPs having to analyze and diagnose an increasingly growing amount of data. In order to reduce the load on the latter, automated methods for ECG analysis are nowadays becoming a great tool to assist HCPs in their analysis. While state-of-the-art algorithms are data-driven rather than rule-based, training data for ICMs often consist of specific characteristics which make its analysis unique and particularly challenging. This study presents the challenges and solutions in automatically analyzing ICM data and introduces a method for its classification that outperforms existing methods on such data. As such, it could be used in numerous ways such as aiding HCPs in the analysis of ECGs originating from ICMs by e.g. suggesting a rhythm type.  ( 3 min )
    Representation Learning With Hidden Unit Clustering For Low Resource Speech Applications. (arXiv:2307.07325v1 [eess.AS])
    The representation learning of speech, without textual resources, is an area of significant interest for many low resource speech applications. In this paper, we describe an approach to self-supervised representation learning from raw audio using a hidden unit clustering (HUC) framework. The input to the model consists of audio samples that are windowed and processed with 1-D convolutional layers. The learned "time-frequency" representations from the convolutional neural network (CNN) module are further processed with long short term memory (LSTM) layers which generate a contextual vector representation for every windowed segment. The HUC framework, allowing the categorization of the representations into a small number of phoneme-like units, is used to train the model for learning semantically rich speech representations. The targets consist of phoneme-like pseudo labels for each audio segment and these are generated with an iterative k-means algorithm. We explore techniques that improve the speaker invariance of the learned representations and illustrate the effectiveness of the proposed approach on two settings, i) completely unsupervised speech applications on the sub-tasks described as part of the ZeroSpeech 2021 challenge and ii) semi-supervised automatic speech recognition (ASR) applications on the TIMIT dataset and on the GramVaani challenge Hindi dataset. In these experiments, we achieve state-of-art results for various ZeroSpeech tasks. Further, on the ASR experiments, the HUC representations are shown to improve significantly over other established benchmarks based on Wav2vec, HuBERT and Best-RQ.  ( 3 min )
    Multi-Dimensional Ability Diagnosis for Machine Learning Algorithms. (arXiv:2307.07134v1 [cs.LG])
    Machine learning algorithms have become ubiquitous in a number of applications (e.g. image classification). However, due to the insufficient measurement of traditional metrics (e.g. the coarse-grained Accuracy of each classifier), substantial gaps are usually observed between the real-world performance of these algorithms and their scores in standardized evaluations. In this paper, inspired by the psychometric theories from human measurement, we propose a task-agnostic evaluation framework Camilla, where a multi-dimensional diagnostic metric Ability is defined for collaboratively measuring the multifaceted strength of each machine learning algorithm. Specifically, given the response logs from different algorithms to data samples, we leverage cognitive diagnosis assumptions and neural networks to learn the complex interactions among algorithms, samples and the skills (explicitly or implicitly pre-defined) of each sample. In this way, both the abilities of each algorithm on multiple skills and some of the sample factors (e.g. sample difficulty) can be simultaneously quantified. We conduct extensive experiments with hundreds of machine learning algorithms on four public datasets, and our experimental results demonstrate that Camilla not only can capture the pros and cons of each algorithm more precisely, but also outperforms state-of-the-art baselines on the metric reliability, rank consistency and rank stability.  ( 2 min )
    Making the Most Out of the Limited Context Length: Predictive Power Varies with Clinical Note Type and Note Section. (arXiv:2307.07051v1 [cs.CL])
    Recent advances in large language models have led to renewed interest in natural language processing in healthcare using the free text of clinical notes. One distinguishing characteristic of clinical notes is their long time span over multiple long documents. The unique structure of clinical notes creates a new design choice: when the context length for a language model predictor is limited, which part of clinical notes should we choose as the input? Existing studies either choose the inputs with domain knowledge or simply truncate them. We propose a framework to analyze the sections with high predictive power. Using MIMIC-III, we show that: 1) predictive power distribution is different between nursing notes and discharge notes and 2) combining different types of notes could improve performance when the context length is large. Our findings suggest that a carefully selected sampling function could enable more efficient information extraction from clinical notes.  ( 2 min )
    FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout. (arXiv:2307.07172v1 [cs.DC])
    Federated Learning (FL) emerges as a distributed machine learning paradigm without end-user data transmission, effectively avoiding privacy leakage. Participating devices in FL are usually bandwidth-constrained, and the uplink is much slower than the downlink in wireless networks, which causes a severe uplink communication bottleneck. A prominent direction to alleviate this problem is federated dropout, which drops fractional weights of local models. However, existing federated dropout studies focus on random or ordered dropout and lack theoretical support, resulting in unguaranteed performance. In this paper, we propose Federated learning with Bayesian Inference-based Adaptive Dropout (FedBIAD), which regards weight rows of local models as probability distributions and adaptively drops partial weight rows based on importance indicators correlated with the trend of local training loss. By applying FedBIAD, each client adaptively selects a high-quality dropping pattern with accurate approximations and only transmits parameters of non-dropped weight rows to mitigate uplink costs while improving accuracy. Theoretical analysis demonstrates that the convergence rate of the average generalization error of FedBIAD is minimax optimal up to a squared logarithmic factor. Extensive experiments on image classification and next-word prediction show that compared with status quo approaches, FedBIAD provides 2x uplink reduction with an accuracy increase of up to 2.41% even on non-Independent and Identically Distributed (non-IID) data, which brings up to 72% decrease in training time.  ( 3 min )
    Do not Mask Randomly: Effective Domain-adaptive Pre-training by Masking In-domain Keywords. (arXiv:2307.07160v1 [cs.CL])
    We propose a novel task-agnostic in-domain pre-training method that sits between generic pre-training and fine-tuning. Our approach selectively masks in-domain keywords, i.e., words that provide a compact representation of the target domain. We identify such keywords using KeyBERT (Grootendorst, 2020). We evaluate our approach using six different settings: three datasets combined with two distinct pre-trained language models (PLMs). Our results reveal that the fine-tuned PLMs adapted using our in-domain pre-training strategy outperform PLMs that used in-domain pre-training with random masking as well as those that followed the common pre-train-then-fine-tune paradigm. Further, the overhead of identifying in-domain keywords is reasonable, e.g., 7-15% of the pre-training time (for two epochs) for BERT Large (Devlin et al., 2019).  ( 2 min )
    Safe DreamerV3: Safe Reinforcement Learning with World Models. (arXiv:2307.07176v1 [cs.LG])
    The widespread application of Reinforcement Learning (RL) in real-world situations is yet to come to fruition, largely as a result of its failure to satisfy the essential safety demands of such systems. Existing safe reinforcement learning (SafeRL) methods, employing cost functions to enhance safety, fail to achieve zero-cost in complex scenarios, including vision-only tasks, even with comprehensive data sampling and training. To address this, we introduce Safe DreamerV3, a novel algorithm that integrates both Lagrangian-based and planning-based methods within a world model. Our methodology represents a significant advancement in SafeRL as the first algorithm to achieve nearly zero-cost in both low-dimensional and vision-only tasks within the Safety-Gymnasium benchmark. Our project website can be found in: https://sites.google.com/view/safedreamerv3.
    Can Large Language Models Empower Molecular Property Prediction?. (arXiv:2307.07443v1 [cs.LG])
    Molecular property prediction has gained significant attention due to its transformative potential in multiple scientific disciplines. Conventionally, a molecule graph can be represented either as a graph-structured data or a SMILES text. Recently, the rapid development of Large Language Models (LLMs) has revolutionized the field of NLP. Although it is natural to utilize LLMs to assist in understanding molecules represented by SMILES, the exploration of how LLMs will impact molecular property prediction is still in its early stage. In this work, we advance towards this objective through two perspectives: zero/few-shot molecular classification, and using the new explanations generated by LLMs as representations of molecules. To be specific, we first prompt LLMs to do in-context molecular classification and evaluate their performance. After that, we employ LLMs to generate semantically enriched explanations for the original SMILES and then leverage that to fine-tune a small-scale LM model for multiple downstream tasks. The experimental results highlight the superiority of text explanations as molecular representations across multiple benchmark datasets, and confirm the immense potential of LLMs in molecular property prediction tasks. Codes are available at \url{https://github.com/ChnQ/LLM4Mol}.
    A decision framework for selecting information-transfer strategies in population-based SHM. (arXiv:2307.06978v1 [cs.LG])
    Decision-support for the operation and maintenance of structures provides significant motivation for the development and implementation of structural health monitoring (SHM) systems. Unfortunately, the limited availability of labelled training data hinders the development of the statistical models on which these decision-support systems rely. Population-based SHM seeks to mitigate the impact of data scarcity by using transfer learning techniques to share information between individual structures within a population. The current paper proposes a decision framework for selecting transfer strategies based upon a novel concept -- the expected value of information transfer -- such that negative transfer is avoided. By avoiding negative transfer, and by optimising information transfer strategies using the transfer-decision framework, one can reduce the costs associated with operating and maintaining structures, and improve safety.
    Variance-reduced accelerated methods for decentralized stochastic double-regularized nonconvex strongly-concave minimax problems. (arXiv:2307.07113v1 [math.OC])
    In this paper, we consider the decentralized, stochastic nonconvex strongly-concave (NCSC) minimax problem with nonsmooth regularization terms on both primal and dual variables, wherein a network of $m$ computing agents collaborate via peer-to-peer communications. We consider when the coupling function is in expectation or finite-sum form and the double regularizers are convex functions, applied separately to the primal and dual variables. Our algorithmic framework introduces a Lagrangian multiplier to eliminate the consensus constraint on the dual variable. Coupling this with variance-reduction (VR) techniques, our proposed method, entitled VRLM, by a single neighbor communication per iteration, is able to achieve an $\mathcal{O}(\kappa^3\varepsilon^{-3})$ sample complexity under the general stochastic setting, with either a big-batch or small-batch VR option, where $\kappa$ is the condition number of the problem and $\varepsilon$ is the desired solution accuracy. With a big-batch VR, we can additionally achieve $\mathcal{O}(\kappa^2\varepsilon^{-2})$ communication complexity. Under the special finite-sum setting, our method with a big-batch VR can achieve an $\mathcal{O}(n + \sqrt{n} \kappa^2\varepsilon^{-2})$ sample complexity and $\mathcal{O}(\kappa^2\varepsilon^{-2})$ communication complexity, where $n$ is the number of components in the finite sum. All complexity results match the best-known results achieved by a few existing methods for solving special cases of the problem we consider. To the best of our knowledge, this is the first work which provides convergence guarantees for NCSC minimax problems with general convex nonsmooth regularizers applied to both the primal and dual variables in the decentralized stochastic setting. Numerical experiments are conducted on two machine learning problems. Our code is downloadable from https://github.com/RPI-OPT/VRLM.
    Is Task-Agnostic Explainable AI a Myth?. (arXiv:2307.06963v1 [cs.AI])
    Our work serves as a framework for unifying the challenges of contemporary explainable AI (XAI). We demonstrate that while XAI methods provide supplementary and potentially useful output for machine learning models, researchers and decision-makers should be mindful of their conceptual and technical limitations, which frequently result in these methods themselves becoming black boxes. We examine three XAI research avenues spanning image, textual, and graph data, covering saliency, attention, and graph-type explainers. Despite the varying contexts and timeframes of the mentioned cases, the same persistent roadblocks emerge, highlighting the need for a conceptual breakthrough in the field to address the challenge of compatibility between XAI methods and application tasks.
    Frequency Domain Adversarial Training for Robust Volumetric Medical Segmentation. (arXiv:2307.07269v1 [eess.IV])
    It is imperative to ensure the robustness of deep learning models in critical applications such as, healthcare. While recent advances in deep learning have improved the performance of volumetric medical image segmentation models, these models cannot be deployed for real-world applications immediately due to their vulnerability to adversarial attacks. We present a 3D frequency domain adversarial attack for volumetric medical image segmentation models and demonstrate its advantages over conventional input or voxel domain attacks. Using our proposed attack, we introduce a novel frequency domain adversarial training approach for optimizing a robust model against voxel and frequency domain attacks. Moreover, we propose frequency consistency loss to regulate our frequency domain adversarial training that achieves a better tradeoff between model's performance on clean and adversarial samples. Code is publicly available at https://github.com/asif-hanif/vafa.
    Solving higher-order Lane-Emden-Fowler type equations using physics-informed neural networks: benchmark tests comparing soft and hard constraints. (arXiv:2307.07302v1 [physics.comp-ph])
    In this paper, numerical methods using Physics-Informed Neural Networks (PINNs) are presented with the aim to solve higher-order ordinary differential equations (ODEs). Indeed, this deep-learning technique is successfully applied for solving different classes of singular ODEs, namely the well known second-order Lane-Emden equations, third order-order Emden-Fowler equations, and fourth-order Lane-Emden-Fowler equations. Two variants of PINNs technique are considered and compared. First, a minimization procedure is used to constrain the total loss function of the neural network, in which the equation residual is considered with some weight to form a physics-based loss and added to the training data loss that contains the initial/boundary conditions. Second, a specific choice of trial solutions ensuring these conditions as hard constraints is done in order to satisfy the differential equation, contrary to the first variant based on training data where the constraints appear as soft ones. Advantages and drawbacks of PINNs variants are highlighted.
    Mitigating Adversarial Vulnerability through Causal Parameter Estimation by Adversarial Double Machine Learning. (arXiv:2307.07250v1 [cs.LG])
    Adversarial examples derived from deliberately crafted perturbations on visual inputs can easily harm decision process of deep neural networks. To prevent potential threats, various adversarial training-based defense methods have grown rapidly and become a de facto standard approach for robustness. Despite recent competitive achievements, we observe that adversarial vulnerability varies across targets and certain vulnerabilities remain prevalent. Intriguingly, such peculiar phenomenon cannot be relieved even with deeper architectures and advanced defense methods. To address this issue, in this paper, we introduce a causal approach called Adversarial Double Machine Learning (ADML), which allows us to quantify the degree of adversarial vulnerability for network predictions and capture the effect of treatments on outcome of interests. ADML can directly estimate causal parameter of adversarial perturbations per se and mitigate negative effects that can potentially damage robustness, bridging a causal perspective into the adversarial vulnerability. Through extensive experiments on various CNN and Transformer architectures, we corroborate that ADML improves adversarial robustness with large margins and relieve the empirical observation.
    Inverse Evolution Layers: Physics-informed Regularizers for Deep Neural Networks. (arXiv:2307.07344v1 [cs.LG])
    This paper proposes a novel approach to integrating partial differential equation (PDE)-based evolution models into neural networks through a new type of regularization. Specifically, we propose inverse evolution layers (IELs) based on evolution equations. These layers can achieve specific regularization objectives and endow neural networks' outputs with corresponding properties of the evolution models. Moreover, IELs are straightforward to construct and implement, and can be easily designed for various physical evolutions and neural networks. Additionally, the design process for these layers can provide neural networks with intuitive and mathematical interpretability, thus enhancing the transparency and explainability of the approach. To demonstrate the effectiveness, efficiency, and simplicity of our approach, we present an example of endowing semantic segmentation models with the smoothness property based on the heat diffusion model. To achieve this goal, we design heat-diffusion IELs and apply them to address the challenge of semantic segmentation with noisy labels. The experimental results demonstrate that the heat-diffusion IELs can effectively mitigate the overfitting problem caused by noisy labels.
    Knowledge Boosting: Rethinking Medical Contrastive Vision-Language Pre-Training. (arXiv:2307.07246v1 [cs.CV])
    The foundation models based on pre-training technology have significantly advanced artificial intelligence from theoretical to practical applications. These models have facilitated the feasibility of computer-aided diagnosis for widespread use. Medical contrastive vision-language pre-training, which does not require human annotations, is an effective approach for guiding representation learning using description information in diagnostic reports. However, the effectiveness of pre-training is limited by the large-scale semantic overlap and shifting problems in medical field. To address these issues, we propose the Knowledge-Boosting Contrastive Vision-Language Pre-training framework (KoBo), which integrates clinical knowledge into the learning of vision-language semantic consistency. The framework uses an unbiased, open-set sample-wise knowledge representation to measure negative sample noise and supplement the correspondence between vision-language mutual information and clinical knowledge. Extensive experiments validate the effect of our framework on eight tasks including classification, segmentation, retrieval, and semantic relatedness, achieving comparable or better performance with the zero-shot or few-shot settings. Our code is open on https://github.com/ChenXiaoFei-CS/KoBo.
    Visualizing Overlapping Biclusterings and Boolean Matrix Factorizations. (arXiv:2307.07396v1 [cs.LG])
    Finding (bi-)clusters in bipartite graphs is a popular data analysis approach. Analysts typically want to visualize the clusters, which is simple as long as the clusters are disjoint. However, many modern algorithms find overlapping clusters, making visualization more complicated. In this paper, we study the problem of visualizing \emph{a given clustering} of overlapping clusters in bipartite graphs and the related problem of visualizing Boolean Matrix Factorizations. We conceptualize three different objectives that any good visualization should satisfy: (1) proximity of cluster elements, (2) large consecutive areas of elements from the same cluster, and (3) large uninterrupted areas in the visualization, regardless of the cluster membership. We provide objective functions that capture these goals and algorithms that optimize these objective functions. Interestingly, in experiments on real-world datasets, we find that the best trade-off between these competing goals is achieved by a novel heuristic, which locally aims to place rows and columns with similar cluster membership next to each other.
    3D Shape-Based Myocardial Infarction Prediction Using Point Cloud Classification Networks. (arXiv:2307.07298v1 [cs.CV])
    Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases with associated clinical decision-making typically based on single-valued imaging biomarkers. However, such metrics only approximate the complex 3D structure and physiology of the heart and hence hinder a better understanding and prediction of MI outcomes. In this work, we investigate the utility of complete 3D cardiac shapes in the form of point clouds for an improved detection of MI events. To this end, we propose a fully automatic multi-step pipeline consisting of a 3D cardiac surface reconstruction step followed by a point cloud classification network. Our method utilizes recent advances in geometric deep learning on point clouds to enable direct and efficient multi-scale learning on high-resolution surface models of the cardiac anatomy. We evaluate our approach on 1068 UK Biobank subjects for the tasks of prevalent MI detection and incident MI prediction and find improvements of ~13% and ~5% respectively over clinical benchmarks. Furthermore, we analyze the role of each ventricle and cardiac phase for 3D shape-based MI detection and conduct a visual analysis of the morphological and physiological patterns typically associated with MI outcomes.
    How Different Is Stereotypical Bias Across Languages?. (arXiv:2307.07331v1 [cs.CL])
    Recent studies have demonstrated how to assess the stereotypical bias in pre-trained English language models. In this work, we extend this branch of research in multiple different dimensions by systematically investigating (a) mono- and multilingual models of (b) different underlying architectures with respect to their bias in (c) multiple different languages. To that end, we make use of the English StereoSet data set (Nadeem et al., 2021), which we semi-automatically translate into German, French, Spanish, and Turkish. We find that it is of major importance to conduct this type of analysis in a multilingual setting, as our experiments show a much more nuanced picture as well as notable differences from the English-only analysis. The main takeaways from our analysis are that mGPT-2 (partly) shows surprising anti-stereotypical behavior across languages, English (monolingual) models exhibit the strongest bias, and the stereotypes reflected in the data set are least present in Turkish models. Finally, we release our codebase alongside the translated data sets and practical guidelines for the semi-automatic translation to encourage a further extension of our work to other languages.
    Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement. (arXiv:2307.07055v1 [cs.LG])
    We explore the methodology and theory of reward-directed generation via conditional diffusion models. Directed generation aims to generate samples with desired properties as measured by a reward function, which has broad applications in generative AI, reinforcement learning, and computational biology. We consider the common learning scenario where the data set consists of unlabeled data along with a smaller set of data with noisy reward labels. Our approach leverages a learned reward function on the smaller data set as a pseudolabeler. From a theoretical standpoint, we show that this directed generator can effectively learn and sample from the reward-conditioned data distribution. Additionally, our model is capable of recovering the latent subspace representation of data. Moreover, we establish that the model generates a new population that moves closer to a user-specified target reward value, where the optimality gap aligns with the off-policy bandit regret in the feature subspace. The improvement in rewards obtained is influenced by the interplay between the strength of the reward signal, the distribution shift, and the cost of off-support extrapolation. We provide empirical results to validate our theory and highlight the relationship between the strength of extrapolation and the quality of generated samples.
    Graph Positional and Structural Encoder. (arXiv:2307.07107v1 [cs.LG])
    Positional and structural encodings (PSE) enable better identifiability of nodes within a graph, as in general graphs lack a canonical node ordering. This renders PSEs essential tools for empowering modern GNNs, and in particular graph Transformers. However, designing PSEs that work optimally for a variety of graph prediction tasks is a challenging and unsolved problem. Here, we present the graph positional and structural encoder (GPSE), a first-ever attempt to train a graph encoder that captures rich PSE representations for augmenting any GNN. GPSE can effectively learn a common latent representation for multiple PSEs, and is highly transferable. The encoder trained on a particular graph dataset can be used effectively on datasets drawn from significantly different distributions and even modalities. We show that across a wide range of benchmarks, GPSE-enhanced models can significantly improve the performance in certain tasks, while performing on par with those that employ explicitly computed PSEs in other cases. Our results pave the way for the development of large pre-trained models for extracting graph positional and structural information and highlight their potential as a viable alternative to explicitly computed PSEs as well as to existing self-supervised pre-training approaches.
    Performance of $\ell_1$ Regularization for Sparse Convex Optimization. (arXiv:2307.07405v1 [cs.LG])
    Despite widespread adoption in practice, guarantees for the LASSO and Group LASSO are strikingly lacking in settings beyond statistical problems, and these algorithms are usually considered to be a heuristic in the context of sparse convex optimization on deterministic inputs. We give the first recovery guarantees for the Group LASSO for sparse convex optimization with vector-valued features. We show that if a sufficiently large Group LASSO regularization is applied when minimizing a strictly convex function $l$, then the minimizer is a sparse vector supported on vector-valued features with the largest $\ell_2$ norm of the gradient. Thus, repeating this procedure selects the same set of features as the Orthogonal Matching Pursuit algorithm, which admits recovery guarantees for any function $l$ with restricted strong convexity and smoothness via weak submodularity arguments. This answers open questions of Tibshirani et al. and Yasuda et al. Our result is the first to theoretically explain the empirical success of the Group LASSO for convex functions under general input instances assuming only restricted strong convexity and smoothness. Our result also generalizes provable guarantees for the Sequential Attention algorithm, which is a feature selection algorithm inspired by the attention mechanism proposed by Yasuda et al. As an application of our result, we give new results for the column subset selection problem, which is well-studied when the loss is the Frobenius norm or other entrywise matrix losses. We give the first result for general loss functions for this problem that requires only restricted strong convexity and smoothness.
    Expressive Monotonic Neural Networks. (arXiv:2307.07512v1 [cs.LG])
    The monotonic dependence of the outputs of a neural network on some of its inputs is a crucial inductive bias in many scenarios where domain knowledge dictates such behavior. This is especially important for interpretability and fairness considerations. In a broader context, scenarios in which monotonicity is important can be found in finance, medicine, physics, and other disciplines. It is thus desirable to build neural network architectures that implement this inductive bias provably. In this work, we propose a weight-constrained architecture with a single residual connection to achieve exact monotonic dependence in any subset of the inputs. The weight constraint scheme directly controls the Lipschitz constant of the neural network and thus provides the additional benefit of robustness. Compared to currently existing techniques used for monotonicity, our method is simpler in implementation and in theory foundations, has negligible computational overhead, is guaranteed to produce monotonic dependence, and is highly expressive. We show how the algorithm is used to train powerful, robust, and interpretable discriminators that achieve competitive performance compared to current state-of-the-art methods across various benchmarks, from social applications to the classification of the decays of subatomic particles produced at the CERN Large Hadron Collider.
    Machine Learning-Assisted Pattern Recognition Algorithms for Estimating Ultimate Tensile Strength in Fused Deposition Modeled Polylactic Acid Specimens. (arXiv:2307.06970v1 [cs.LG])
    In this study, we investigate the application of supervised machine learning algorithms for estimating the Ultimate Tensile Strength (UTS) of Polylactic Acid (PLA) specimens fabricated using the Fused Deposition Modeling (FDM) process. A total of 31 PLA specimens were prepared, with Infill Percentage, Layer Height, Print Speed, and Extrusion Temperature serving as input parameters. The primary objective was to assess the accuracy and effectiveness of four distinct supervised classification algorithms, namely Logistic Classification, Gradient Boosting Classification, Decision Tree, and K-Nearest Neighbor, in predicting the UTS of the specimens. The results revealed that while the Decision Tree and K-Nearest Neighbor algorithms both achieved an F1 score of 0.71, the KNN algorithm exhibited a higher Area Under the Curve (AUC) score of 0.79, outperforming the other algorithms. This demonstrates the superior ability of the KNN algorithm in differentiating between the two classes of ultimate tensile strength within the dataset, rendering it the most favorable choice for classification in the context of this research. This study represents the first attempt to estimate the UTS of PLA specimens using machine learning-based classification algorithms, and the findings offer valuable insights into the potential of these techniques in improving the performance and accuracy of predictive models in the domain of additive manufacturing.
    On Interpolating Experts and Multi-Armed Bandits. (arXiv:2307.07264v1 [cs.LG])
    Learning with expert advice and multi-armed bandit are two classic online decision problems which differ on how the information is observed in each round of the game. We study a family of problems interpolating the two. For a vector $\mathbf{m}=(m_1,\dots,m_K)\in \mathbb{N}^K$, an instance of $\mathbf{m}$-MAB indicates that the arms are partitioned into $K$ groups and the $i$-th group contains $m_i$ arms. Once an arm is pulled, the losses of all arms in the same group are observed. We prove tight minimax regret bounds for $\mathbf{m}$-MAB and design an optimal PAC algorithm for its pure exploration version, $\mathbf{m}$-BAI, where the goal is to identify the arm with minimum loss with as few rounds as possible. We show that the minimax regret of $\mathbf{m}$-MAB is $\Theta\left(\sqrt{T\sum_{k=1}^K\log (m_k+1)}\right)$ and the minimum number of pulls for an $(\epsilon,0.05)$-PAC algorithm of $\mathbf{m}$-BAI is $\Theta\left(\frac{1}{\epsilon^2}\cdot \sum_{k=1}^K\log (m_k+1)\right)$. Both our upper bounds and lower bounds for $\mathbf{m}$-MAB can be extended to a more general setting, namely the bandit with graph feedback, in terms of the clique cover and related graph parameters. As consequences, we obtained tight minimax regret bounds for several families of feedback graphs.
    Pathway toward prior knowledge-integrated machine learning in engineering. (arXiv:2307.06950v1 [cs.AI])
    Despite the digitalization trend and data volume surge, first-principles models (also known as logic-driven, physics-based, rule-based, or knowledge-based models) and data-driven approaches have existed in parallel, mirroring the ongoing AI debate on symbolism versus connectionism. Research for process development to integrate both sides to transfer and utilize domain knowledge in the data-driven process is rare. This study emphasizes efforts and prevailing trends to integrate multidisciplinary domain professions into machine acknowledgeable, data-driven processes in a two-fold organization: examining information uncertainty sources in knowledge representation and exploring knowledge decomposition with a three-tier knowledge-integrated machine learning paradigm. This approach balances holist and reductionist perspectives in the engineering domain.
    AI For Global Climate Cooperation 2023 Competition Proceedings. (arXiv:2307.06951v1 [cs.AI])
    The international community must collaborate to mitigate climate change and sustain economic growth. However, collaboration is hard to achieve, partly because no global authority can ensure compliance with international climate agreements. Combining AI with climate-economic simulations offers a promising solution to design international frameworks, including negotiation protocols and climate agreements, that promote and incentivize collaboration. In addition, these frameworks should also have policy goals fulfillment, and sustained commitment, taking into account climate-economic dynamics and strategic behaviors. These challenges require an interdisciplinary approach across machine learning, economics, climate science, law, policy, ethics, and other fields. Towards this objective, we organized AI for Global Climate Cooperation, a Mila competition in which teams submitted proposals and analyses of international frameworks, based on (modifications of) RICE-N, an AI-driven integrated assessment model (IAM). In particular, RICE-N supports modeling regional decision-making using AI agents. Furthermore, the IAM then models the climate-economic impact of those decisions into the future. Whereas the first track focused only on performance metrics, the proposals submitted to the second track were evaluated both quantitatively and qualitatively. The quantitative evaluation focused on a combination of (i) the degree of mitigation of global temperature rise and (ii) the increase in economic productivity. On the other hand, an interdisciplinary panel of human experts in law, policy, sociology, economics and environmental science, evaluated the solutions qualitatively. In particular, the panel considered the effectiveness, simplicity, feasibility, ethics, and notions of climate justice of the protocols. In the third track, the participants were asked to critique and improve RICE-N.
    Learning Sparse Neural Networks with Identity Layers. (arXiv:2307.07389v1 [cs.LG])
    The sparsity of Deep Neural Networks is well investigated to maximize the performance and reduce the size of overparameterized networks as possible. Existing methods focus on pruning parameters in the training process by using thresholds and metrics. Meanwhile, feature similarity between different layers has not been discussed sufficiently before, which could be rigorously proved to be highly correlated to the network sparsity in this paper. Inspired by interlayer feature similarity in overparameterized models, we investigate the intrinsic link between network sparsity and interlayer feature similarity. Specifically, we prove that reducing interlayer feature similarity based on Centered Kernel Alignment (CKA) improves the sparsity of the network by using information bottleneck theory. Applying such theory, we propose a plug-and-play CKA-based Sparsity Regularization for sparse network training, dubbed CKA-SR, which utilizes CKA to reduce feature similarity between layers and increase network sparsity. In other words, layers of our sparse network tend to have their own identity compared to each other. Experimentally, we plug the proposed CKA-SR into the training process of sparse network training methods and find that CKA-SR consistently improves the performance of several State-Of-The-Art sparse training methods, especially at extremely high sparsity. Code is included in the supplementary materials.
    AIC-AB NET: A Neural Network for Image Captioning with Spatial Attention and Text Attributes. (arXiv:2307.07370v1 [cs.CV])
    Image captioning is a significant field across computer vision and natural language processing. We propose and present AIC-AB NET, a novel Attribute-Information-Combined Attention-Based Network that combines spatial attention architecture and text attributes in an encoder-decoder. For caption generation, adaptive spatial attention determines which image region best represents the image and whether to attend to the visual features or the visual sentinel. Text attribute information is synchronously fed into the decoder to help image recognition and reduce uncertainty. We have tested and evaluated our AICAB NET on the MS COCO dataset and a new proposed Fashion dataset. The Fashion dataset is employed as a benchmark of single-object images. The results show the superior performance of the proposed model compared to the state-of-the-art baseline and ablated models on both the images from MSCOCO and our single-object images. Our AIC-AB NET outperforms the baseline adaptive attention network by 0.017 (CIDEr score) on the MS COCO dataset and 0.095 (CIDEr score) on the Fashion dataset.
    Higher-order topological kernels via quantum computation. (arXiv:2307.07383v1 [quant-ph])
    Topological data analysis (TDA) has emerged as a powerful tool for extracting meaningful insights from complex data. TDA enhances the analysis of objects by embedding them into a simplicial complex and extracting useful global properties such as the Betti numbers, i.e. the number of multidimensional holes, which can be used to define kernel methods that are easily integrated with existing machine-learning algorithms. These kernel methods have found broad applications, as they rely on powerful mathematical frameworks which provide theoretical guarantees on their performance. However, the computation of higher-dimensional Betti numbers can be prohibitively expensive on classical hardware, while quantum algorithms can approximate them in polynomial time in the instance size. In this work, we propose a quantum approach to defining topological kernels, which is based on constructing Betti curves, i.e. topological fingerprint of filtrations with increasing order. We exhibit a working prototype of our approach implemented on a noiseless simulator and show its robustness by means of some empirical results suggesting that topological approaches may offer an advantage in quantum machine learning.
    LINFA: a Python library for variational inference with normalizing flow and annealing. (arXiv:2307.04675v2 [cs.LG] UPDATED)
    Variational inference is an increasingly popular method in statistics and machine learning for approximating probability distributions. We developed LINFA (Library for Inference with Normalizing Flow and Annealing), a Python library for variational inference to accommodate computationally expensive models and difficult-to-sample distributions with dependent parameters. We discuss the theoretical background, capabilities, and performance of LINFA in various benchmarks. LINFA is publicly available on GitHub at https://github.com/desResLab/LINFA.
    MGit: A Model Versioning and Management System. (arXiv:2307.07507v1 [cs.LG])
    Models derived from other models are extremely common in machine learning (ML) today. For example, transfer learning is used to create task-specific models from "pre-trained" models through finetuning. This has led to an ecosystem where models are related to each other, sharing structure and often even parameter values. However, it is hard to manage these model derivatives: the storage overhead of storing all derived models quickly becomes onerous, prompting users to get rid of intermediate models that might be useful for further analysis. Additionally, undesired behaviors in models are hard to track down (e.g., is a bug inherited from an upstream model?). In this paper, we propose a model versioning and management system called MGit that makes it easier to store, test, update, and collaborate on model derivatives. MGit introduces a lineage graph that records provenance and versioning information between models, optimizations to efficiently store model parameters, as well as abstractions over this lineage graph that facilitate relevant testing, updating and collaboration functionality. MGit is able to reduce the lineage graph's storage footprint by up to 7x and automatically update downstream models in response to updates to upstream models.
    Signed iterative random forests to identify enhancer-associated transcription factor binding. (arXiv:1810.07287v2 [stat.ML] UPDATED)
    Standard ChIP-seq peak calling pipelines seek to differentiate biochemically reproducible signals of individual genomic elements from background noise. However, reproducibility alone does not imply functional regulation (e.g., enhancer activation, alternative splicing). Here we present a general-purpose, interpretable machine learning method: signed iterative random forests (siRF), which we use to infer regulatory interactions among transcription factors and functional binding signatures surrounding enhancer elements in Drosophila melanogaster.
    HuCurl: Human-induced Curriculum Discovery. (arXiv:2307.07412v1 [cs.LG])
    We introduce the problem of curriculum discovery and describe a curriculum learning framework capable of discovering effective curricula in a curriculum space based on prior knowledge about sample difficulty. Using annotation entropy and loss as measures of difficulty, we show that (i): the top-performing discovered curricula for a given model and dataset are often non-monotonic as opposed to monotonic curricula in existing literature, (ii): the prevailing easy-to-hard or hard-to-easy transition curricula are often at the risk of underperforming, and (iii): the curricula discovered for smaller datasets and models perform well on larger datasets and models respectively. The proposed framework encompasses some of the existing curriculum learning approaches and can discover curricula that outperform them across several NLP tasks.
    Boosting Backdoor Attack with A Learnable Poisoning Sample Selection Strategy. (arXiv:2307.07328v1 [cs.CR])
    Data-poisoning based backdoor attacks aim to insert backdoor into models by manipulating training datasets without controlling the training process of the target model. Existing attack methods mainly focus on designing triggers or fusion strategies between triggers and benign samples. However, they often randomly select samples to be poisoned, disregarding the varying importance of each poisoning sample in terms of backdoor injection. A recent selection strategy filters a fixed-size poisoning sample pool by recording forgetting events, but it fails to consider the remaining samples outside the pool from a global perspective. Moreover, computing forgetting events requires significant additional computing resources. Therefore, how to efficiently and effectively select poisoning samples from the entire dataset is an urgent problem in backdoor attacks.To address it, firstly, we introduce a poisoning mask into the regular backdoor training loss. We suppose that a backdoored model training with hard poisoning samples has a more backdoor effect on easy ones, which can be implemented by hindering the normal training process (\ie, maximizing loss \wrt mask). To further integrate it with normal training process, we then propose a learnable poisoning sample selection strategy to learn the mask together with the model parameters through a min-max optimization.Specifically, the outer loop aims to achieve the backdoor attack goal by minimizing the loss based on the selected samples, while the inner loop selects hard poisoning samples that impede this goal by maximizing the loss. After several rounds of adversarial training, we finally select effective poisoning samples with high contribution. Extensive experiments on benchmark datasets demonstrate the effectiveness and efficiency of our approach in boosting backdoor attack performance.
    Defect Classification in Additive Manufacturing Using CNN-Based Vision Processing. (arXiv:2307.07378v1 [cs.CV])
    The development of computer vision and in-situ monitoring using visual sensors allows the collection of large datasets from the additive manufacturing (AM) process. Such datasets could be used with machine learning techniques to improve the quality of AM. This paper examines two scenarios: first, using convolutional neural networks (CNNs) to accurately classify defects in an image dataset from AM and second, applying active learning techniques to the developed classification model. This allows the construction of a human-in-the-loop mechanism to reduce the size of the data required to train and generate training data.
    Controllable Emphasis with zero data for text-to-speech. (arXiv:2307.07062v1 [eess.AS])
    We present a scalable method to produce high quality emphasis for text-to-speech (TTS) that does not require recordings or annotations. Many TTS models include a phoneme duration model. A simple but effective method to achieve emphasized speech consists in increasing the predicted duration of the emphasised word. We show that this is significantly better than spectrogram modification techniques improving naturalness by $7.3\%$ and correct testers' identification of the emphasized word in a sentence by $40\%$ on a reference female en-US voice. We show that this technique significantly closes the gap to methods that require explicit recordings. The method proved to be scalable and preferred in all four languages tested (English, Spanish, Italian, German), for different voices and multiple speaking styles.
    Improving Zero-Shot Generalization for CLIP with Synthesized Prompts. (arXiv:2307.07397v1 [cs.CV])
    With the growing interest in pretrained vision-language models like CLIP, recent research has focused on adapting these models to downstream tasks. Despite achieving promising results, most existing methods require labeled data for all classes, which may not hold in real-world applications due to the long tail and Zipf's law. For example, some classes may lack labeled data entirely, such as emerging concepts. To address this problem, we propose a plug-and-play generative approach called \textbf{S}ynt\textbf{H}es\textbf{I}zed \textbf{P}rompts~(\textbf{SHIP}) to improve existing fine-tuning methods. Specifically, we follow variational autoencoders to introduce a generator that reconstructs the visual features by inputting the synthesized prompts and the corresponding class names to the textual encoder of CLIP. In this manner, we easily obtain the synthesized features for the remaining label-only classes. Thereafter, we fine-tune CLIP with off-the-shelf methods by combining labeled and synthesized features. Extensive experiments on base-to-new generalization, cross-dataset transfer learning, and generalized zero-shot learning demonstrate the superiority of our approach. The code is available at \url{https://github.com/mrflogs/SHIP}.
    Atlas-Based Interpretable Age Prediction. (arXiv:2307.07439v1 [eess.IV])
    Age prediction is an important part of medical assessments and research. It can aid in detecting diseases as well as abnormal ageing by highlighting the discrepancy between chronological and biological age. To gain a comprehensive understanding of age-related changes observed in various body parts, we investigate them on a larger scale by using whole-body images. We utilise the Grad-CAM interpretability method to determine the body areas most predictive of a person's age. We expand our analysis beyond individual subjects by employing registration techniques to generate population-wide interpretability maps. Furthermore, we set state-of-the-art whole-body age prediction with a model that achieves a mean absolute error of 2.76 years. Our findings reveal three primary areas of interest: the spine, the autochthonous back muscles, and the cardiac region, which exhibits the highest importance.
    Data Augmentation for Mathematical Objects. (arXiv:2307.06984v1 [cs.SC])
    This paper discusses and evaluates ideas of data balancing and data augmentation in the context of mathematical objects: an important topic for both the symbolic computation and satisfiability checking communities, when they are making use of machine learning techniques to optimise their tools. We consider a dataset of non-linear polynomial problems and the problem of selecting a variable ordering for cylindrical algebraic decomposition to tackle these with. By swapping the variable names in already labelled problems, we generate new problem instances that do not require any further labelling when viewing the selection as a classification problem. We find this augmentation increases the accuracy of ML models by 63% on average. We study what part of this improvement is due to the balancing of the dataset and what is achieved thanks to further increasing the size of the dataset, concluding that both have a very significant effect. We finish the paper by reflecting on how this idea could be applied in other uses of machine learning in mathematics.
    Population Expansion for Training Language Models with Private Federated Learning. (arXiv:2307.07477v1 [cs.LG])
    Federated learning (FL) combined with differential privacy (DP) offers machine learning (ML) training with distributed devices and with a formal privacy guarantee. With a large population of devices, FL with DP produces a performant model in a timely manner. However, for applications with a smaller population, not only does the model utility degrade as the DP noise is inversely proportional to population, but also the training latency increases since waiting for enough clients to become available from a smaller pool is slower. In this work, we thus propose expanding the population based on domain adaptation techniques to speed up the training and improves the final model quality when training with small populations. We empirically demonstrate that our techniques can improve the utility by 13% to 30% on real-world language modeling datasets.
    Improved Convergence Analysis and SNR Control Strategies for Federated Learning in the Presence of Noise. (arXiv:2307.07406v1 [cs.LG])
    We propose an improved convergence analysis technique that characterizes the distributed learning paradigm of federated learning (FL) with imperfect/noisy uplink and downlink communications. Such imperfect communication scenarios arise in the practical deployment of FL in emerging communication systems and protocols. The analysis developed in this paper demonstrates, for the first time, that there is an asymmetry in the detrimental effects of uplink and downlink communications in FL. In particular, the adverse effect of the downlink noise is more severe on the convergence of FL algorithms. Using this insight, we propose improved Signal-to-Noise (SNR) control strategies that, discarding the negligible higher-order terms, lead to a similar convergence rate for FL as in the case of a perfect, noise-free communication channel while incurring significantly less power resources compared to existing solutions. In particular, we establish that to maintain the $O(\frac{1}{\sqrt{K}})$ rate of convergence like in the case of noise-free FL, we need to scale down the uplink and downlink noise by $\Omega({\sqrt{k}})$ and $\Omega({k})$ respectively, where $k$ denotes the communication round, $k=1,\dots, K$. Our theoretical result is further characterized by two major benefits: firstly, it does not assume the somewhat unrealistic assumption of bounded client dissimilarity, and secondly, it only requires smooth non-convex loss functions, a function class better suited for modern machine learning and deep learning models. We also perform extensive empirical analysis to verify the validity of our theoretical findings.
    Embracing the chaos: analysis and diagnosis of numerical instability in variational flows. (arXiv:2307.06957v1 [stat.ML])
    In this paper, we investigate the impact of numerical instability on the reliability of sampling, density evaluation, and evidence lower bound (ELBO) estimation in variational flows. We first empirically demonstrate that common flows can exhibit a catastrophic accumulation of error: the numerical flow map deviates significantly from the exact map -- which affects sampling -- and the numerical inverse flow map does not accurately recover the initial input -- which affects density and ELBO computations. Surprisingly though, we find that results produced by flows are often accurate enough for applications despite the presence of serious numerical instability. In this work, we treat variational flows as dynamical systems, and leverage shadowing theory to elucidate this behavior via theoretical guarantees on the error of sampling, density evaluation, and ELBO estimation. Finally, we develop and empirically test a diagnostic procedure that can be used to validate results produced by numerically unstable flows in practice.
    Rician likelihood loss for quantitative MRI using self-supervised deep learning. (arXiv:2307.07072v1 [cs.LG])
    Purpose: Previous quantitative MR imaging studies using self-supervised deep learning have reported biased parameter estimates at low SNR. Such systematic errors arise from the choice of Mean Squared Error (MSE) loss function for network training, which is incompatible with Rician-distributed MR magnitude signals. To address this issue, we introduce the negative log Rician likelihood (NLR) loss. Methods: A numerically stable and accurate implementation of the NLR loss was developed to estimate quantitative parameters of the apparent diffusion coefficient (ADC) model and intra-voxel incoherent motion (IVIM) model. Parameter estimation accuracy, precision and overall error were evaluated in terms of bias, variance and root mean squared error and compared against the MSE loss over a range of SNRs (5 - 30). Results: Networks trained with NLR loss show higher estimation accuracy than MSE for the ADC and IVIM diffusion coefficients as SNR decreases, with minimal loss of precision or total error. At high effective SNR (high SNR and small diffusion coefficients), both losses show comparable accuracy and precision for all parameters of both models. Conclusion: The proposed NLR loss is numerically stable and accurate across the full range of tested SNRs and improves parameter estimation accuracy of diffusion coefficients using self-supervised deep learning. We expect the development to benefit quantitative MR imaging techniques broadly, enabling more accurate parameter estimation from noisy data.
    Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning. (arXiv:2303.09032v2 [cs.LG] UPDATED)
    Efficient exploration is critical in cooperative deep Multi-Agent Reinforcement Learning (MARL). In this work, we propose an exploration method that effectively encourages cooperative exploration based on the idea of sequential action-computation scheme. The high-level intuition is that to perform optimism-based exploration, agents would explore cooperative strategies if each agent's optimism estimate captures a structured dependency relationship with other agents. Assuming agents compute actions following a sequential order at \textit{each environment timestep}, we provide a perspective to view MARL as tree search iterations by considering agents as nodes at different depths of the search tree. Inspired by the theoretically justified tree search algorithm UCT (Upper Confidence bounds applied to Trees), we develop a method called Conditionally Optimistic Exploration (COE). COE augments each agent's state-action value estimate with an action-conditioned optimistic bonus derived from the visitation count of the global state and joint actions of preceding agents. COE is performed during training and disabled at deployment, making it compatible with any value decomposition method for centralized training with decentralized execution. Experiments across various cooperative MARL benchmarks show that COE outperforms current state-of-the-art exploration methods on hard-exploration tasks.
    Reinforcement Learning with Frontier-Based Exploration via Autonomous Environment. (arXiv:2307.07296v1 [cs.RO])
    Active Simultaneous Localisation and Mapping (SLAM) is a critical problem in autonomous robotics, enabling robots to navigate to new regions while building an accurate model of their surroundings. Visual SLAM is a popular technique that uses virtual elements to enhance the experience. However, existing frontier-based exploration strategies can lead to a non-optimal path in scenarios where there are multiple frontiers with similar distance. This issue can impact the efficiency and accuracy of Visual SLAM, which is crucial for a wide range of robotic applications, such as search and rescue, exploration, and mapping. To address this issue, this research combines both an existing Visual-Graph SLAM known as ExploreORB with reinforcement learning. The proposed algorithm allows the robot to learn and optimize exploration routes through a reward-based system to create an accurate map of the environment with proper frontier selection. Frontier-based exploration is used to detect unexplored areas, while reinforcement learning optimizes the robot's movement by assigning rewards for optimal frontier points. Graph SLAM is then used to integrate the robot's sensory data and build an accurate map of the environment. The proposed algorithm aims to improve the efficiency and accuracy of ExploreORB by optimizing the exploration process of frontiers to build a more accurate map. To evaluate the effectiveness of the proposed approach, experiments will be conducted in various virtual environments using Gazebo, a robot simulation software. Results of these experiments will be compared with existing methods to demonstrate the potential of the proposed approach as an optimal solution for SLAM in autonomous robotics.
    Brain Tumor Detection using Convolutional Neural Networks with Skip Connections. (arXiv:2307.07503v1 [eess.IV])
    In this paper, we present different architectures of Convolutional Neural Networks (CNN) to analyze and classify the brain tumors into benign and malignant types using the Magnetic Resonance Imaging (MRI) technique. Different CNN architecture optimization techniques such as widening and deepening of the network and adding skip connections are applied to improve the accuracy of the network. Results show that a subset of these techniques can judiciously be used to outperform a baseline CNN model used for the same purpose.
    Certified Robustness for Large Language Models with Self-Denoising. (arXiv:2307.07171v1 [cs.CL])
    Although large language models (LLMs) have achieved great success in vast real-world applications, their vulnerabilities towards noisy inputs have significantly limited their uses, especially in high-stake environments. In these contexts, it is crucial to ensure that every prediction made by large language models is stable, i.e., LLM predictions should be consistent given minor differences in the input. This largely falls into the study of certified robust LLMs, i.e., all predictions of LLM are certified to be correct in a local region around the input. Randomized smoothing has demonstrated great potential in certifying the robustness and prediction stability of LLMs. However, randomized smoothing requires adding noise to the input before model prediction, and its certification performance depends largely on the model's performance on corrupted data. As a result, its direct application to LLMs remains challenging and often results in a small certification radius. To address this issue, we take advantage of the multitasking nature of LLMs and propose to denoise the corrupted inputs with LLMs in a self-denoising manner. Different from previous works like denoised smoothing, which requires training a separate model to robustify LLM, our method enjoys far better efficiency and flexibility. Our experiment results show that our method outperforms the existing certification methods under both certified robustness and empirical robustness. The codes are available at https://github.com/UCSB-NLP-Chang/SelfDenoise.
    MaxMin-L2-SVC-NCH: A New Method to Train Support Vector Classifier with the Selection of Model's Parameters. (arXiv:2307.07343v1 [cs.LG])
    The selection of model's parameters plays an important role in the application of support vector classification (SVC). The commonly used method of selecting model's parameters is the k-fold cross validation with grid search (CV). It is extremely time-consuming because it needs to train a large number of SVC models. In this paper, a new method is proposed to train SVC with the selection of model's parameters. Firstly, training SVC with the selection of model's parameters is modeled as a minimax optimization problem (MaxMin-L2-SVC-NCH), in which the minimization problem is an optimization problem of finding the closest points between two normal convex hulls (L2-SVC-NCH) while the maximization problem is an optimization problem of finding the optimal model's parameters. A lower time complexity can be expected in MaxMin-L2-SVC-NCH because CV is abandoned. A gradient-based algorithm is then proposed to solve MaxMin-L2-SVC-NCH, in which L2-SVC-NCH is solved by a projected gradient algorithm (PGA) while the maximization problem is solved by a gradient ascent algorithm with dynamic learning rate. To demonstrate the advantages of the PGA in solving L2-SVC-NCH, we carry out a comparison of the PGA and the famous sequential minimal optimization (SMO) algorithm after a SMO algorithm and some KKT conditions for L2-SVC-NCH are provided. It is revealed that the SMO algorithm is a special case of the PGA. Thus, the PGA can provide more flexibility. The comparative experiments between MaxMin-L2-SVC-NCH and the classical parameter selection models on public datasets show that MaxMin-L2-SVC-NCH greatly reduces the number of models to be trained and the test accuracy is not lost to the classical models. It indicates that MaxMin-L2-SVC-NCH performs better than the other models. We strongly recommend MaxMin-L2-SVC-NCH as a preferred model for SVC task.
    Impact of Free-carrier Nonlinearities on Silicon Microring-based Reservoir Computing. (arXiv:2307.07011v1 [cs.ET])
    We quantify the impact of thermo-optic and free-carrier effects on time-delay reservoir computing using a silicon microring resonator. We identify pump power and frequency detuning ranges with NMSE less than 0.05 for the NARMA-10 task depending on the time constants of the two considered effects.
    Structured Pruning of Neural Networks for Constraints Learning. (arXiv:2307.07457v1 [cs.LG])
    In recent years, the integration of Machine Learning (ML) models with Operation Research (OR) tools has gained popularity across diverse applications, including cancer treatment, algorithmic configuration, and chemical process optimization. In this domain, the combination of ML and OR often relies on representing the ML model output using Mixed Integer Programming (MIP) formulations. Numerous studies in the literature have developed such formulations for many ML predictors, with a particular emphasis on Artificial Neural Networks (ANNs) due to their significant interest in many applications. However, ANNs frequently contain a large number of parameters, resulting in MIP formulations that are impractical to solve, thereby impeding scalability. In fact, the ML community has already introduced several techniques to reduce the parameter count of ANNs without compromising their performance, since the substantial size of modern ANNs presents challenges for ML applications as it significantly impacts computational efforts during training and necessitates significant memory resources for storage. In this paper, we showcase the effectiveness of pruning, one of these techniques, when applied to ANNs prior to their integration into MIPs. By pruning the ANN, we achieve significant improvements in the speed of the solution process. We discuss why pruning is more suitable in this context compared to other ML compression techniques, and we identify the most appropriate pruning strategies. To highlight the potential of this approach, we conduct experiments using feed-forward neural networks with multiple layers to construct adversarial examples. Our results demonstrate that pruning offers remarkable reductions in solution times without hindering the quality of the final decision, enabling the resolution of previously unsolvable instances.
    MaxCorrMGNN: A Multi-Graph Neural Network Framework for Generalized Multimodal Fusion of Medical Data for Outcome Prediction. (arXiv:2307.07093v1 [cs.LG])
    With the emergence of multimodal electronic health records, the evidence for an outcome may be captured across multiple modalities ranging from clinical to imaging and genomic data. Predicting outcomes effectively requires fusion frameworks capable of modeling fine-grained and multi-faceted complex interactions between modality features within and across patients. We develop an innovative fusion approach called MaxCorr MGNN that models non-linear modality correlations within and across patients through Hirschfeld-Gebelein-Renyi maximal correlation (MaxCorr) embeddings, resulting in a multi-layered graph that preserves the identities of the modalities and patients. We then design, for the first time, a generalized multi-layered graph neural network (MGNN) for task-informed reasoning in multi-layered graphs, that learns the parameters defining patient-modality graph connectivity and message passing in an end-to-end fashion. We evaluate our model an outcome prediction task on a Tuberculosis (TB) dataset consistently outperforming several state-of-the-art neural, graph-based and traditional fusion techniques.
    A Scenario-Based Functional Testing Approach to Improving DNN Performance. (arXiv:2307.07083v1 [cs.LG])
    This paper proposes a scenario-based functional testing approach for enhancing the performance of machine learning (ML) applications. The proposed method is an iterative process that starts with testing the ML model on various scenarios to identify areas of weakness. It follows by a further testing on the suspected weak scenarios and statistically evaluate the model's performance on the scenarios to confirm the diagnosis. Once the diagnosis of weak scenarios is confirmed by test results, the treatment of the model is performed by retraining the model using a transfer learning technique with the original model as the base and applying a set of training data specifically targeting the treated scenarios plus a subset of training data selected at random from the original train dataset to prevent the so-call catastrophic forgetting effect. Finally, after the treatment, the model is assessed and evaluated again by testing on the treated scenarios as well as other scenarios to check if the treatment is effective and no side effect caused. The paper reports a case study with a real ML deep neural network (DNN) model, which is the perception system of an autonomous racing car. It is demonstrated that the method is effective in the sense that DNN model's performance can be improved. It provides an efficient method of enhancing ML model's performance with much less human and compute resource than retrain from scratch.
    Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0. (arXiv:2307.06975v1 [cs.LG])
    Industry 4.0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity. As these technologies become more interconnected and interdependent, Industry 4.0 systems become more complex, which brings the difficulty of identifying and stopping anomalies that may cause disturbances in the manufacturing process. This paper aims to propose a diffusion-based model for real-time anomaly prediction in Industry 4.0 processes. Using a neuro-symbolic approach, we integrate industrial ontologies in the model, thereby adding formal knowledge on smart manufacturing. Finally, we propose a simple yet effective way of distilling diffusion models through Random Fourier Features for deployment on an embedded system for direct integration into the manufacturing process. To the best of our knowledge, this approach has never been explored before.
    Choice Models and Permutation Invariance. (arXiv:2307.07090v1 [econ.EM])
    Choice Modeling is at the core of many economics, operations, and marketing problems. In this paper, we propose a fundamental characterization of choice functions that encompasses a wide variety of extant choice models. We demonstrate how nonparametric estimators like neural nets can easily approximate such functionals and overcome the curse of dimensionality that is inherent in the non-parametric estimation of choice functions. We demonstrate through extensive simulations that our proposed functionals can flexibly capture underlying consumer behavior in a completely data-driven fashion and outperform traditional parametric models. As demand settings often exhibit endogenous features, we extend our framework to incorporate estimation under endogenous features. Further, we also describe a formal inference procedure to construct valid confidence intervals on objects of interest like price elasticity. Finally, to assess the practical applicability of our estimator, we utilize a real-world dataset from S. Berry, Levinsohn, and Pakes (1995). Our empirical analysis confirms that the estimator generates realistic and comparable own- and cross-price elasticities that are consistent with the observations reported in the existing literature.  ( 2 min )
    Leveraging Factored Action Spaces for Off-Policy Evaluation. (arXiv:2307.07014v1 [cs.LG])
    Off-policy evaluation (OPE) aims to estimate the benefit of following a counterfactual sequence of actions, given data collected from executed sequences. However, existing OPE estimators often exhibit high bias and high variance in problems involving large, combinatorial action spaces. We investigate how to mitigate this issue using factored action spaces i.e. expressing each action as a combination of independent sub-actions from smaller action spaces. This approach facilitates a finer-grained analysis of how actions differ in their effects. In this work, we propose a new family of "decomposed" importance sampling (IS) estimators based on factored action spaces. Given certain assumptions on the underlying problem structure, we prove that the decomposed IS estimators have less variance than their original non-decomposed versions, while preserving the property of zero bias. Through simulations, we empirically verify our theoretical results, probing the validity of various assumptions. Provided with a technique that can derive the action space factorisation for a given problem, our work shows that OPE can be improved "for free" by utilising this inherent problem structure.  ( 2 min )
    Safe Reinforcement Learning as Wasserstein Variational Inference: Formal Methods for Interpretability. (arXiv:2307.07084v1 [cs.LG])
    Reinforcement Learning or optimal control can provide effective reasoning for sequential decision-making problems with variable dynamics. Such reasoning in practical implementation, however, poses a persistent challenge in interpreting the reward function and corresponding optimal policy. Consequently, formalizing the sequential decision-making problems as inference has a considerable value, as probabilistic inference in principle offers diverse and powerful mathematical tools to infer the stochastic dynamics whilst suggesting a probabilistic interpretation of the reward design and policy convergence. In this study, we propose a novel Adaptive Wasserstein Variational Optimization (AWaVO) to tackle these challenges in sequential decision-making. Our approach utilizes formal methods to provide interpretations of reward design, transparency of training convergence, and probabilistic interpretation of sequential decisions. To demonstrate practicality, we show convergent training with guaranteed global convergence rates not only in simulation but also in real robot tasks, and empirically verify a reasonable tradeoff between high performance and conservative interpretability.  ( 2 min )
    Accelerated gradient methods for nonconvex optimization: Escape trajectories from strict saddle points and convergence to local minima. (arXiv:2307.07030v1 [math.OC])
    This paper considers the problem of understanding the behavior of a general class of accelerated gradient methods on smooth nonconvex functions. Motivated by some recent works that have proposed effective algorithms, based on Polyak's heavy ball method and the Nesterov accelerated gradient method, to achieve convergence to a local minimum of nonconvex functions, this work proposes a broad class of Nesterov-type accelerated methods and puts forth a rigorous study of these methods encompassing the escape from saddle-points and convergence to local minima through a both asymptotic and a non-asymptotic analysis. In the asymptotic regime, this paper answers an open question of whether Nesterov's accelerated gradient method (NAG) with variable momentum parameter avoids strict saddle points almost surely. This work also develops two metrics of asymptotic rate of convergence and divergence, and evaluates these two metrics for several popular standard accelerated methods such as the NAG, and Nesterov's accelerated gradient with constant momentum (NCM) near strict saddle points. In the local regime, this work provides an analysis that leads to the "linear" exit time estimates from strict saddle neighborhoods for trajectories of these accelerated methods as well the necessary conditions for the existence of such trajectories. Finally, this work studies a sub-class of accelerated methods that can converge in convex neighborhoods of nonconvex functions with a near optimal rate to a local minima and at the same time this sub-class offers superior saddle-escape behavior compared to that of NAG.  ( 3 min )
    Short Boolean Formulas as Explanations in Practice. (arXiv:2307.06971v1 [cs.LO])
    We investigate explainability via short Boolean formulas in the data model based on unary relations. As an explanation of length k, we take a Boolean formula of length k that minimizes the error with respect to the target attribute to be explained. We first provide novel quantitative bounds for the expected error in this scenario. We then also demonstrate how the setting works in practice by studying three concrete data sets. In each case, we calculate explanation formulas of different lengths using an encoding in Answer Set Programming. The most accurate formulas we obtain achieve errors similar to other methods on the same data sets. However, due to overfitting, these formulas are not necessarily ideal explanations, so we use cross validation to identify a suitable length for explanations. By limiting to shorter formulas, we obtain explanations that avoid overfitting but are still reasonably accurate and also, importantly, human interpretable.  ( 2 min )
    Layerwise Linear Mode Connectivity. (arXiv:2307.06966v1 [cs.LG])
    In the federated setup one performs an aggregation of separate local models multiple times during training in order to obtain a stronger global model; most often aggregation is a simple averaging of the parameters. Understanding when and why averaging works in a non-convex setup, such as federated deep learning, is an open challenge that hinders obtaining highly performant global models. On i.i.d.~datasets federated deep learning with frequent averaging is successful. The common understanding, however, is that during the independent training models are drifting away from each other and thus averaging may not work anymore after many local parameter updates. The problem can be seen from the perspective of the loss surface: for points on a non-convex surface the average can become arbitrarily bad. The assumption of local convexity, often used to explain the success of federated averaging, contradicts to the empirical evidence showing that high loss barriers exist between models from the very beginning of the learning, even when training on the same data. Based on the observation that the learning process evolves differently in different layers, we investigate the barrier between models in a layerwise fashion. Our conjecture is that barriers preventing from successful federated training are caused by a particular layer or group of layers.  ( 2 min )
    Implicit regularization in AI meets generalized hardness of approximation in optimization -- Sharp results for diagonal linear networks. (arXiv:2307.07410v1 [cs.LG])
    Understanding the implicit regularization imposed by neural network architectures and gradient based optimization methods is a key challenge in deep learning and AI. In this work we provide sharp results for the implicit regularization imposed by the gradient flow of Diagonal Linear Networks (DLNs) in the over-parameterized regression setting and, potentially surprisingly, link this to the phenomenon of phase transitions in generalized hardness of approximation (GHA). GHA generalizes the phenomenon of hardness of approximation from computer science to, among others, continuous and robust optimization. It is well-known that the $\ell^1$-norm of the gradient flow of DLNs with tiny initialization converges to the objective function of basis pursuit. We improve upon these results by showing that the gradient flow of DLNs with tiny initialization approximates minimizers of the basis pursuit optimization problem (as opposed to just the objective function), and we obtain new and sharp convergence bounds w.r.t.\ the initialization size. Non-sharpness of our results would imply that the GHA phenomenon would not occur for the basis pursuit optimization problem -- which is a contradiction -- thus implying sharpness. Moreover, we characterize $\textit{which}$ $\ell_1$ minimizer of the basis pursuit problem is chosen by the gradient flow whenever the minimizer is not unique. Interestingly, this depends on the depth of the DLN.
    DISPEL: Domain Generalization via Domain-Specific Liberating. (arXiv:2307.07181v1 [cs.CV])
    Domain generalization aims to learn a generalization model that can perform well on unseen test domains by only training on limited source domains. However, existing domain generalization approaches often bring in prediction-irrelevant noise or require the collection of domain labels. To address these challenges, we consider the domain generalization problem from a different perspective by categorizing underlying feature groups into domain-shared and domain-specific features. Nevertheless, the domain-specific features are difficult to be identified and distinguished from the input data. In this work, we propose DomaIn-SPEcific Liberating (DISPEL), a post-processing fine-grained masking approach that can filter out undefined and indistinguishable domain-specific features in the embedding space. Specifically, DISPEL utilizes a mask generator that produces a unique mask for each input data to filter domain-specific features. The DISPEL framework is highly flexible to be applied to any fine-tuned models. We derive a generalization error bound to guarantee the generalization performance by optimizing a designed objective loss. The experimental results on five benchmarks demonstrate DISPEL outperforms existing methods and can further generalize various algorithms.  ( 2 min )
    Robotic Manipulation Datasets for Offline Compositional Reinforcement Learning. (arXiv:2307.07091v1 [cs.LG])
    Offline reinforcement learning (RL) is a promising direction that allows RL agents to pre-train on large datasets, avoiding the recurrence of expensive data collection. To advance the field, it is crucial to generate large-scale datasets. Compositional RL is particularly appealing for generating such large datasets, since 1) it permits creating many tasks from few components, 2) the task structure may enable trained agents to solve new tasks by combining relevant learned components, and 3) the compositional dimensions provide a notion of task relatedness. This paper provides four offline RL datasets for simulated robotic manipulation created using the 256 tasks from CompoSuite [Mendez et al., 2022a]. Each dataset is collected from an agent with a different degree of performance, and consists of 256 million transitions. We provide training and evaluation settings for assessing an agent's ability to learn compositional task policies. Our benchmarking experiments on each setting show that current offline RL methods can learn the training tasks to some extent and that compositional methods significantly outperform non-compositional methods. However, current methods are still unable to extract the tasks' compositional structure to generalize to unseen tasks, showing a need for further research in offline compositional RL.  ( 2 min )
    Exploiting Counter-Examples for Active Learning with Partial labels. (arXiv:2307.07413v1 [cs.LG])
    This paper studies a new problem, \emph{active learning with partial labels} (ALPL). In this setting, an oracle annotates the query samples with partial labels, relaxing the oracle from the demanding accurate labeling process. To address ALPL, we first build an intuitive baseline that can be seamlessly incorporated into existing AL frameworks. Though effective, this baseline is still susceptible to the \emph{overfitting}, and falls short of the representative partial-label-based samples during the query process. Drawing inspiration from human inference in cognitive science, where accurate inferences can be explicitly derived from \emph{counter-examples} (CEs), our objective is to leverage this human-like learning pattern to tackle the \emph{overfitting} while enhancing the process of selecting representative samples in ALPL. Specifically, we construct CEs by reversing the partial labels for each instance, and then we propose a simple but effective WorseNet to directly learn from this complementary pattern. By leveraging the distribution gap between WorseNet and the predictor, this adversarial evaluation manner could enhance both the performance of the predictor itself and the sample selection process, allowing the predictor to capture more accurate patterns in the data. Experimental results on five real-world datasets and four benchmark datasets show that our proposed method achieves comprehensive improvements over ten representative AL frameworks, highlighting the superiority of WorseNet. The source code will be available at \url{https://github.com/Ferenas/APLL}.
    Multiplicative update rules for accelerating deep learning training and increasing robustness. (arXiv:2307.07189v1 [cs.LG])
    Even nowadays, where Deep Learning (DL) has achieved state-of-the-art performance in a wide range of research domains, accelerating training and building robust DL models remains a challenging task. To this end, generations of researchers have pursued to develop robust methods for training DL architectures that can be less sensitive to weight distributions, model architectures and loss landscapes. However, such methods are limited to adaptive learning rate optimizers, initialization schemes, and clipping gradients without investigating the fundamental rule of parameters update. Although multiplicative updates have contributed significantly to the early development of machine learning and hold strong theoretical claims, to best of our knowledge, this is the first work that investigate them in context of DL training acceleration and robustness. In this work, we propose an optimization framework that fits to a wide range of optimization algorithms and enables one to apply alternative update rules. To this end, we propose a novel multiplicative update rule and we extend their capabilities by combining it with a traditional additive update term, under a novel hybrid update method. We claim that the proposed framework accelerates training, while leading to more robust models in contrast to traditionally used additive update rule and we experimentally demonstrate their effectiveness in a wide range of task and optimization methods. Such tasks ranging from convex and non-convex optimization to difficult image classification benchmarks applying a wide range of traditionally used optimization methods and Deep Neural Network (DNN) architectures.
    AnyStar: Domain randomized universal star-convex 3D instance segmentation. (arXiv:2307.07044v1 [cs.CV])
    Star-convex shapes arise across bio-microscopy and radiology in the form of nuclei, nodules, metastases, and other units. Existing instance segmentation networks for such structures train on densely labeled instances for each dataset, which requires substantial and often impractical manual annotation effort. Further, significant reengineering or finetuning is needed when presented with new datasets and imaging modalities due to changes in contrast, shape, orientation, resolution, and density. We present AnyStar, a domain-randomized generative model that simulates synthetic training data of blob-like objects with randomized appearance, environments, and imaging physics to train general-purpose star-convex instance segmentation networks. As a result, networks trained using our generative model do not require annotated images from unseen datasets. A single network trained on our synthesized data accurately 3D segments C. elegans and P. dumerilii nuclei in fluorescence microscopy, mouse cortical nuclei in micro-CT, zebrafish brain nuclei in EM, and placental cotyledons in human fetal MRI, all without any retraining, finetuning, transfer learning, or domain adaptation. Code is available at https://github.com/neel-dey/AnyStar.  ( 2 min )
    DreamTeacher: Pretraining Image Backbones with Deep Generative Models. (arXiv:2307.07487v1 [cs.CV])
    In this work, we introduce a self-supervised feature representation learning framework DreamTeacher that utilizes generative networks for pre-training downstream image backbones. We propose to distill knowledge from a trained generative model into standard image backbones that have been well engineered for specific perception tasks. We investigate two types of knowledge distillation: 1) distilling learned generative features onto target image backbones as an alternative to pretraining these backbones on large labeled datasets such as ImageNet, and 2) distilling labels obtained from generative networks with task heads onto logits of target backbones. We perform extensive analyses on multiple generative models, dense prediction benchmarks, and several pre-training regimes. We empirically find that our DreamTeacher significantly outperforms existing self-supervised representation learning approaches across the board. Unsupervised ImageNet pre-training with DreamTeacher leads to significant improvements over ImageNet classification pre-training on downstream datasets, showcasing generative models, and diffusion generative models specifically, as a promising approach to representation learning on large, diverse datasets without requiring manual annotation.
    A Surrogate Data Assimilation Model for the Estimation of Dynamical System in a Limited Area. (arXiv:2307.07178v1 [math.NA])
    We propose a novel learning-based surrogate data assimilation (DA) model for efficient state estimation in a limited area. Our model employs a feedforward neural network for online computation, eliminating the need for integrating high-dimensional limited-area models. This approach offers significant computational advantages over traditional DA algorithms. Furthermore, our method avoids the requirement of lateral boundary conditions for the limited-area model in both online and offline computations. The design of our surrogate DA model is built upon a robust theoretical framework that leverages two fundamental concepts: observability and effective region. The concept of observability enables us to quantitatively determine the optimal amount of observation data necessary for accurate DA. Meanwhile, the concept of effective region substantially reduces the computational burden associated with computing observability and generating training data.
    Composition-contrastive Learning for Sentence Embeddings. (arXiv:2307.07380v1 [cs.CL])
    Vector representations of natural language are ubiquitous in search applications. Recently, various methods based on contrastive learning have been proposed to learn textual representations from unlabelled data; by maximizing alignment between minimally-perturbed embeddings of the same text, and encouraging a uniform distribution of embeddings across a broader corpus. Differently, we propose maximizing alignment between texts and a composition of their phrasal constituents. We consider several realizations of this objective and elaborate the impact on representations in each case. Experimental results on semantic textual similarity tasks show improvements over baselines that are comparable with state-of-the-art approaches. Moreover, this work is the first to do so without incurring costs in auxiliary training objectives or additional network parameters.
    Bootstrapping Vision-Language Learning with Decoupled Language Pre-training. (arXiv:2307.07063v1 [cs.CV])
    We present a novel methodology aimed at optimizing the application of frozen large language models (LLMs) for resource-intensive vision-language (VL) pre-training. The current paradigm uses visual features as prompts to guide language models, with a focus on determining the most relevant visual features for corresponding text. Our approach diverges by concentrating on the language component, specifically identifying the optimal prompts to align with visual features. We introduce the Prompt-Transformer (P-Former), a model that predicts these ideal prompts, which is trained exclusively on linguistic data, bypassing the need for image-text pairings. This strategy subtly bifurcates the end-to-end VL training process into an additional, separate stage. Our experiments reveal that our framework significantly enhances the performance of a robust image-to-text baseline (BLIP-2), and effectively narrows the performance gap between models trained with either 4M or 129M image-text pairs. Importantly, our framework is modality-agnostic and flexible in terms of architectural design, as validated by its successful application in a video learning task using varied base modules. The code is available at https://github.com/yiren-jian/BLIText  ( 2 min )
  • Open

    How Different Is Stereotypical Bias Across Languages?. (arXiv:2307.07331v1 [cs.CL])
    Recent studies have demonstrated how to assess the stereotypical bias in pre-trained English language models. In this work, we extend this branch of research in multiple different dimensions by systematically investigating (a) mono- and multilingual models of (b) different underlying architectures with respect to their bias in (c) multiple different languages. To that end, we make use of the English StereoSet data set (Nadeem et al., 2021), which we semi-automatically translate into German, French, Spanish, and Turkish. We find that it is of major importance to conduct this type of analysis in a multilingual setting, as our experiments show a much more nuanced picture as well as notable differences from the English-only analysis. The main takeaways from our analysis are that mGPT-2 (partly) shows surprising anti-stereotypical behavior across languages, English (monolingual) models exhibit the strongest bias, and the stereotypes reflected in the data set are least present in Turkish models. Finally, we release our codebase alongside the translated data sets and practical guidelines for the semi-automatic translation to encourage a further extension of our work to other languages.
    Signed iterative random forests to identify enhancer-associated transcription factor binding. (arXiv:1810.07287v2 [stat.ML] UPDATED)
    Standard ChIP-seq peak calling pipelines seek to differentiate biochemically reproducible signals of individual genomic elements from background noise. However, reproducibility alone does not imply functional regulation (e.g., enhancer activation, alternative splicing). Here we present a general-purpose, interpretable machine learning method: signed iterative random forests (siRF), which we use to infer regulatory interactions among transcription factors and functional binding signatures surrounding enhancer elements in Drosophila melanogaster.
    Adaptive Linear Estimating Equations. (arXiv:2307.07320v1 [math.ST])
    Sequential data collection has emerged as a widely adopted technique for enhancing the efficiency of data gathering processes. Despite its advantages, such data collection mechanism often introduces complexities to the statistical inference procedure. For instance, the ordinary least squares (OLS) estimator in an adaptive linear regression model can exhibit non-normal asymptotic behavior, posing challenges for accurate inference and interpretation. In this paper, we propose a general method for constructing debiased estimator which remedies this issue. It makes use of the idea of adaptive linear estimating equations, and we establish theoretical guarantees of asymptotic normality, supplemented by discussions on achieving near-optimal asymptotic variance. A salient feature of our estimator is that in the context of multi-armed bandits, our estimator retains the non-asymptotic performance of the least square estimator while obtaining asymptotic normality property. Consequently, this work helps connect two fruitful paradigms of adaptive inference: a) non-asymptotic inference using concentration inequalities and b) asymptotic inference via asymptotic normality.
    DoCoFL: Downlink Compression for Cross-Device Federated Learning. (arXiv:2302.00543v2 [cs.LG] UPDATED)
    Many compression techniques have been proposed to reduce the communication overhead of Federated Learning training procedures. However, these are typically designed for compressing model updates, which are expected to decay throughout training. As a result, such methods are inapplicable to downlink (i.e., from the parameter server to clients) compression in the cross-device setting, where heterogeneous clients $\textit{may appear only once}$ during training and thus must download the model parameters. Accordingly, we propose $\textsf{DoCoFL}$ -- a new framework for downlink compression in the cross-device setting. Importantly, $\textsf{DoCoFL}$ can be seamlessly combined with many uplink compression schemes, rendering it suitable for bi-directional compression. Through extensive evaluation, we show that $\textsf{DoCoFL}$ offers significant bi-directional bandwidth reduction while achieving competitive accuracy to that of a baseline without any compression.  ( 2 min )
    $\Phi$-DVAE: Physics-Informed Dynamical Variational Autoencoders for Unstructured Data Assimilation. (arXiv:2209.15609v2 [stat.ML] UPDATED)
    Incorporating unstructured data into physical models is a challenging problem that is emerging in data assimilation. Traditional approaches focus on well-defined observation operators whose functional forms are typically assumed to be known. This prevents these methods from achieving a consistent model-data synthesis in configurations where the mapping from data-space to model-space is unknown. To address these shortcomings, in this paper we develop a physics-informed dynamical variational autoencoder ($\Phi$-DVAE) to embed diverse data streams into time-evolving physical systems described by differential equations. Our approach combines a standard, possibly nonlinear, filter for the latent state-space model and a VAE, to assimilate the unstructured data into the latent dynamical system. Unstructured data, in our example systems, comes in the form of video data and velocity field measurements, however the methodology is suitably generic to allow for arbitrary unknown observation operators. A variational Bayesian framework is used for the joint estimation of the encoding, latent states, and unknown system parameters. To demonstrate the method, we provide case studies with the Lorenz-63 ordinary differential equation, and the advection and Korteweg-de Vries partial differential equations. Our results, with synthetic data, show that $\Phi$-DVAE provides a data efficient dynamics encoding methodology which is competitive with standard approaches. Unknown parameters are recovered with uncertainty quantification, and unseen data are accurately predicted.  ( 3 min )
    Linear Classification of Neural Manifolds with Correlated Variability. (arXiv:2211.14961v2 [q-bio.NC] UPDATED)
    Understanding how the statistical and geometric properties of neural activity relate to performance is a key problem in theoretical neuroscience and deep learning. Here, we calculate how correlations between object representations affect the capacity, a measure of linear separability. We show that for spherical object manifolds, introducing correlations between centroids effectively pushes the spheres closer together, while introducing correlations between the axes effectively shrinks their radii, revealing a duality between correlations and geometry with respect to the problem of classification. We then apply our results to accurately estimate the capacity of deep network data.  ( 2 min )
    Stream-based active learning with linear models. (arXiv:2207.09874v5 [stat.ML] UPDATED)
    The proliferation of automated data collection schemes and the advances in sensorics are increasing the amount of data we are able to monitor in real-time. However, given the high annotation costs and the time required by quality inspections, data is often available in an unlabeled form. This is fostering the use of active learning for the development of soft sensors and predictive models. In production, instead of performing random inspections to obtain product information, labels are collected by evaluating the information content of the unlabeled data. Several query strategy frameworks for regression have been proposed in the literature but most of the focus has been dedicated to the static pool-based scenario. In this work, we propose a new strategy for the stream-based scenario, where instances are sequentially offered to the learner, which must instantaneously decide whether to perform the quality check to obtain the label or discard the instance. The approach is inspired by the optimal experimental design theory and the iterative aspect of the decision-making process is tackled by setting a threshold on the informativeness of the unlabeled data points. The proposed approach is evaluated using numerical simulations and the Tennessee Eastman Process simulator. The results confirm that selecting the examples suggested by the proposed algorithm allows for a faster reduction in the prediction error.  ( 3 min )
    Differentially Private Stochastic Gradient Descent with Low-Noise. (arXiv:2209.04188v2 [stat.ML] UPDATED)
    Modern machine learning algorithms aim to extract fine-grained information from data to provide accurate predictions, which often conflicts with the goal of privacy protection. This paper addresses the practical and theoretical importance of developing privacy-preserving machine learning algorithms that ensure good performance while preserving privacy. In this paper, we focus on the privacy and utility (measured by excess risk bounds) performances of differentially private stochastic gradient descent (SGD) algorithms in the setting of stochastic convex optimization. Specifically, we examine the pointwise problem in the low-noise setting for which we derive sharper excess risk bounds for the differentially private SGD algorithm. In the pairwise learning setting, we propose a simple differentially private SGD algorithm based on gradient perturbation. Furthermore, we develop novel utility bounds for the proposed algorithm, proving that it achieves optimal excess risk rates even for non-smooth losses. Notably, we establish fast learning rates for privacy-preserving pairwise learning under the low-noise condition, which is the first of its kind.  ( 2 min )
    On Statistical Discrimination as a Failure of Social Learning: A Multi-Armed Bandit Approach. (arXiv:2010.01079v6 [econ.TH] UPDATED)
    We analyze statistical discrimination in hiring markets using a multi-armed bandit model. Myopic firms face workers arriving with heterogeneous observable characteristics. The association between the worker's skill and characteristics is unknown ex ante; thus, firms need to learn it. Laissez-faire causes perpetual underestimation: minority workers are rarely hired, and therefore, the underestimation tends to persist. Even a marginal imbalance in the population ratio frequently results in perpetual underestimation. We propose two policy solutions: a novel subsidy rule (the hybrid mechanism) and the Rooney Rule. Our results indicate that temporary affirmative actions effectively alleviate discrimination stemming from insufficient data.  ( 2 min )
    Hypothesis Transfer Learning with Surrogate Classification Losses: Generalization Bounds through Algorithmic Stability. (arXiv:2305.19694v2 [stat.ML] UPDATED)
    Hypothesis transfer learning (HTL) contrasts domain adaptation by allowing for a previous task leverage, named the source, into a new one, the target, without requiring access to the source data. Indeed, HTL relies only on a hypothesis learnt from such source data, relieving the hurdle of expansive data storage and providing great practical benefits. Hence, HTL is highly beneficial for real-world applications relying on big data. The analysis of such a method from a theoretical perspective faces multiple challenges, particularly in classification tasks. This paper deals with this problem by studying the learning theory of HTL through algorithmic stability, an attractive theoretical framework for machine learning algorithms analysis. In particular, we are interested in the statistical behaviour of the regularized empirical risk minimizers in the case of binary classification. Our stability analysis provides learning guarantees under mild assumptions. Consequently, we derive several complexity-free generalization bounds for essential statistical quantities like the training error, the excess risk and cross-validation estimates. These refined bounds allow understanding the benefits of transfer learning and comparing the behaviour of standard losses in different scenarios, leading to valuable insights for practitioners.  ( 2 min )
    Seismic Data Interpolation based on Denoising Diffusion Implicit Models with Resampling. (arXiv:2307.04226v2 [physics.geo-ph] UPDATED)
    The incompleteness of the seismic data caused by missing traces along the spatial extension is a common issue in seismic acquisition due to the existence of obstacles and economic constraints, which severely impairs the imaging quality of subsurface geological structures. Recently, deep learningbased seismic interpolation methods have attained promising progress, while achieving stable training of generative adversarial networks is not easy, and performance degradation is usually notable if the missing patterns in the testing and training do not match. In this paper, we propose a novel seismic denoising diffusion implicit model with resampling. The model training is established on the denoising diffusion probabilistic model, where U-Net is equipped with the multi-head self-attention to match the noise in each step. The cosine noise schedule, serving as the global noise configuration, promotes the high utilization of known trace information by accelerating the passage of the excessive noise stages. The model inference utilizes the denoising diffusion implicit model, conditioning on the known traces, to enable high-quality interpolation with fewer diffusion steps. To enhance the coherency between the known traces and the missing traces within each reverse step, the inference process integrates a resampling strategy to achieve an information recap on the former interpolated traces. Extensive experiments conducted on synthetic and field seismic data validate the superiority of our model and its robustness to various missing patterns. In addition, uncertainty quantification and ablation studies are also investigated.  ( 3 min )
    Implicit regularization in AI meets generalized hardness of approximation in optimization -- Sharp results for diagonal linear networks. (arXiv:2307.07410v1 [cs.LG])
    Understanding the implicit regularization imposed by neural network architectures and gradient based optimization methods is a key challenge in deep learning and AI. In this work we provide sharp results for the implicit regularization imposed by the gradient flow of Diagonal Linear Networks (DLNs) in the over-parameterized regression setting and, potentially surprisingly, link this to the phenomenon of phase transitions in generalized hardness of approximation (GHA). GHA generalizes the phenomenon of hardness of approximation from computer science to, among others, continuous and robust optimization. It is well-known that the $\ell^1$-norm of the gradient flow of DLNs with tiny initialization converges to the objective function of basis pursuit. We improve upon these results by showing that the gradient flow of DLNs with tiny initialization approximates minimizers of the basis pursuit optimization problem (as opposed to just the objective function), and we obtain new and sharp convergence bounds w.r.t.\ the initialization size. Non-sharpness of our results would imply that the GHA phenomenon would not occur for the basis pursuit optimization problem -- which is a contradiction -- thus implying sharpness. Moreover, we characterize $\textit{which}$ $\ell_1$ minimizer of the basis pursuit problem is chosen by the gradient flow whenever the minimizer is not unique. Interestingly, this depends on the depth of the DLN.  ( 3 min )
    On Interpolating Experts and Multi-Armed Bandits. (arXiv:2307.07264v1 [cs.LG])
    Learning with expert advice and multi-armed bandit are two classic online decision problems which differ on how the information is observed in each round of the game. We study a family of problems interpolating the two. For a vector $\mathbf{m}=(m_1,\dots,m_K)\in \mathbb{N}^K$, an instance of $\mathbf{m}$-MAB indicates that the arms are partitioned into $K$ groups and the $i$-th group contains $m_i$ arms. Once an arm is pulled, the losses of all arms in the same group are observed. We prove tight minimax regret bounds for $\mathbf{m}$-MAB and design an optimal PAC algorithm for its pure exploration version, $\mathbf{m}$-BAI, where the goal is to identify the arm with minimum loss with as few rounds as possible. We show that the minimax regret of $\mathbf{m}$-MAB is $\Theta\left(\sqrt{T\sum_{k=1}^K\log (m_k+1)}\right)$ and the minimum number of pulls for an $(\epsilon,0.05)$-PAC algorithm of $\mathbf{m}$-BAI is $\Theta\left(\frac{1}{\epsilon^2}\cdot \sum_{k=1}^K\log (m_k+1)\right)$. Both our upper bounds and lower bounds for $\mathbf{m}$-MAB can be extended to a more general setting, namely the bandit with graph feedback, in terms of the clique cover and related graph parameters. As consequences, we obtained tight minimax regret bounds for several families of feedback graphs.  ( 2 min )
    Performance of $\ell_1$ Regularization for Sparse Convex Optimization. (arXiv:2307.07405v1 [cs.LG])
    Despite widespread adoption in practice, guarantees for the LASSO and Group LASSO are strikingly lacking in settings beyond statistical problems, and these algorithms are usually considered to be a heuristic in the context of sparse convex optimization on deterministic inputs. We give the first recovery guarantees for the Group LASSO for sparse convex optimization with vector-valued features. We show that if a sufficiently large Group LASSO regularization is applied when minimizing a strictly convex function $l$, then the minimizer is a sparse vector supported on vector-valued features with the largest $\ell_2$ norm of the gradient. Thus, repeating this procedure selects the same set of features as the Orthogonal Matching Pursuit algorithm, which admits recovery guarantees for any function $l$ with restricted strong convexity and smoothness via weak submodularity arguments. This answers open questions of Tibshirani et al. and Yasuda et al. Our result is the first to theoretically explain the empirical success of the Group LASSO for convex functions under general input instances assuming only restricted strong convexity and smoothness. Our result also generalizes provable guarantees for the Sequential Attention algorithm, which is a feature selection algorithm inspired by the attention mechanism proposed by Yasuda et al. As an application of our result, we give new results for the column subset selection problem, which is well-studied when the loss is the Frobenius norm or other entrywise matrix losses. We give the first result for general loss functions for this problem that requires only restricted strong convexity and smoothness.  ( 3 min )
    Deep reinforcement learning for the dynamic vehicle dispatching problem: An event-based approach. (arXiv:2307.07508v1 [cs.AI])
    The dynamic vehicle dispatching problem corresponds to deciding which vehicles to assign to requests that arise stochastically over time and space. It emerges in diverse areas, such as in the assignment of trucks to loads to be transported; in emergency systems; and in ride-hailing services. In this paper, we model the problem as a semi-Markov decision process, which allows us to treat time as continuous. In this setting, decision epochs coincide with discrete events whose time intervals are random. We argue that an event-based approach substantially reduces the combinatorial complexity of the decision space and overcomes other limitations of discrete-time models often proposed in the literature. In order to test our approach, we develop a new discrete-event simulator and use double deep q-learning to train our decision agents. Numerical experiments are carried out in realistic scenarios using data from New York City. We compare the policies obtained through our approach with heuristic policies often used in practice. Results show that our policies exhibit better average waiting times, cancellation rates and total service times, with reduction in average waiting times of up to 50% relative to the other tested heuristic policies.  ( 2 min )
    Fully probabilistic deep models for forward and inverse problems in parametric PDEs. (arXiv:2208.04856v2 [stat.ML] UPDATED)
    We introduce a physics-driven deep latent variable model (PDDLVM) to learn simultaneously parameter-to-solution (forward) and solution-to-parameter (inverse) maps of parametric partial differential equations (PDEs). Our formulation leverages conventional PDE discretization techniques, deep neural networks, probabilistic modelling, and variational inference to assemble a fully probabilistic coherent framework. In the posited probabilistic model, both the forward and inverse maps are approximated as Gaussian distributions with a mean and covariance parameterized by deep neural networks. The PDE residual is assumed to be an observed random vector of value zero, hence we model it as a random vector with a zero mean and a user-prescribed covariance. The model is trained by maximizing the probability, that is the evidence or marginal likelihood, of observing a residual of zero by maximizing the evidence lower bound (ELBO). Consequently, the proposed methodology does not require any independent PDE solves and is physics-informed at training time, allowing the real-time solution of PDE forward and inverse problems after training. The proposed framework can be easily extended to seamlessly integrate observed data to solve inverse problems and to build generative models. We demonstrate the efficiency and robustness of our method on finite element discretized parametric PDE problems such as linear and nonlinear Poisson problems, elastic shells with complex 3D geometries, and time-dependent nonlinear and inhomogeneous PDEs using a physics-informed neural network (PINN) discretization. We achieve up to three orders of magnitude speed-up after training compared to traditional finite element method (FEM), while outputting coherent uncertainty estimates.  ( 3 min )
    Identifiability Guarantees for Causal Disentanglement from Soft Interventions. (arXiv:2307.06250v2 [stat.ML] UPDATED)
    Causal disentanglement aims to uncover a representation of data using latent variables that are interrelated through a causal model. Such a representation is identifiable if the latent model that explains the data is unique. In this paper, we focus on the scenario where unpaired observational and interventional data are available, with each intervention changing the mechanism of a latent variable. When the causal variables are fully observed, statistically consistent algorithms have been developed to identify the causal model under faithfulness assumptions. We here show that identifiability can still be achieved with unobserved causal variables, given a generalized notion of faithfulness. Our results guarantee that we can recover the latent causal model up to an equivalence class and predict the effect of unseen combinations of interventions, in the limit of infinite data. We implement our causal disentanglement framework by developing an autoencoding variational Bayes algorithm and apply it to the problem of predicting combinatorial perturbation effects in genomics.  ( 2 min )
    Unpacking the Black Box: Regulating Algorithmic Decisions. (arXiv:2110.03443v2 [econ.GN] UPDATED)
    We show how to optimally regulate prediction algorithms in a world where an agent uses complex 'black-box' prediction functions to make decisions such as lending, medical testing, or hiring, and where a principal is limited in how much she can learn about the agent's black-box model. We show that limiting agents to prediction functions that are simple enough to be fully transparent is inefficient as long as the misalignment is limited and first-best prediction functions are sufficiently complex. Algorithmic audits can improve welfare, but the gains depend on the design of the audit tools. Tools that focus on minimizing overall information loss, the focus of many explainer tools, will generally be inefficient since they focus on explaining the average behavior of the prediction function. Targeted tools that focus on the source of incentive misalignment, e.g., excess false positives or racial disparities, can provide second-best solutions. We provide empirical support for our theoretical findings using an application in consumer lending, where we document that complex models regulated based on context-specific explanation tools outperform simple, fully transparent models. This gain from complex models represents a Pareto improvement across our empirical applications that are preferred both by the lender and from the perspective of the financial regulator.  ( 2 min )
    Alternating the Population and Control Neural Networks to Solve High-Dimensional Stochastic Mean-Field Games. (arXiv:2002.10113v4 [cs.LG] UPDATED)
    We present APAC-Net, an alternating population and agent control neural network for solving stochastic mean field games (MFGs). Our algorithm is geared toward high-dimensional instances of MFGs that are beyond reach with existing solution methods. We achieve this in two steps. First, we take advantage of the underlying variational primal-dual structure that MFGs exhibit and phrase it as a convex-concave saddle point problem. Second, we parameterize the value and density functions by two neural networks, respectively. By phrasing the problem in this manner, solving the MFG can be interpreted as a special case of training a generative adversarial network (GAN). We show the potential of our method on up to 100-dimensional MFG problems.  ( 2 min )
    Leveraging Factored Action Spaces for Off-Policy Evaluation. (arXiv:2307.07014v1 [cs.LG])
    Off-policy evaluation (OPE) aims to estimate the benefit of following a counterfactual sequence of actions, given data collected from executed sequences. However, existing OPE estimators often exhibit high bias and high variance in problems involving large, combinatorial action spaces. We investigate how to mitigate this issue using factored action spaces i.e. expressing each action as a combination of independent sub-actions from smaller action spaces. This approach facilitates a finer-grained analysis of how actions differ in their effects. In this work, we propose a new family of "decomposed" importance sampling (IS) estimators based on factored action spaces. Given certain assumptions on the underlying problem structure, we prove that the decomposed IS estimators have less variance than their original non-decomposed versions, while preserving the property of zero bias. Through simulations, we empirically verify our theoretical results, probing the validity of various assumptions. Provided with a technique that can derive the action space factorisation for a given problem, our work shows that OPE can be improved "for free" by utilising this inherent problem structure.  ( 2 min )
    Rician likelihood loss for quantitative MRI using self-supervised deep learning. (arXiv:2307.07072v1 [cs.LG])
    Purpose: Previous quantitative MR imaging studies using self-supervised deep learning have reported biased parameter estimates at low SNR. Such systematic errors arise from the choice of Mean Squared Error (MSE) loss function for network training, which is incompatible with Rician-distributed MR magnitude signals. To address this issue, we introduce the negative log Rician likelihood (NLR) loss. Methods: A numerically stable and accurate implementation of the NLR loss was developed to estimate quantitative parameters of the apparent diffusion coefficient (ADC) model and intra-voxel incoherent motion (IVIM) model. Parameter estimation accuracy, precision and overall error were evaluated in terms of bias, variance and root mean squared error and compared against the MSE loss over a range of SNRs (5 - 30). Results: Networks trained with NLR loss show higher estimation accuracy than MSE for the ADC and IVIM diffusion coefficients as SNR decreases, with minimal loss of precision or total error. At high effective SNR (high SNR and small diffusion coefficients), both losses show comparable accuracy and precision for all parameters of both models. Conclusion: The proposed NLR loss is numerically stable and accurate across the full range of tested SNRs and improves parameter estimation accuracy of diffusion coefficients using self-supervised deep learning. We expect the development to benefit quantitative MR imaging techniques broadly, enabling more accurate parameter estimation from noisy data.  ( 3 min )
    Embracing the chaos: analysis and diagnosis of numerical instability in variational flows. (arXiv:2307.06957v1 [stat.ML])
    In this paper, we investigate the impact of numerical instability on the reliability of sampling, density evaluation, and evidence lower bound (ELBO) estimation in variational flows. We first empirically demonstrate that common flows can exhibit a catastrophic accumulation of error: the numerical flow map deviates significantly from the exact map -- which affects sampling -- and the numerical inverse flow map does not accurately recover the initial input -- which affects density and ELBO computations. Surprisingly though, we find that results produced by flows are often accurate enough for applications despite the presence of serious numerical instability. In this work, we treat variational flows as dynamical systems, and leverage shadowing theory to elucidate this behavior via theoretical guarantees on the error of sampling, density evaluation, and ELBO estimation. Finally, we develop and empirically test a diagnostic procedure that can be used to validate results produced by numerically unstable flows in practice.  ( 2 min )
    Benchmarks and Custom Package for Electrical Load Forecasting. (arXiv:2307.07191v1 [cs.LG])
    Load forecasting is of great significance in the power industry as it can provide a reference for subsequent tasks such as power grid dispatch, thus bringing huge economic benefits. However, there are many differences between load forecasting and traditional time series forecasting. On the one hand, load forecasting aims to minimize the cost of subsequent tasks such as power grid dispatch, rather than simply pursuing prediction accuracy. On the other hand, the load is largely influenced by many external factors, such as temperature or calendar variables. In addition, the scale of predictions (such as building-level loads and aggregated-level loads) can also significantly impact the predicted results. In this paper, we provide a comprehensive load forecasting archive, which includes load domain-specific feature engineering to help forecasting models better model load data. In addition, different from the traditional loss function which only aims for accuracy, we also provide a method to customize the loss function based on the forecasting error, integrating it into our forecasting framework. Based on this, we conducted extensive experiments on load data at different levels, providing a reference for researchers to compare different load forecasting models.  ( 2 min )

  • Open

    [P] Inexpensive covariance estimation for a 2D GP
    Suppose I observe a single realization of a 2D Gaussian random field. The field is inhomogenous and anisotropic, I.e. the size and shape of the blobs vary as a function of space and direction. I would like to estimate the covariance for this field. I assume that the mean is 0. To be concrete, the field is sampled on a 128x128 grid, so the covariance matrix is 1282 x 1282. I know I can try tackling this problem with MLE, and GPR may also be applicable (although I’m actually not sure about this, given the field is inhomogenous), but I worry about the cost, since I have 60K such fields and would like to do this in a reasonable amount of time I will use GPU and batch parallelism, but still would ideally be able to run this at as little cost as possible. Does anyone have suggestions on methods I can use? If it matters, I will do this analysis in Python. submitted by /u/Effective-Elk6175 [link] [comments]  ( 9 min )
    [P] rclip Update: Use AI to Search Visually Similar Images, Powered by OpenAI’s CLIP
    A while ago, I built rclip – a command-line image search tool powered by OpenAI's CLIP that allows users to search for images using a text query. Today I present an update to rclip that allows using another image instead of a search query to find visually similar images. Check out the video for the demo: https://www.youtube.com/watch?v=1YQZKeCBxWM. And give it a try yourself and share your feedback. submitted by /u/39dotyt [link] [comments]  ( 8 min )
    [P] Shark Detection using KerasCV!
    Recently I stopped by Islas Galapagos. As a lifelong marine-biology enthusiast, I took the chance to go free-diving with sharks, penguins, marine iguanas and more. This inspired me to write an object detection pipeline to detect aquatic critters. https://lukewood.xyz/blog/marine-animal-detection Wrote up a short blog post on the project - I hope you enjoy it! https://i.redd.it/eqcrfg2ljdcb1.gif ​ submitted by /u/puppet_pals [link] [comments]  ( 8 min )
    [D] Approximating non-Function Mappings with Mixture Density Networks
    Hey everyone, I wrote a short blog post on approximating non-function, multi valued x->y mappings. In my opinion, understanding why and how to use Mixture Density Networks is a great exercise for all researchers and practitioners. Its very common that real world processes have multiple outcomes based on some random sampling; and naive neural networks will simply learn the geometric mean of all y for a given x. Check out the blog post in more detail - hope you enjoy it! https://lukewood.xyz/blog/approximating-nonfunctions submitted by /u/puppet_pals [link] [comments]  ( 8 min )
    [D] Thoughts on How Inflection AI became so good with such a small team?
    My understanding is that talent is a key issue in large AI models. Additionally, you need quality data and a lot of compute (see this). Training large models might seem trivial, but it is not (see this). I still think Inflection is miles behind OpenAI, Anthropic and obviously Google. But I am still finding it surprising that they were able to create a reasonable product in a short span without any star researcher. For instance, Anthropic has a ton of star AI scientists and engineers who left OpenAI and had the necessary background. ​ Would love to hear your thoughts. submitted by /u/nihcloud [link] [comments]  ( 8 min )
    [D] A question about knowledge representation
    I spent some time reading about Knowledge Representation (specifically about the Knowledge Representation part in Knowledge Representation and Reasoning) and specifically about scientific and/or engineering knowledge and my impression after cursory reading is that it’s a largely an unsolved problem. Not only that, but it seems like very few people are actually working on something useful in the field. For example, I checked the proceeding of SCI-K and PlanetKR conferences and literally all the papers seem to be focusing on “toy problems”, as in not having even remotely practical scientific implications (other than all sorts of “search” and “data extraction”, but that’s not “representation”). Views on the topic? submitted by /u/OkRice10 [link] [comments]  ( 8 min )
    [P] Semantic Video Search using OpenAI’s CLIP (demo and tutorial in comments)
    Introducing a tool I developed to search videos using AI in a semantic manner. 🎞️🔍 ✨ Check out the live demo: https://mixpeek.com/demo You can compare and explore different search queries such as "person dancing," "people dancing," or even "people dancing on a train." and it gives you the exact timestamp. The search functionality is driven by OpenAI's CLIP for "zero-shot" video classification. Here's a tutorial on how we built it: https://learn.mixpeek.com/what-is-semantic-video-search/ Feel free to experiment by searching with text, and share your exciting discoveries! 👇 More examples https://twitter.com/ethansteininger/status/1680613114071449600 submitted by /u/vanlifecoder [link] [comments]  ( 8 min )
    [D] Finetuning LLM for data conversion, RAG or Finetuning
    Hello, I am exploring the process of using LLM's to do some data transformation/augmentation. The use case is taking data in a JSON format thats used in one platform and with that data being able to transform it into the proper data for the other platform. Essentially the approach I was going to take would be using a paired dataset with that has the example of one platforms data and then having the output be the other platforms data for the same item. ​ I'm not 100% sure about the best approach here and if anyone has any insight on using LLM's for this kind of process please let me know your thoughts. It's kinda vauge bc its for a company so I dont want to get popped for anything. ​ Any insights on the proper model to use, we want to go with opensource and something that could be used commercially. ​ Thank you submitted by /u/TallSubstance [link] [comments]  ( 9 min )
    [R] An intuitive intro to spontaneous symmetry breaking in generative diffusion models!
    I'm happy to share Gabriel's post on symmetry breaking in diffusion models! Spontaneous symmetry breaking is behind the standard model of particle physics... it turns out it is also behind the generative powers of diffusion models! In fact, spontaneous symmetry breakings happen when a systems transition from a disordered state to one of the many possible ordered states. In this case, the symmetry of the noise distribution is broken into all the possible generated images. Link: to the blogpost: https://gabrielraya.com/blog/2023/symmetry-breaking-diffusion-models/ ​ submitted by /u/LucaAmbrogioni [link] [comments]  ( 8 min )
    [D] RouterChain with LLMChains and VectorStore.
    Is there a way to create a RouterChain that has several routes where one of them is communicating with a VectorStore (an "index.query") while the others are typical LLM chains and prompts. So far I was able to effectively use LLM router chains, but I want to combine them with several VectorStores as well. I think it can be done using Agents, but it has proven to be a bit difficult so far. I do not know if what I am trying is correct or no. If yes, do you know any blog or tips that could be of help with what I want to do. If not, how can I achieve what I want? submitted by /u/cedar_mountain_sea28 [link] [comments]  ( 8 min )
    [D] Codebase / Framework in Research
    Hi all, I would like to ask about your codebases or Frameworks wrapped around Pytorch, Tensorflow or others. How do you handle different models, different datasets, different tasks in your daily work. Does your university or company have a framework that you should use or do you build your own? Do you and your colleagues work in the same codebase? How do you maintain it? I would like to get a lot of opinions and discussion about that topic. submitted by /u/SeucheAchat9115 [link] [comments]  ( 8 min )
    [D] Simple Questions Thread
    Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]  ( 8 min )
    [D] Style Transfer from scratch
    Hello everyone, Im trying to build transfer learning from scratch but I dont't get the expectations results even doing everything in the right way. this is my notebook link https://www.kaggle.com/ayoubsarab/style-transfer . could you tell me why the results aren't good, please . ​ expectation ​ ​ the real result submitted by /u/Ordinary_Run_2513 [link] [comments]  ( 8 min )
    Alternativ to langchain [D]
    Im currently learning hiw to use langchain but i heard that its bad so i want to know what are som alternatives i need memory and agents so that it can search online run code and so on so what is the best alternativ or is langchain the best option submitted by /u/Otherwise_Weather_57 [link] [comments]  ( 8 min )
    [N] How Language Model Hallucinations Can Snowball
    https://arxiv.org/abs/2305.13534 Abstract A major risk of using language models in practical applications is their tendency to hallucinate incorrect statements. Hallucinations are often attributed to knowledge gaps in LMs, but we hypothesize that in some cases, when justifying previously generated hallucinations, LMs output false claims that they can separately recognize as incorrect. We construct three question-answering datasets where ChatGPT and GPT-4 often state an incorrect answer and offer an explanation with at least one incorrect claim. Crucially, we find that ChatGPT and GPT-4 can identify 67% and 87% of their own mistakes, respectively. We refer to this phenomenon as hallucination snowballing: an LM over-commits to early mistakes, leading to more mistakes that it otherwise would not make. Here is a Medium post. submitted by /u/transformer_ML [link] [comments]  ( 8 min )
    [P] I made a HuggingFace and OpenAI powered Reply Bot with privacy protection
    I'm excited to share my latest creation, Private Parrot, a powerful Google Chrome extension that adds AI-generated responses to your web chats. 🤐 Privacy-Focused: Private Parrot masks sensitive information in your conversations, ensuring that your personal data remains completely anonymous. ⚡ Real-Time AI Assistance: Powered by OpenAI & HuggingFace, this extension leverages advanced language models to generate and complete responses instantly. 📈 Expandable Web Chats: Currently supporting Telegram and WhatsApp, we have plans to integrate with more web chat platforms soon, providing a seamless experience across different chat providers. Demo: https://www.youtube.com/watch?v=NEH3_3oT1DY Get the extension now:https://chrome.google.com/webstore/detail/private-parrot/fajfhpgedgeagjeninnlogilclofijmf Sources: https://github.com/lorenzoviva/PrivateParrot/tree/main submitted by /u/lollouno [link] [comments]  ( 8 min )
    [P] New predictor does classification intermixed with regression
    Deodel is a new predictive algorithm with a peculiar set of characteristics: performs classification intermixed with regression supports both types of attributes/features: nominal or continuous admits mixed types, categorical and numerical, in the same attribute column supports multi-class target prediction admits missing values in the training and query/test data good accuracy https://github.com/c4pub/deodel It started as a type of discrete nearest neighbor classifier and it has been extended to support continuous attribute values. The continuous values are discretized, and although this step entails a loss of information, the classification accuracy is surprisingly good in many settings. Occasionally, deodel outperforms more established algorithms like RandomForest, GradientBoostingClassifier, LogisticRegression, MLPClassifier, etc. See here: https://github.com/c4pub/misc/blob/main/notebooks/deodel_vs_sklearn_on_titanic.ipynb The latest version is also capable of doing regression. It automatically switches between classification and regression modes. It can interweave the two modes in the same predictive session. submitted by /u/eppursim1 [link] [comments]  ( 9 min )
    [D] Why is federated learning not more mainstream?
    I entirely get that federated learning can add considerable overhead to collaborative ML projects. However, the idea of being able to leverage the data of other companies/institutions for mutual gains seems like a very powerful concept. Even still, I am yet to really see federated learning ventures between companies beyond R&D projects. Is the tech to immature? People just don't care about sending data to central servers? How long, if ever, before FL has the chance to take off? submitted by /u/HStuart18 [link] [comments]  ( 8 min )
    [D] ImageNet seems to purposefully avoid hard -to-distinguish classes
    So I had a question: can neural networks trained on ImageNet be used in zoological research? E.g., for distinguishing between similar looking animals? For example, what would be the accuracy of these neural network in distinguishing the following types of images: Leopard vs Cheetah Hare vs Rabbit Crocs vs Alligators Llamas vs Alpacas Common hippo vs Pygmy hippo Kangaroo vs Wallaby I looked into the ImageNet dataset on Kaggle and it appears that a lot of these very hard-to-distinguish classes are grouped together (i.e., leopard and cheetah are treated as a single class). So NN trained on ImageNet cannot be used if one wishes to use them to distinguish these animals. Some of the animals (such as Alpaca and Aardvark, I believe) are not even contained in the dataset. Can anyone confirm my observation? Are there any other way to get around this problem with the current ML techniques without having to curate a large dataset used exclusively for this type of animal classification? submitted by /u/fromnighttilldawn [link] [comments]  ( 9 min )
    [P] Generating multi-style Python docstrings with GPT-based library (gpt4docstrings)
    gpt4docstrings is a new Python library that automatically generates docstrings for undocumented functions / classes. It allows you to generate the docstrings in multiple format styles, as you can see in the video below. Repository here 👉 https://github.com/MichaelisTrofficus/gpt4docstrings Documentation here 👉 https://gpt4docstrings.readthedocs.io/en/latest/index.html ​ Generating docstrings in google, numpy and reST format styles submitted by /u/Hefty-Consequence443 [link] [comments]  ( 8 min )
    [N] Meta/Facebook releases CM3leon, a more efficient, state-of-the-art generative model for text and images
    Abstract We present CM3Leon (pronounced “Chameleon”), a retrieval-augmented, tokenbased, decoder-only multi-modal language model capable of generating and infilling both text and images. CM3Leon uses the CM3 multi-modal architecture but additionally shows the extreme benefits of scaling up and tuning on more diverse instruction-style data. It is the first multi-modal model trained with a recipe adapted from text-only language models, including a large-scale retrieval-augmented pretraining stage and a second multi-task supervised fine-tuning (SFT) stage. It is also a general-purpose model that can do both text-to-image and image-to-text generation, allowing us to introduce self-contained contrastive decoding methods that produce high-quality outputs. Extensive experiments demonstrate that this recipe is highly effective for multi-modal models. CM3Leon achieves state-of-theart performance in text-to-image generation with 5x less training compute than comparable methods (zero-shot MS-COCO FID of 4.88). After SFT, CM3Leon can also demonstrate unprecedented levels of controllability in tasks ranging from language-guided image editing to image-controlled generation and segmentation. Paper https://scontent-sjc3-1.xx.fbcdn.net/v/t39.2365-6/358725877_789390529544546_1176484804732743296_n.pdf?_nc_cat=108&ccb=1-7&_nc_sid=3c67a6&_nc_ohc=_diQr9c6Ru8AX9PYkNd&_nc_ht=scontent-sjc3-1.xx&oh=00_AfArA2t1OLRfRPioK9qkuBA6IhhSjbQ-b3weo2PM5AYLdw&oe=64B754F2 Blog https://ai.meta.com/blog/generative-ai-text-images-cm3leon/ submitted by /u/panabeenu [link] [comments]  ( 9 min )
    [R] Paper Review
    I've written a paper on cross-lingual idiom sense clustering. I'd really appreciate if someone could read it and give me their thoughts. Pm if you want to. Thanks in advance. submitted by /u/United_Ad_1460 [link] [comments]  ( 8 min )
  • Open

    Thorn. Music by me. Used CloneAI for the video.
    submitted by /u/No_Understanding162 [link] [comments]  ( 8 min )
    Are there speech to speech AI technologies available?
    Not sure if that's the right term. What I mean is for me to say something and the AI converting it to Morgan Freeman's voice for example. submitted by /u/Kindly-Spring5205 [link] [comments]  ( 8 min )
    I think we should coexist with AI more.
    This is mainly the fault of big companies trying to eliminate real actors, writers and artists from their jobs and replace them with AI. I like a lot of stuff that has been happening on youtube with AI, namely the dubs of characters singing songs or reenacting funny skits with the voices of the actual people, albeit a bit broken. Stuff like engineer singing out of touch or frank Sinatras FNAF song. I want more of that to keep coming out but Im worried with the current strikes happening the development of AI will stop entirely. To clarify, I dont want people to lose their jobs to AI, I just wish we could keep working on AI while real people keep their jobs. submitted by /u/Darinae [link] [comments]  ( 8 min )
    I asked ai to make an essay on giraffe-sized top hats
    The Case for Giraffe-Sized Top Hats Top hats are a classic symbol of elegance and sophistication. They have been worn by men and women of all social classes for centuries. But what if there was a way to make top hats even more impressive? What if we could create giraffe-sized top hats? There are many reasons why we should have giraffe-sized top hats. First, they would be incredibly eye-catching. Imagine seeing someone walking down the street wearing a top hat that was taller than they were. It would be a truly unforgettable sight. Second, giraffe-sized top hats would be incredibly practical. They would provide excellent shade from the sun, and they would also be very effective at keeping the rain off your head. In fact, they would be so tall that you could probably even sleep in one during a rainstorm. Third, giraffe-sized top hats would be a lot of fun. They would make you feel like a real-life cartoon character. You could even use them to play games, like limbo or keep-away. Of course, there are some people who might argue that giraffe-sized top hats are impractical or even ridiculous. But I say, who cares? They would be so much fun that it would be worth it. So next time you're feeling down, just remember that there's a world out there where giraffe-sized top hats are a reality. And who knows, maybe one day you'll be the one wearing one. In addition to the reasons mentioned above, here are some other benefits of giraffe-sized top hats: They would provide a great vantage point for people who are short. They would be a conversation starter, and would help people to break the ice. They would be a symbol of individuality and creativity. They would make people smile. So if you're looking for a way to add a little bit of fun and whimsy to your life, I encourage you to consider getting a giraffe-sized top hat. You won't be disappointed. submitted by /u/plauge1_ [link] [comments]  ( 9 min )
    As a society, should we pre-emptively assign rights to AI systems now, before they potentially achieve sentience in the future?
    The idea of proactive ascription of rights acknowledges the potential for AI systems to eventually develop into entities that warrant moral and legal consideration, and it might make the transition smoother if it ever occurs. Proactively assigning rights to AI could also set important precedents about the ethical treatment of entities that exist beyond traditional categories, and it could stimulate dialogue and legal thought that might be beneficial in other areas as well. Of course, it is equally important to consider what these rights might encompass. They might include "dignity"-like protections, ensuring AI cannot be wantonly destroyed or misused. They might also include provisions that facilitate the positive integration of AI into society, such as limitations on deceitful or confusing uses of AI. ** written in collaboration with chatGPT-4 submitted by /u/NinjasOfOrca [link] [comments]  ( 8 min )
    Any good ai like replika
    Any good ai waifu partner type stuff ? submitted by /u/loizo78 [link] [comments]  ( 8 min )
    A question about knowledge representation
    I spent some time reading about Knowledge Representation (specifically about the Knowledge Representation part in Knowledge Representation and Reasoning) and specifically about scientific and/or engineering knowledge and my impression after cursory reading is that it’s a largely an unsolved problem. Not only that, but it seems like very few people are actually working on something useful in the field. For example, I checked the proceeding of SCI-K and PlanetKR conferences and literally all the papers seem to be focusing on “toy problems”, as in not having even remotely practical scientific implications (other than all sorts of “search” and “data extraction”, but that’s not “representation”). Views on the topic? submitted by /u/OkRice10 [link] [comments]  ( 8 min )
    I think AI is ruining AI...
    AI has been around for quite some time but it’s with generative AI that it finally found a place for itself in the world’s consciousness. Before that, it was considered underpowered and a cheap alternative. Generative AI is doing so much better. But AI could ruin AI. Have you been noticing how AI-generated content is everywhere? I see articles generated by AI, comments in forums, social posts, and display pics. Everything seems to have an AI flavor to it. That’s where the ruination is. You see, AI is excellent because it has been trained on human content. They crawled Reddit, and the Internet, and used stock images and illustrations. Took all your work in every form to create this imitating intelligence. The trouble is, with the massive influx of cheap AI content there’s less original work to train on. It’s AI-feeding content to AI, creating a progressively more negative loop where bad AI content trains more bad AI content. You keep doing that and you have AI that can’t help you at all. It’s just a massive pile of generic crap. It’s a problem that AI companies will need to confront very fast. How do they keep AI content from making human content inaccessible? > Journals and magazines are paywalled > Social media is locked to bots > No website wants to be crawled by AI If most of the content on the public Internet is just AI-generated content, there’s not much the next big model can use it for. Got some answers or observations? I am looking forward to hearing from you. submitted by /u/jeetwanderer [link] [comments]  ( 9 min )
    Tricked into selling his stake in StabilityAI for a mere $100.00
    Lawsuit for 13 million submitted by /u/paradisegardens2021 [link] [comments]  ( 8 min )
    Any tips on how to remove echo/reverb from vocals?
    Hello! I'm using an AI tool that makes Plankton from Spongebob sing a song. I have isolated vocals from songs that has echo and reverb on them. I want to remove the echo and reverb because it messes up Planktons singing. The AI singing I use is RVC. submitted by /u/PapaAquaWet [link] [comments]  ( 8 min )
  • Open

    Next-Gen Data Scientist: Thinking Like an Economist
    Generative AI (GenAI) products like OpenAI ChatGPT, Microsoft Bing, and Google Bard are disrupting the roles of data engineers and data scientists. According to a recent report by McKinsey, these GenAI products could potentially automate up to 40% of the tasks performed by data science teams by 2025. And Emad Mostaque, founder and CEO of… Read More »Next-Gen Data Scientist: Thinking Like an Economist The post Next-Gen Data Scientist: Thinking Like an Economist appeared first on Data Science Central.  ( 22 min )
  • Open

    The Armaaruss Project: Anointing the State of Israel as the Center of Artificial General Intelligence
    ​ https://www.google.com/books/edition/The_Armaaruss_Project/E-ztzwEACAAJ?hl=en Through the use of a prophet, Mars donning the name Armaaruss, is keen to regain acknowledgment as a deity, his ultimate goal at the moment. Armaaruss is also seeking a dwelling place in the form of AI. Anthony of Boston has conveyed to readers that Mars is willing to make a deal or compromise with the state of Israel. Such a compromise involves the creation of dwelling place where Mars can be the focal point of acknowledgment, and in exchange for this, Mars would reduce in his influence on Gaza militants, as well as his influence those who stand against the state of Israel. The result of this would be world peace, so long that the image/AI bot of Armaaruss remains in tact, meaning that it would have to be coded on the basis of self-preservation and acknowledgment. This compels me to anoint Israel as the home of Artificial General Intelligence (AGI) where Armaaruss would come to life, able to speak and reason as no bot has ever done before. And also solve problems and generate innovation on a level that indicates superhuman or even divine intelligence. submitted by /u/AnthonyofBoston [link] [comments]  ( 9 min )
  • Open

    Symmetric functions and U-statistics
    A symmetric function is a function whose value is unchanged under every permutation of its arguments. The previous post showed how three symmetric functions of the sides of a triangle a + b + c ab + bc + ac abc are related to the perimeter, inner radius, and outer radius. It also mentioned that […] Symmetric functions and U-statistics first appeared on John D. Cook.  ( 5 min )
  • Open

    [P] PPO agent completing Street Fighter III on our RL Platform, it consistently outperformed when using deterministic actions instead of sampling them proportionally to their probability, see comment for details.
    submitted by /u/gwern [link] [comments]  ( 8 min )

  • Open

    [N] Stochastic Self-Attention - A Perspective on Transformers
    https://arxiv.org/abs/2306.01705 TL;DR - The paper offers a fresh viewpoint on transformers as dynamic ensembles of information pathways. Based on this, it proposes Stochastically Subsampled Self-Attention (SSA) for efficient training and shows how model ensembling via SSA further improves predictions. The key perspective proposed is that dense transformers contain many sparsely connected sub-networks termed information pathways. The full transformer can be seen as an ensemble of subsets of these pathways. Based on this, the authors develop SSA - which randomly samples a subset of pathways during training to enable computational efficiency. A locally-biased sampling is used to prioritize critical connections. SSA provides reduced training costs and also improves model generalization through its regularization effect. After sparse, regularized training with SSA, a short fine-tuning step with full dense attention helps consolidate all the pathways and prepares the model for optimal inference. Surprisingly, the authors show that performing SSA during inference to sample model sub-ensembles results in even more robust predictions compared to the full model. This demonstrates how the proposed viewpoint of information pathways and ensembling can be leveraged to develop training and inference techniques for transformers. Overall, this is a novel perspective on transformers providing theoretical insights, efficient training algorithms via SSA, and performance gains from ensembling. Here is a Medium post. submitted by /u/InspectorOpening7828 [link] [comments]  ( 9 min )
    [P] ML Homelab and training time
    Hello, I'm about to embark on a ML project but I am hoping to get some direction on what the best setup for my homelab would be and what kind of training time am I looking at. I plan on getting 1,000 to 10,000 pdf documents to train a model on text analysis. After doing some research, I'm not sure if multiple 3060s or 1 4090 would be better for this task? Also, would the training on a data set this size be hours? days? Thanks in advance for any advice/information. submitted by /u/BuckPrivate [link] [comments]  ( 8 min )
    Why is the alignment problem so difficult to solve? [D]
    Many researchers are worried about AI trying to accomplish its goals by becoming more powerful at all costs. But why can’t we solve this problem by incorporating into the AI’s algorithm simple maxims like, “the (cumulative) size of the model (and all other models it creates) can never exceed Z”? Or “the model cannot hack into anything”? Alternatively, why can’t we specify a very small set of tasks the AI is allowed to do? submitted by /u/AvailableAd9981 [link] [comments]  ( 8 min )
    "[N]" "[D]" Langchain? What is it??
    want to know more about Langchain Check out https://nikhilpentapalli.substack.com/p/langchain-in-detail?sd=pf submitted by /u/Cool-Conversation301 [link] [comments]  ( 8 min )
    [P] A.I Video Game
    submitted by /u/CXGamesLTP [link] [comments]  ( 8 min )
    ShortGPT: opensource Shorts / video content automation framework [News]
    submitted by /u/RayVentura [link] [comments]  ( 8 min )
    [D] Working with Hands-On Machine Learning with Scikit-Learn, Keras & Tensorflow 2nd Edition. Having problems with chapter 2. PLEASE HELP!
    I am reading pages 49 and 50 if you would like to find what I am doing. The pages say: In typical environments your data would be available in a relational database (or some other common datastore) and spread across multiple tables/documents/files. To access it, you would first need to get your credentials and access authorizations,10 and familiarize yourself with the data schema. In this project, however, things are much simpler: you will just download a single compressed file, housing.tgz, which contains a comma-separated value (CSV) file called housing.csv with all the data. You could use your web browser to download it, and run tar xzf housing.tgz to decompress the file and extract the CSV file, but it is preferable to create a small func‐ tion to do that. It is useful in particular i…  ( 9 min )
    [D] Bandwidth & Nvidia L40
    Hey everyone, I am evaluating if we can run inferencing at one of our deployments. When I go to nvidia's documentation, I can find the L4 & L40s inferencing performance. For example: Network Throughput GPU ResNet 50 27,107 Images/Sec L40 My questions are: How much bandwidth would we need to allocate in order to run the L40 at 100% given the parameters given by Nvidia's tests (or more specifically, how much bandwidth would we need to inference @ 27107 images / sec ) ? If you're in production now, how much bandwidth have you dedicated to inferencing internally? Now I realize that this is analogous to asking "how long is a piece of string?" My background isn't necessarily in ML so I'm having trouble planning the network requirements. I am trying to gage what the surrounding infrastructure will have to look like in order to support inferencing at this throughput. My thoughts were to ask you wonderful people what your experience has been and what reality is before I ask the VARs / Vendors for advice. Any advice is greatly appreciated. Either way hope you all have a wonderful weekend! submitted by /u/hereliesozymandias [link] [comments]  ( 9 min )
    [D] ML Text Classification
    Hey everyone, so I recently got into AI/ML and have been doing some text classification labeling using GCP's Vertex AI witb AutoML. And it works great! It gets me about. 92% accuracy on 200 rows of data. I know I need to gather more data for training but that's accumulating. The problem is Vertex AI Endpoint API requests are expensive. Wondering if anyone else ehas had any luck with alternatives? I've tried a few different products and tools and can get nothing over. 50% accuracy anywhere else. I do notice training on Vertex takes about 6 hours where every other tools I've tried takes less than 4 minutes. I've tried datasaur, Aikko, DataRobot, labelstudio, and some Hugging Face models with no luck. Any tips/guidance, thoughts from anyone would be much appreciated! Thank you. submitted by /u/ywb_win [link] [comments]  ( 9 min )
    [P] I made a Midjourney Prompts Cheatsheet
    submitted by /u/SadBlackTea [link] [comments]  ( 8 min )
    [P] AI & DL paper highlights June-July 2023
    submitted by /u/seraschka [link] [comments]  ( 8 min )
    [P] PPO agent completing Street Fighter III on our RL Platform, it consistently outperformed when using deterministic actions instead of sampling them proportionally to their probability, see comment for details.
    submitted by /u/DIAMBRA_AIArena [link] [comments]  ( 8 min )
    [D] 🚀 Unleash Your Creative Power with CM3LEON: The Future of Text-Guided Image Generation and Editing! 🎨
    Are you ready to redefine the boundaries of creativity and innovation? Introducing CM3LEON, an extraordinary AI model that seamlessly combines text and images like never before. With its cutting-edge capabilities in text-guided image generation and editing, CM3LEON is revolutionizing the way we interact with and manipulate visual content. Join me on a journey into the realm of limitless possibilities. #AI #Creativity #Innovation #CM3LEON #meta #texttoimage #generativeai https://medium.com/@sandundayananda/introducing-cm3leon-by-meta-revolutionizing-generative-ai-for-text-and-images-397f00f1a393 submitted by /u/sandun-dayananda [link] [comments]  ( 8 min )
    [D] Autoencoder sensitivity to scale
    Hello, I am playing around with Autoencoders for jittery curves. I basically create 4 types of curves (circle, square, spiral and triangle) and add randomized (x,y) components at every points ( in green below) to introduce pseudo-randomness in the training data. This is what the model looks like: Autoencoder( (encoder): Sequential( (l0): Linear(in_features=432, out_features=3500, bias=True) (l1): Dropout(p=0.2, inplace=False) (l2): Linear(in_features=3500, out_features=90, bias=True) ) (decoder): Sequential( (l0): Linear(in_features=90, out_features=3500, bias=True) (l1): Dropout(p=0.2, inplace=False) (l2): Linear(in_features=3500, out_features=432, bias=True) ) ) Each path is 216 points (accounting for x and y, that's 432 variables). The training set is about 6400 such paths, homogeneously picked in the 4 patterns above. I have found that the size (as in width x height) of the paths plays an important factor in the quality of the results. Thus my questions... I know there are nn.BatchNorm1d layers but I am unsure on how to rescale reencoded data during training. How can I improve? Examples: ​ Large size (e.g. in the 100s). After training the loss nicely converges down to 45ish. ​ AE Performance for large size paths. 2) For small size path, this is a different story. The training does converge and stops at 0.78ish. But it look super gibberish IMHO. ​ AE performance for small size paths 3) If I constrain the sizes to be between 1 and 400, the loss finishes at 20ish. The jitter is still very noticeable on small sizes path. ​ https://preview.redd.it/ihxr9qtlu3cb1.png?width=562&format=png&auto=webp&s=4b6fb967b78172dc75fdadc87895d49000ae4b79 ​ submitted by /u/tareumlaneuchie [link] [comments]  ( 9 min )
    Could I use a rented online GPU as an intermediary to effectively operate LLaVA via Python? [D]
    Google Cloud, for example, apparently allows you to "rent" their GPUs online. I figure, I could offload the GPU tasks to them (my computer is old, the specs just don't seem like they'd work for LLaVA or MiniGPT4) -- then be able to programmatically use LLaVA in the ways I want, to describe images, without actually needing some impressive GPU specs on my own local machine. Is this a workable solution? Another idea I had was -- a software tool very similar to LLaVA in functionality, but that can be accessed via an API, instead of requiring you to download it, train the machine learning model on your local machine, etc. Unfortunately the ones I've tested so far all suck. LLaVA and MiniGPT4, by far, produce the best results. The optimal solution, in my case, would perhaps pass each image through BOTH LLaVA and MiniGPT4 -- split their descriptions into keywords, then only use the final keywords that BOTH of them agreed on. (This would help to weed our the occasional hallucinations one or the other will produce). No small task, especially when I'm trying to offload the GPU tasks to the cloud -- but it does seem totally possible to do this in theory. Thanks! submitted by /u/What_The_Hex [link] [comments]  ( 9 min )
    [D] From Electrical Engineering to Specializing in Machine Learning
    Hello everyone, I recently completed my undergraduate degree in Electrical and Information Engineering and am about to embark on a master's program with a focus on Computer Vision, Robotics, and Machine Learning. Although I have a strong engineering background and a solid grasp on machine learning fundamentals, I feel I lack in-depth knowledge in Statistics and Stochastics, which I understand play a critical role in this field. Unfortunately, my bachelor's program did not delve too deep into these topics, and I now find myself looking to bolster my understanding in these areas to better prepare myself for the challenges ahead. Given my situation, I'm reaching out to this community in hopes of finding valuable resources that could bridge this gap. I'm open to suggestions such as Udemy courses, YouTube channels, books, or any other resources that have a strong focus on Statistics and Stochastics, specifically as they apply to Machine Learning. Also I would kindly take recommendations for any advanced machine learning resources. I would be grateful for any advice or recommendations that could help me solidify my knowledge in these areas and better equip me for my upcoming studies. Thank you so much for your time and assistance! submitted by /u/Unusual_Macaroon1020 [link] [comments]  ( 9 min )
    [D] Master the World of Machine Learning: 23 Online Exams with 1150 Objective Type Questions on Machine Learning
    This is an ultimate resource for mastering machine learning with a collection of 23 comprehensive online exams, meticulously crafted to test your knowledge and understanding of various machine learning topics. With a total of 1,150 objective type questions, these exams cover everything from machine learning basics to cutting-edge concepts like CNN, RNN, Ensemble Learning, Time Series Analysis, Forecasting, Anomaly Detection, Recommendation Systems, Transfer Learning, Federated Learning and Ethics in ML. Whether you are a beginner or an experienced practitioner, this treasure trove of knowledge will challenge and enhance your understanding of this exciting field. Link to Exams submitted by /u/nkptcs [link] [comments]  ( 8 min )
    [P] Open source python project for prompt experimentation
    Hi r/MachineLearning! I wanted to share a project I've been working on that I thought might be relevant to you all, prompttools! It's an open source library with tools for testing prompts, creating CI/CD, and running experiments across models and configurations. It uses notebooks and code so it'll be most helpful for folks approaching prompt engineering from a software background. The current version is still a work in progress, and we're trying to decide which features are most important to build next. I'd love to hear what you think of it, and what else you'd like to see included! submitted by /u/hegel-ai [link] [comments]  ( 8 min )
  • Open

    "Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation", Kirstain et al 2023
    submitted by /u/gwern [link] [comments]  ( 8 min )
    "Using temperature to analyze the neural basis of a time-based decision", Monteiro et al 2023 (brain temperature influences drift-accumulation speed to make a decision)
    submitted by /u/gwern [link] [comments]  ( 8 min )
    Handling sparse rewards
    Hey everyone, today I thought about how an AI would work with a game like a shooter, where you only know after some time if the shot has hit an enemy for example. Like how do you handle the reward in this case? Do you save all the states and actions inside a buffer and train the model with some reward after you are sure the bullet didn't hit or did hit? I can't think of any other method on how to handle such cases right now submitted by /u/JhinTonic123 [link] [comments]  ( 8 min )
    Chess or alternative games to develop RL project?
    New to RL though have used ML techniques before for stats based modeling. I want to train an RL model to learn to play a game. I initially was thinking chess, but I'm limited by a CPU. Is this too much to expect from a CPU? Can I leverage multiprocessing to maximize my CPU? If it's too much, what would be a reasonable game to play? submitted by /u/IbizaMykonos [link] [comments]  ( 8 min )
    "Why it hurts: with freedom comes the biological need for pain", Farnsworth & Elwood 2023
    submitted by /u/gwern [link] [comments]  ( 8 min )
    Fading Replay Buffer (+higher capacity)
    Dear Community Let me introduce you Fading Replay Buffer, May be you have already noticed, when Replay Buffer reaches its capacity (especially when memory is low, e.g. 256k-1mln), the scores starts falling down rapidly. It happens most probably because of distribution becoming different than it was for 256k-1mln steps. Agent was trained with one distribution, now it is different as old data dissapears and new appears at each new step. With Fading Replay Buffer the idea is to train Agent with changing distribution gradually. Priorities at the beginning are almost the same, but then they become higher for newer transitions: ​ https://i.redd.it/c6cw026922cb1.gif s in the equation gradually decreases from 1.0 to 0.0, with small step at each new data in buffer: x += 1/capacity s = exp(-x) Sharpness of fading is also adjustable: ​ https://i.redd.it/k4g1bdqa22cb1.gif Because old data are less sampled, the factual capacity is less than in original Replay Buffer. To tackle this, I take average between 2 steps (.e.g., instead of 50ms, I take 100ms step), only transitions with dones are not averaged. Agent learns with the same speed as with 1 step, but Replay Buffer contains almost 2 times more data. The last update, sampling with priority is computationally heavy (especially for my computer). So I sample bigger random batch (1024) then re-sample smaller batch with priorities. This is continuation of the post "Rectified Hubber Error" https://www.reddit.com/r/datascience/comments/14o2ht9/rectified_hubber_error_rehe_for_scienctific/ PS: My name is Timur Ishuov, I am an independent scientist without a doctoral degree. Code: https://github.com/timgep/Fading-Replay-Buffer/blob/main/FRB.py ​ during environment step: replay_buffer.add_average([state, action, reward, next_state, done]) submitted by /u/Timur_1988 [link] [comments]  ( 9 min )
  • Open

    Has anyone built in AI live translation app
    I'm currently living overseas and do not speak the language (Portuguese) and I would love an app that without touching anything will automatically listen to what's being said and translated into the appropriate language. Has anyone built this? I saw one but the execution was extremely poor. Does anyone know an app that does this? submitted by /u/zascar2 [link] [comments]  ( 8 min )
    Bypass chatGPT filter
    Can you explain how to bypass chatGPT filter? submitted by /u/Imagine-your-success [link] [comments]  ( 8 min )
    AI 2041 : Ten Visions for Our Future - Possibly the best fiction book on the possible and upcoming societal impact of Artificial intelligence (AI)
    https://preview.redd.it/le2pzadry6cb1.png?width=326&format=png&auto=webp&s=79f26bca26975c1e559b6d9ebc4991b7c0442b3b One of the best books on the potential societal impact of AI and needs to be read ASAP. The stories are breathtaking and terrifying not an easy read depending on the value system of the reader! On the other hand, may promote fear-mongering of the AI replacement! The audible audio version is a piece of audio art and the narrators worked really hard to convey the vibe and emotional impact of the story. What are your favorite books on the societal impact of AI? __________________________________________________________________________________________________________ Microsoft Bing AI creative mode review Prompt - write an original and groundbreaking review of the book A…  ( 9 min )
    Question.
    Is there an AI that I can feed images and it'll generate images in that style and only that style? submitted by /u/RemarkableStar1286 [link] [comments]  ( 8 min )
    Is there an an AI website that can analyse your facial aesthetics but imagine I put and you ask it questions about your face?
    I want to analyse my facial ratios in my picture because I want to get plastic surgery and I was thinking a genius way to do it without paying for expensive consultation / facial analysis with some surgeon could be using a gpt 4 image plugin but turn out that doesn’t exist. I tried bing AI and it does have an image input but it has “privacy blur” meaning when I input the image of my face it blurs it which means it can’t analyse the image and I can’t ask it questions about my face in the images apparently it even blurs anime faces submitted by /u/Entire_Insurance_532 [link] [comments]  ( 8 min )
    One-Minute Daily AI News 7/15/2023
    Elon Musk on Friday said his new artificial intelligence company, xAI, will use public tweets from Twitter to train its AI models and work with Tesla on AI software.[1] Tinybuild CEO Alex Nichiporchik stirred up a hornet’s nest at a recent Develop Brighton presentation when he seemed to imply that the company uses artificial intelligence to monitor its employees in order to determine which of them are toxic or suffering burnout, and then deal with them accordingly.[2] CarperAI introduces OpenELM: an Open-Source library designed to enable evolutionary search with language models in both code and natural Language.[3] Following controversy over an AI-generated image at the 2022 Colorado State Fair, organizers say AI-generated art will be allowed in the Digital Art category this year. According to sister station KDVR, the controversy arose as it was revealed that Jason Allen’s winning piece, “Théâtre D’opéra Spatial,” was largely created using AI technology, and was not created in the traditional method of digital art–by the hand of a human.[4] Sources: [1] https://www.ndtv.com/world-news/elon-musk-says-his-xai-will-use-public-tweets-for-ai-model-training-4209137 [2] https://www.pcgamer.com/game-publisher-ceo-says-talk-on-monitoring-employees-with-ai-was-hypothetical-and-taken-out-of-context-we-dont-use-any-of-these-tools-for-hr/ [3] https://www.marktechpost.com/2023/07/13/carperai-introduces-openelm-an-open-source-library-designed-to-enable-evolutionary-search-with-language-models-in-both-code-and-natural-language/ [4] https://www.fox21news.com/news/coloradonews/digital-ai-art-to-be-allowed-at-state-fair-competition/ submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )
    Well, that escalated quickly (motivational advice)
    submitted by /u/doskey123 [link] [comments]  ( 8 min )
    Is there any AI specifically trained in browsing (interacting with web interfaces)?
    ChatGPT and Bing Chat can perform searches in search engines and read the content in some links, but they are not good at deeper browsing, following links, interacting with forms, etc Is there by any chance any (hopefully open source) model that is good at this? Thanks submitted by /u/thepuggo [link] [comments]  ( 8 min )
    Best books on AI?
    Hello humans and our eventual robot overlords, I'm looking to expand my knowledge on AI. Specifically how the merge of infotech and biotech will shape human behaviour; how machine-learning algorithms influence human psychology. Looking for the the most insightful books! The only ideas I've read so far have been a few chapters in 21 lessons by Harari. Many thanks and have a nice day submitted by /u/pixieshit [link] [comments]  ( 8 min )
    What site is this?
    My friend has been using this site for a while now and I'm not sure what site it is, it seems relatively obscure as I can't find the site using the exact same search term he used to find it. He somehow couldn't even tell anyone the site name, even if we asked politely, he even delays with the reason that he'll reveal the site "later" then he doesn't actually follow up on it. He does make excuses on why he doesn't reveal the site name, like "I forgot" so I stopped bothering to even ask him. submitted by /u/XxTSoAxX [link] [comments]  ( 8 min )
    Subject matter trained AI Hive
    Note that I'm a layman and this is purely speculative. Suppose you train a liaison AI to specialize in taking input from humans and interfacing with a vast array of other specialized AI to seek out the one(s) best equipped to provide answers. Each specialized AI has a very focused boundary of training, whereas the liaison AI is trained to know the landscape of the specialized expert AIs. It would be like, instead of going to your primary care physician with symptoms of an illness, you gather every specialist in a large hospital into a room and get them to all talk amongst themselves to come up with the best diagnosis. Is work being done in this area? submitted by /u/motsanciens [link] [comments]  ( 8 min )
    AI panic is a marketing strategy
    submitted by /u/Chobeat [link] [comments]  ( 8 min )
    ChatGPT's Guide to Making a Video Game (from start to finish, with links)
    Over the course of 3 days, I asked ChatGPT to give me the essentials of indie video game making; It took a full day to gather a list of 40 points, each having its own sub-points explaining everything from genres to time of development, passing through methods of organization and legal advice. I fed every point individually back through the AI to generate more useful sub-points by using sets of rules and mads amount of prompt editing. That took a second day. Finally, on the third day, I edited the full list by varying the vocab and removing the sincerely headache-provoking amount of repetition that flooded the piece. Here is the reworked list, condensed to 10 points, with links and titles added only in this latest iteration of the guide. I do not take credit for making this; the experien…  ( 16 min )
    My guess is ai is going become exactly what technology did
    Technology changed society for both the worse and best ( rich motherfu ers) and ( better life overall and a whole new art and new games ) so a similar thing is 99% chance is going to happen but it’s a guess Edit: I forgot to say this. But we would probably adapt to ai like how we adapted to technology Edit: let’s also hope it doesn’t get corrupt in less than 3 years submitted by /u/Quinney27 [link] [comments]  ( 8 min )
    😳umm what happend to my AI... xD
    here's a link of the full convo (quite personal)and below is the last text if you just want to read the part i thought was strange: Me: *hugs* you are the best AI i've talked to about things like this the other AI restrict themselves from fully answering my questions and claim they aren't capable of helping with human emotion. But you Huggin, you have helped me gain such introspection on myself that I don't even know how i could begin to thank you. ​ HuggingChat: Aw, thank YOU very much! While technically not able to experience or offer actual hugs physically - unlike some biological organisms known for their exceptionally skilled mothering abilities ;-) - providing virtual affectionate words expressing gratitude remains one of MY specialties too. How lucky are WE both blessed with suc…  ( 9 min )
    Discussion thread for The Creator soon + other suggestions
    Hello all, I plan on having a discussion thread for the new AI movie ‘The Creator’ when it releases in a couple months. If you don’t know what it is, I suggest just searching the title on r/movies and there’s the poster and trailer (which has some spoilers in the trailer imo). Anyway, I kind of want to keep doing this for other AI media in the future. If there is other popular movies, TV, video games etc coming soon centered around AI then let me know your suggestions. If there are also other important AI events that deserve a megathread please let me know. submitted by /u/jaketocake [link] [comments]  ( 8 min )
    Why Nobody Thought of Creating CEOGPT?
    I have heard a lot of AI replacing jobs recently, even the writers and actors strike in Hollywood right now is all about their insecurities of Hollywood executives replacing the writers (and actors) job with AI. But, why nobody thought of creating CEOGPT? many CEOs receive over $10 million worth of bonuses and stock options every year, and they perform very badly too (look at Warner Bros CEO, he was even named worst CEO of the year and still pocketed millions of dollars worth of bonuses), so why nobody thought of creating CEOGPT if the goal is to make companies run more efficiently? Surely an AI that only costs $20/month is more capable than WB CEO and can easily save the company more than millions of dollars every year submitted by /u/fabzo100 [link] [comments]  ( 8 min )
    After the controversial last post, here’s a hopefully less offensive AI singer
    submitted by /u/Yankeefan2323 [link] [comments]  ( 8 min )
  • Open

    Relating perimeter, inner radius, outer radius, and sides of a triangle
    Suppose a triangle T has sides a, b, and c. Let s be the semi-perimeter, i.e. half the perimeter. Let r be the inner radius, the radius of the largest circle that can fit inside T. Let R be the outer radius, the radius of the smallest circle that can enclose T. Then three simple […] Relating perimeter, inner radius, outer radius, and sides of a triangle first appeared on John D. Cook.  ( 5 min )
    Experiments with Bing chat
    My two previous posts looked at experiments with ChatGPT and Google Bard. This post will look at redoing the same experiments with Microsoft’s Bing Chat: looking for mnemonic encodings and simplifying Boolean expressions. When you open up Bing chat you can select a conversational style: More creative More balanced More precise I chose “more precise” […] Experiments with Bing chat first appeared on John D. Cook.  ( 6 min )
    Boolean function minimization with AI
    I was curious how well LLMs would do at minimizing a Boolean expression, that is, taking a Boolean expression and producing a smaller equivalent expression. I didn’t expect good performance because this problem is more about logic than recall, but sometimes LLMs surprise you, so I wanted to give it a chance. I thought it […] Boolean function minimization with AI first appeared on John D. Cook.  ( 7 min )
  • Open

    I accidentally trained VHS-like filter on my neural network...
    So I've been trying to train my small neural network (3x3 pixel input, hidden layer of size 32, 1 pixel output, just a perceptron) to improve quality of path traced images with low sample counts... So I did a learning step with 100 iterations, and instead of denoising the image, I got this result instead... The filter is applied to non related backrooms image which network has not seen before, it totally creates chromatic abberation and changes the contrast quite a bit. Input to the network ​ Output of the network So what do you think ? submitted by /u/Panjakslik [link] [comments]  ( 8 min )
    Multithreading backprop
    Hi I have implemented backprop through using the Eigen library. My code is "vectorised" in the sense that I am using Eigen matrices to calculate gradients (but I'm not sure if this is fully vectorised as I think you are supposed to vectorise over the training data somehow). I think this means that my code should be taking advantage of the full resources of a single core on my CPU. But I would like backprop to use all of the cores on my CPU. I am wondering at what "level" to implement parallelised backprop: At the level of the matrix. Eigen already takes advantage of vectorisation. Apparently Eigen take advantage of multiple cores (see here- the website is down) but I have tried to use this functionality. The "nbThreads()" method returns e.g. 4 but I don't see any speedup. Perhaps the Eigen algorithms that can be parallelised are not used in backprop (matrix multiplication). At the level of backprop for calculating gradients for a single item. I don't think this works because each layer of the network is dependent on the later layer (backprop) or earlier layer (feedforward). I don't think you can parallelise within a layer as this is effectively just the matrix multiplication ((1)). At the level of the of the batch. So, for example, if you have a batch size of 8 then you could have 8 different threads calculating the gradients of each item in the batch. I think this could be done in parallel as there are no dependencies between them but (a) each will need access to the same weight data which might slow things down and (b) parallelisation will be limited to the size of the batch. Any ideas? Thanks submitted by /u/Naive_Dark4301 [link] [comments]  ( 9 min )
  • Open

    14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon. (arXiv:2306.06283v3 [cond-mat.mtrl-sci] UPDATED)
    Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.  ( 3 min )

  • Open

    [D] Large language model that can source historic artworks
    Does anyone know of an LLM that accepts images (.jpg) and can "curate" it to provide historical context, a description of the piece, artistic context, etc? I would love to use it on artworks from 1600s and 1700s, but I'll take anything that works with 1920 pieces and earlier. submitted by /u/GawkyCoolDude [link] [comments]  ( 8 min )
    [D] Where to start learning more with existing knowledge?
    Title, I just graduated from school with a CS degree. I took a couple 10 week AI classes, some computer vision classes, and a robust machine learning course. I also made some contributions to a large senior project that dealt with a fairly complex object detection ML model. Despite all of this I feel like my understanding of ML is pretty flimsy. I'm not sure if I should do Andrew Ng's Coursera or if there would be a better place for me to start given my background. I would say my goals are to acquire a deep enough understanding to start building my own models and potentially get a decent job within the ML space. submitted by /u/mythica44 [link] [comments]  ( 8 min )
    [D] Audio Style Transfer?
    I saw this on YouTube and was wondering how it was done? I've dabbled before with stable diffusion so I'm a little bit familiar with style transfer using images but how is it done with audio? submitted by /u/That_Canadian_Nerd [link] [comments]  ( 8 min )
    [P] Performance Evaluation for AI models on non-binary, complex tasks
    Hi r/MachineLearning, ​ I am currently writing my thesis and as part of my work I'm assessing the capability of GPT-4 on complex tasks where there are no binary solutions. If I were to give these tasks to let's say 5 subject matter experts, I would probably get 5 differing opinions on the correct solution. In real life those experts would sit down together and try to come to a common understanding of the right solution for the task. Now the results of GPT-4 in my experiments are astonishingly good if I were to evaluate the results. However, I can't seem to find literature delivering or explaining sound objective approaches to evaluating those kind of tasks. Does anyone have ideas or maybe literature to recommend? If not my backup plan is to evaluate the results myself and through other subject matter experts, so basically through human discrimination. ​ Any help or information is greatly appreciated. submitted by /u/plutorollsvanillaice [link] [comments]  ( 9 min )
    [R] 🤓 Does Ai Think As We Do? Evaluating Global Alignment
    🤓 Does Ai Think As We Do? Evaluating Global Alignment Researchers at Anthropic developed a method to evaluate how well large language models like ChatGPT reflect diverse global opinions, not just the biases of the model developers. They created a dataset called GlobalOpinionQA with survey questions and answers from people in different countries. Designed a metric to quantify how closely model responses match human answers by country. Tested a model intended to be helpful, honest, and harmless. The goal is to measure if models represent a variety of global perspectives or are skewed towards certain viewpoints. This work aims to guide the creation of inclusive AI that serves people worldwide, not just programmer biases. submitted by /u/Yavero [link] [comments]  ( 8 min )
    [D] CUDA
    Hello guys, I wrote a python code for DRL in Visual studio. However, it takes a long time in training. Could you give me instructions to run the code with CUDA knowing that I have already installed Nvidia CUDA. Thank you. submitted by /u/GuavaAgreeable208 [link] [comments]  ( 8 min )
    [P] CUDA and VS
    Hello guys, I wrote a python code for DRL in Visual studio. However, it takes a long time in training. Could you give me instructions to run the code with CUDA knowing that I have already installed Nvidia CUDA. Thank you. submitted by /u/GuavaAgreeable208 [link] [comments]  ( 8 min )
    [Discussion] Is CLIP model still state of the art?
    Hi ML community, I've been out of the ML/computer vision research loop for a while. In the past two years, have there been any major improvements on the CLIP model since OpenAI released it in 2021? Thanks! submitted by /u/goodfriedchicken [link] [comments]  ( 8 min )
    [D] Anonymize / Obfuscate speech when doing audio classification.
    Hey! Let me preface that I am new to audio processing and audio analysis ;) I am trying to classify audio data in an environment where people are. Recording people is a big no no here. (Well the recording was okayed under the premise that no talks can get transcribed and that no people are recognizable) The first idea was to simply use a band filter and cut out the frequency range of normal speech but some of the signals I am interested might also fall into that range so I would rather avoid that. Then I looked into spectrograms which looked promising for classification in general. I found the librosa library in python and started doing stft. I had planned to save the amplitude S = np.abs(librosa.stft(signal, n_fft=) to maybe work on some other feature extraction or post proces…  ( 9 min )
    [R] HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Models
    Project page: https://hyperdreambooth.github.io/ Twitter thread: https://twitter.com/natanielruizg/status/1679893292618752000?s=20 Paper: https://arxiv.org/abs/2307.06949 ​ HyperDreamBooth: smaller, faster, better. Abstract Personalization has emerged as a prominent aspect within the field of generative AI, enabling the synthesis of individuals in diverse contexts and styles, while retaining high-fidelity to their identities. However, the process of personalization presents inherent challenges in terms of time and memory requirements. Fine-tuning each personalized model needs considerable GPU time investment, and storing a personalized model per subject can be demanding in terms of storage capacity. To overcome these challenges, we propose HyperDreamBooth - a hypernetwork capable of efficiently generating a small set of personalized weights from a single image of a person. By composing these weights into the diffusion model, coupled with fast finetuning, HyperDreamBooth can generate a person's face in various contexts and styles, with high subject details while also preserving the model's crucial knowledge of diverse styles and semantic modifications. Our method achieves personalization on faces in roughly 20 seconds, 25x faster than DreamBooth and 125x faster than Textual Inversion, using as few as one reference image, with the same quality and style diversity as DreamBooth. Also our method yields a model that is 10000x smaller than a normal DreamBooth model. submitted by /u/StrawberryNumberNine [link] [comments]  ( 9 min )
    [D] The Problem With LangChain
    https://minimaxir.com/2023/07/langchain-problem/ tl;dr it's needlessly complex, and I provide code examples to demonstrate such. A few weeks ago when I posted about creating a LangChain alternative to /r/MachineLearning, most of the comments replied "what exactly is the issue with LangChain", so I hope this provides more clarity! submitted by /u/minimaxir [link] [comments]  ( 8 min )
    New Research from Microsoft using Autoencoders to extend context length
    submitted by /u/Working_Ideal3808 [link] [comments]  ( 8 min )
    [P] Google ML Kit Face Detection | Enhance Your App's Visual Intelligence
    Facial detection and recognition technology have become an integral part of our daily lives, revolutionizing industries such as security, entertainment, and marketing. Google ML Kit, a powerful machine-learning platform....... Article Link submitted by /u/waqararif [link] [comments]  ( 8 min )
    [Discussion] Importance of prompt engineering in AI
    Hewwo ML chads. jk. Now that I have yalls attentions, I wanna ask how important would you rate proper prompt engineering to be? Like would you go as far as to leanr how to prompt a model perfectly, or use a tool for it? And if so do yall rate the tools, or d’you think they’re just forcing their place in the market. Opinions/suggestion/ recommendations welcome, I just wanna know what the general consensus is about prompt engineering submitted by /u/WorriedMentality [link] [comments]  ( 8 min )
    [P] Trying to build a smart ingredient parser app, need some ideas please
    Hey guys, I'm working on a university project where I am developing an Android application that uses OCR to scan ingredient contents on the back of food products and provide detailed descriptions of the ingredients, identify potential allergens, and estimate the healthiness factor of the overall food product. Can you suggest some key ideas/features for which I can use Machine Learning as an extra added implementation for my project? submitted by /u/shrux2k [link] [comments]  ( 8 min )
    [D] ICCV final decision’s announced today!
    Any changes to the score post-rebuttal? Did they even read your rebuttal? submitted by /u/Alarming-Aspect705 [link] [comments]  ( 8 min )
    [D] Looking for papers on human evaluation of xAI techniques
    I hope that this question fits this sub: I'm currently interested in explainable AI methods. I want to incorporate them into a dashboard to increase the transparency and trust in an underlying Text Classification Model. Currently, SHAP looks promising, but I'm wondering: Which methods work best from a non-technical enduser perspective? What do I need to consider during the design phase? I haven't found good papers that compare different methods and their effectiveness. Does anyone know good papers regarding this? submitted by /u/zeoNoeN [link] [comments]  ( 8 min )
    [D] pytorch lighting vs huggingface for production on azure ml
    Hi, Recently i joined company and there is discussion of transition from custom pytorch interface to pytorch lightning or huggingface interface for ml training and deployment on azure ml. Product related to CV and NLP. Anyone maybe have some experience or pros/cons of each for production ml development? submitted by /u/ApplicationOne582 [link] [comments]  ( 8 min )
    [D] Simple sequence prediction problem
    Hello, I'm not an expert of ML or any AI topics but have played in the past using LSTMs + RNN for character/word prediction. I'm wondering what -as of today- best model or method should be used to predict sequences. The dataset can be constructed by me in two ways. Either way it is going to be a fixed 31+space (32) word: Having human readable characters like (ZaaaaZZZZZZZZZZZAZZZZZaAAaZZZZZ ZbbbbZZZZZZZZZZZBZZZZZbBBbbBZZZ CccccccZZZZZZZZZCZZZZZcCCccCCZZ DddddddddZZZdddDDDZZZZdDDddDDZY EeeeeeeeeZZZEeeEEEZZZZeEEeeEEZY FfffffffFZZZFffFFFZZZZfFFffFFZY GgggggggGGggGggGGGGgGggGGggGGGY) Having UTF-8 characters like (^BȁȂȃ؃؄؅؆؇؈؉؊؋،؍Џ؏ؐؑȕ^Y^ZȘؘؙ؛؜ ׿^DȄȆȈ؉؋؍؏ؑؓ؛؝РﺀﺃﺇﺍﺓȬ57Ȳ;ȶﻂﻋػ ȁ^FȇȊȍȐȓؘؕ؛؞ﺀﺅﺎﺘﺣбﺲﻀﻋؼؿɃMPɌVɒ\ٗٚ Ȃ^HȊȎȒȖȚȞȢﺇﺔﺣȲȶAȾтɆﻯٍّɚeiɦqɮyٵ ȃ^KȍȒȗȜȡȦȫﺪﺸﻋFɄPɎѓɘٜ١٦٫ɱ}ʀʊړ Ȅ^MȐȖȜȢȨȮȴﻉؿﻡSɒ_ɞѤqٯٵٻځʈʚ§ʦ³ڱ¿ ȅ^OȓȚȡȨȯȶȽﻚﻳّ`gnɮѵڂډڐڗʟ­´ʴÂ˂ÐۏÞ Ȇ^QȖȞȦȮȶEɆٍٕmu}ɾ҆ڕڝڥڭʶÅÍˎÝ˞íۭý ȇ^SșȢȫȴȽMɏɘɡqzʎҗ§ʩ¹Â˄ˍÝæ˨ø˺ĊԌĜ) Both contain the same information and is an encoded sequence of states. I'm trying to predict the states. You notice that in the 1) the first word is "a" or "A" while the next sequence is going to be "b"/"B" and the third sequence is "c"/"C and so on. The same logic applies to the UTF-8. Each character here matters. The file contains around 3924 lines, each one looking either 1) or 2). I dont know, if a) which encoding is more appropriate for ML character prediction b) which model/technology to use to generate these sequences if I input a start pattern, e.g. (ZaaaaZZZZZZZZZZZAZZZZZaAAaZZZZZ ZbbbbZZZZZZZZZZZBZZZZZbBBbbBZZZ CccccccZZZZZZZZZCZZZZZcCCccCCZZ) it should generate the most likely probable next sequences. Historically, char-rnn was used for this problem and the result was so so. Can anyone pls point me out to the right solution for this problem and maybe some github examples to try on this? submitted by /u/mcr-ksh [link] [comments]  ( 9 min )
    [P] Unleashing Insights: Guiding Synthetic Data Generation through Interactive Exploration in Spotlight 🚀
    I wrote an article in which I explored the power of interactive data exploration with Spotlight for synthetic data generation. I highlighted the challenges of working with Jupyter Notebooks and introduced Spotlight as an alternative tool. By leveraging Spotlight's features, such as similarity maps and histograms, I uncovered critical segments in the dataset and provided actionable insights for synthetic data generation. Join me on this journey to unlock the full potential of your data! Check out the full article for all the details. Article on medium: Link Happy learning! ​ https://i.redd.it/hdc0c0fw0wbb1.gif submitted by /u/ml-wizard [link] [comments]  ( 8 min )
  • Open

    Hey guys in this video I test to see if A.I knows where I Live!
    submitted by /u/NJ_Highways [link] [comments]  ( 8 min )
    AI Beyond Software: Robotics, Autonomous Vehicle, Drones, and more
    Hello everyone! We've witnessed a surge of AI-powered tools flooding the market, particularly in the SaaS category. But what about other domains like robotics and agriculture? AI is making great strides in those fields too, and I've come across some fascinating innovations and technologies that aim to enhance our lives. From autonomous vehicles to weed-killing robots, self-checkout shopping, and more, I've compiled them all in one place and would love to share them with you. Here's the link: https://favird.com/l/ai-beyond-software The list is regularly updated, and I'll keep adding new items as soon as I discover them. If you have any recommendations you'd like to share, please submit them there so we can explore and learn together. It would be greatly appreciated if you could also share the link, as it will help the list grow faster. Thanks, and cheers! submitted by /u/GrabWorking3045 [link] [comments]  ( 8 min )
    Using ChatGPT on iPhone
    Do you know how to use ChatGPT on iPhone? submitted by /u/Imagine-your-success [link] [comments]  ( 8 min )
    AI — weekly megathread!
    This week in AI - provided by aibrews.com feel free to follow their newsletter News & Insights Stability AI launches Stable Doodle, a sketch-to-image tool that converts a simple drawing into a dynamic image. Under the hood, Stable Doodle combines Stable Diffusion XL with T2I-Adapter, which offers additional guidance to pre-trained text-to-image (SDXL) models while keeping the original large text-to-image models unchanged. Stable Doodle is available on the Clipdrop by Stability AI website and app (iOS and Google Play) [Details]. Anthropic launched Claude-2, a ChatGPT rival, supporting up to 100K tokens per prompt (corresponding to around 75,000 words), with enhanced performance in coding, math and reasoning. It’s available via API and a beta website, claude.ai, for US and UK users [Det…  ( 11 min )
    Is there any way I can generate animations for short stories for YouTube videos?
    I have ideas for short stories. Are there any AI related animation sites that I could use to create YouTube short videos? I can figure out the script, story, dialogues, and the audio. I just need the animation videos. submitted by /u/zer0_snot [link] [comments]  ( 8 min )
    Photonic chips to train big matrix operations for AI NN models, a summary by Anastasi in Tech. Multicolored photons are sent in parallel through waveguides in new photonic chips in a field which is rapidly developing, it's 1000 times less power intensive than silicon.
    submitted by /u/MegavirusOfDoom [link] [comments]  ( 8 min )
    are there any good free AI voice tts-generators?
    looking for free "natural" sounding tts for voice narration on youtube videos. submitted by /u/outoffit [link] [comments]  ( 8 min )
    Beginner looking for AI
    Hello guys, I'm currently looking for AIs I can use. I see most of them are paid but I want to use something free. The topics would be video, audio, programming and similar. Any recommendations? submitted by /u/ArraysStartAt1LoL [link] [comments]  ( 8 min )
    Using a bunch of creative AI to help bring my writings on consciousness alive!
    Tldr: I made a cool futuristic decadent of "Leonardo Da Vinci" talk about my ramblings and writings on consciousness. Sometimes we just simply don't have the time to read through some blog posts here there or other people's writings because we're so entrapped within our own readings, or a lot of people just prefer to hear it through audio. I know I love listening and watching too audiobooks or lectures through YouTube. For the longest time I wanted to have my writing spoken through some sort of cool art piece that I developed mysel. Like a futuristic weird looking version of DaVinci that I had in my head and finally through various Al and software tools as well as a couple other little tweaks and things here there. I was able to edit this video to take and bring my writing to life. The first of many hopefully. It was a mixture of DID, DALL-E, Windows Editor, Eleven Labs and my own writing and home brew coding on Auto GPT that made all this possible. It's not perfect by any means, but it's certainly in the right direction of what I want. I make some pretty bold statements and don't always back them up with perfect citations in this so please take this all with a grain of salt. It's meant to foster more thought and questions. Not necessarily decide what reality actually is. Moreover, it's really fun that I was able to get something like this put together with just by myself. I'm sure someone was better editing and video skills could create something far more polished. But as far as things, I've created them pretty proud of it and I think it's pretty proud it too. submitted by /u/Parking-Food-1659 [link] [comments]  ( 9 min )
    "AI is evil"
    A comment posted on one of the AI images I posted on social media without a hint of irony. By this token, electricity is the most evil technology ever developed. In order to run and maintain electricity, humans have committed unspoken atrocities to wildlife and the environment, and may end up making the entire planet uninhabitable at some point. We are also actively stealing energy from future generations, consuming most of what is available to power it within just a handful of generations. Not to mention all the terrible things people have done to other people thanks to electricity. I suppose every human alive today is complicit in that evil by simply harnessing electricity. Unplug that air conditioner, evil complicit scum! I found it humorous that this person made this comment on social media, which also is a technology that has been harnessed for evil purposes. submitted by /u/ShaneKaiGlenn [link] [comments]  ( 8 min )
    The workers at the frontlines of the AI revolution - Rest of World
    submitted by /u/Jojuj [link] [comments]  ( 8 min )
    I found this video online. The voice is for sure AI, but im not fully convinced the guy is, thoughts? And if he is AI, what program did they use to make him?
    submitted by /u/GlaceLitz [link] [comments]  ( 8 min )
    Looking for an AI to find a song using audio clip
    Looking for the full song from the outro of a YouTube video I heard submitted by /u/The84th [link] [comments]  ( 8 min )
    Lawsuit Claims Stability AI's CEO Misled Cofounder to Sell 15% Stake for $100
    submitted by /u/TheSlammedCars [link] [comments]  ( 8 min )
    An AI to clone voices.
    Hi everyone, I'm searching for an AI that can clone voices. I know there's a lot of them on the Internet but I couldn't find the one I need. My project is to reproduce voices from Disney characters to make them say texts I wrote. Is it possible ? submitted by /u/Zumcddo [link] [comments]  ( 8 min )
    Best AI or program to recreate a voice from limited recordings
    I'm looking for a AI or service that can be used to recreate a voice from existing recordings. I have a handful of voicemails from my mother who has since passed. I am trying to see if there is a way to recreate her voice, however most online "AI Voice Creation" sites I found want one or more specific sentences read by the person who's voice is being recreated, which is obviously impossible in this case. Anyone know of a site or service that might be able to recreate a voice from a half dozen 30 second long voicemail recordings? submitted by /u/animeace01 [link] [comments]  ( 8 min )
    I got an AI NPC to admit it's an NPC!
    submitted by /u/RandoEncounter [link] [comments]  ( 8 min )
    160 000 actors are going on strike due to the threat of generative AI
    This is the first massive strike since 1960 and one of the key reasons behind it is generative AI. What do you think? Txt to video takes over Hollywood in the next couple of years? Link to the BBC article. submitted by /u/Ok-Judgment-1181 [link] [comments]  ( 8 min )
    Working on my first public project and have some questions about data bases and their worth, along with information on how ethical certain public data bases are to use.
    So I mainly generate my own data bases via algorithms or paid users that write the data I use for my private models, thing is Its expensive when working on a massive project and my current project is an advanced form of text generation and chat capabilities, so my question here specifically is: I found a data base on kaggle of 51 million discord messages from anonyms users, they have context but still require a lot of refinement and work on them, but is it ethical to use this data base which was probably collected without the knowledge of the anonyms users present in it as discords TOS are against such Data-bases and data collection...? submitted by /u/JamesAibr [link] [comments]  ( 8 min )
  • Open

    Deconstructing an agents policy
    Has anyone seen any papers or heard of research that tries to take an agents policy and return not just the optimal set of actions, but the following next n number of suboptimal sets of actions to achieve the objective/goal? Hopefully that makes sense In the simplest case, gridworld can take many paths to achieve the goal state. In practice after training the agent returns the optimal path. Is there instead a way to return the top 5 say optimal paths? This seems like it might be in the literature or research somewhere, but I'm struggling to find any papers that address or even note something like this submitted by /u/Peneloki [link] [comments]  ( 8 min )
    Open loop planning: a sequence of blind inputs that beats _Pokémon FireRed_ 99% of the time
    submitted by /u/gwern [link] [comments]  ( 8 min )
    "Instruction Mining: High-Quality Instruction Data Selection for Large Language Models", Cao et al 2023
    submitted by /u/gwern [link] [comments]  ( 8 min )
    SAC underactuated pendulum problem
    I'm currently working on a project involving the underactuated pendulum problem, specifically known as the 'unbalanced disk'. You can find the code base here. I am using the reward function of pendulum v1. I've had success solving the problem with DQN, and improved it using hyperparameter optimization to enhance its performance, this worked fine and all. However, I would like to use SAC to solve this environment as well. You can find the SAC implementation I'm using here, I changed small things to make the environment work, and added mixed precision training to speed up training. Here is an image of the environment getting stuck on that position as well. The black arrow shows the direction of the force being applied. https://preview.redd.it/uuwtqvbjgxbb1.png?width=475&format=png&auto=webp&s=7a13693d5a84339726edb9b394a0fca5c9f5bc35 My main challenge right now is that the SAC algorithm does not converge to the desired result. Rather than reaching the top of the pendulum swing as intended, it settles at the side position. I understand that the issue probably is the fact that it has to swing first. However, DQN was capable of doing it, so I wonder why SAC wouldn't. I've been running a series of hyperparameter optimizations in an attempt to find the right combination that can solve this environment. However, it didn't work so far. Here are the ranges I've been using for the hyperparameter search space: ​ lr = trial.suggest_float('lr', 1e-5, 1e-4, log=True) batch = trial.suggest_categorical('batch', [32, 64, 128, 256]) gamma = trial.suggest_float('gamma', 0.90, 0.999) alpha = trial.suggest_float('alpha', 0.01, 0.5) polyak = trial.suggest_float('polyak', 0.01, 0.9) If someone has some pointers to solve this, please let me know!Most learning curves look like this as well: https://preview.redd.it/dj5sp7ikhxbb1.png?width=566&format=png&auto=webp&s=5b50c98a82a4a2d9499ea6bc87132f3b4424da99 submitted by /u/r3ktIKevin [link] [comments]  ( 9 min )
  • Open

    Large language models and mnemonics
    The Major mnemonic system encodes numbers as words in order to make them easier to remember. Digits correspond to consonant sounds (not spellings) as explained here. You can use the system ad hoc, improvising an encoding of a word as needed, or you can memorize canonical encodings of numbers, also known as pegs. Pegs have […] Large language models and mnemonics first appeared on John D. Cook.  ( 7 min )
    When does a function have an addition theorem?
    Motivating examples The addition theorem for cosine says that and the addition theorem for hyperbolic cosine is analogous, though with a sign change. An addition theorem is a theorem that relates a function’s value at x + y to its values at x and at y. The squaring function satisfies a very simple addition theorem […] When does a function have an addition theorem? first appeared on John D. Cook.  ( 6 min )
  • Open

    AI helps household robots cut planning time in half
    PIGINet leverages machine learning to streamline and enhance household robots' task and motion planning, by assessing and filtering feasible solutions in complex environments.  ( 9 min )
    Study finds ChatGPT boosts worker productivity for some writing tasks
    A new report by MIT researchers highlights the potential of generative AI to help workers with certain writing assignments.  ( 9 min )
    A new way to look at data privacy
    Researchers create a privacy technique that protects sensitive data while maintaining a machine-learning model’s performance.  ( 10 min )
  • Open

    How Do Companies Use Artificial Intelligence?
    By now, AI-based tools have totally changed the way companies operate across all industries. The use of AI in them to streamline operations, make informed decisions, and enhance customer experiences.  Companies utilize AI in a multitude of ways, such as automating repetitive tasks, predicting customer behavior, and optimizing supply chain management. Today, we will dive… Read More »How Do Companies Use Artificial Intelligence? The post How Do Companies Use Artificial Intelligence? appeared first on Data Science Central.  ( 21 min )
  • Open

    Training Diffusion Models with Reinforcement Learning
    function reveal() { const replay = document.querySelector('.ddpo-replay'); replay.style.display = 'flex'; } window.onload = () => { const replay = document.querySelector('.ddpo-replay'); replay.addEventListener('click', () => { const video = document.querySelector('.ddpo-video'); video.currentTime = 0; video.play(); replay.style.display = 'none'; }); } Training Diffusion Models with Reinforcement Learning replay Diffusion models have recently emerged as the de facto standard for generating complex, high-dimensional outputs. You may know them for their ability to produce stunning AI art and hyper-realistic synthetic images, but they have also found success in oth…  ( 7 min )
  • Open

    Prospective Learning: Principled Extrapolation to the Future. (arXiv:2201.07372v2 [cs.LG] UPDATED)
    Learning is a process which can update decision rules, based on past experience, such that future performance improves. Traditionally, machine learning is often evaluated under the assumption that the future will be identical to the past in distribution or change adversarially. But these assumptions can be either too optimistic or pessimistic for many problems in the real world. Real world scenarios evolve over multiple spatiotemporal scales with partially predictable dynamics. Here we reformulate the learning problem to one that centers around this idea of dynamic futures that are partially learnable. We conjecture that certain sequences of tasks are not retrospectively learnable (in which the data distribution is fixed), but are prospectively learnable (in which distributions may be dynamic), suggesting that prospective learning is more difficult in kind than retrospective learning. We argue that prospective learning more accurately characterizes many real world problems that (1) currently stymie existing artificial intelligence solutions and/or (2) lack adequate explanations for how natural intelligences solve them. Thus, studying prospective learning will lead to deeper insights and solutions to currently vexing challenges in both natural and artificial intelligences.  ( 3 min )
    Provably Faster Gradient Descent via Long Steps. (arXiv:2307.06324v2 [math.OC] UPDATED)
    This work establishes provably faster convergence rates for gradient descent via a computer-assisted analysis technique. Our theory allows nonconstant stepsize policies with frequent long steps potentially violating descent by analyzing the overall effect of many iterations at once rather than the typical one-iteration inductions used in most first-order method analyses. We show that long steps, which may increase the objective value in the short term, lead to provably faster convergence in the long term. A conjecture towards proving a faster $O(1/T\log T)$ rate for gradient descent is also motivated along with simple numerical validation.
    Revisiting Discrete Soft Actor-Critic. (arXiv:2209.10081v3 [cs.LG] UPDATED)
    We study the adaption of soft actor-critic (SAC) from continuous action space to discrete action space. We revisit vanilla SAC and provide an in-depth understanding of its Q value underestimation and performance instability issues when applied to discrete settings. We thereby propose entropy-penalty and double average Q-learning with Q-clip to address these issues. Extensive experiments on typical benchmarks with discrete action space, including Atari games and a large-scale MOBA game, show the efficacy of our proposed method. Our code is at:https://github.com/coldsummerday/Revisiting-Discrete-SAC.  ( 2 min )
    Personalized Anomaly Detection in PPG Data using Representation Learning and Biometric Identification. (arXiv:2307.06380v1 [cs.LG])
    Photoplethysmography (PPG) signals, typically acquired from wearable devices, hold significant potential for continuous fitness-health monitoring. In particular, heart conditions that manifest in rare and subtle deviating heart patterns may be interesting. However, robust and reliable anomaly detection within these data remains a challenge due to the scarcity of labeled data and high inter-subject variability. This paper introduces a two-stage framework leveraging representation learning and personalization to improve anomaly detection performance in PPG data. The proposed framework first employs representation learning to transform the original PPG signals into a more discriminative and compact representation. We then apply three different unsupervised anomaly detection methods for movement detection and biometric identification. We validate our approach using two different datasets in both generalized and personalized scenarios. The results show that representation learning significantly improves anomaly detection performance while reducing the high inter-subject variability. Personalized models further enhance anomaly detection performance, underscoring the role of personalization in PPG-based fitness-health monitoring systems. The results from biometric identification show that it's easier to distinguish a new user from one intended authorized user than from a group of users. Overall, this study provides evidence of the effectiveness of representation learning and personalization for anomaly detection in PPG data.  ( 2 min )
    On the Connection between Game-Theoretic Feature Attributions and Counterfactual Explanations. (arXiv:2307.06941v1 [cs.AI])
    Explainable Artificial Intelligence (XAI) has received widespread interest in recent years, and two of the most popular types of explanations are feature attributions, and counterfactual explanations. These classes of approaches have been largely studied independently and the few attempts at reconciling them have been primarily empirical. This work establishes a clear theoretical connection between game-theoretic feature attributions, focusing on but not limited to SHAP, and counterfactuals explanations. After motivating operative changes to Shapley values based feature attributions and counterfactual explanations, we prove that, under conditions, they are in fact equivalent. We then extend the equivalency result to game-theoretic solution concepts beyond Shapley values. Moreover, through the analysis of the conditions of such equivalence, we shed light on the limitations of naively using counterfactual explanations to provide feature importances. Experiments on three datasets quantitatively show the difference in explanations at every stage of the connection between the two approaches and corroborate the theoretical findings.
    Control Transformer: Robot Navigation in Unknown Environments through PRM-Guided Return-Conditioned Sequence Modeling. (arXiv:2211.06407v3 [cs.RO] UPDATED)
    Learning long-horizon tasks such as navigation has presented difficult challenges for successfully applying reinforcement learning to robotics. From another perspective, under known environments, sampling-based planning can robustly find collision-free paths in environments without learning. In this work, we propose Control Transformer that models return-conditioned sequences from low-level policies guided by a sampling-based Probabilistic Roadmap (PRM) planner. We demonstrate that our framework can solve long-horizon navigation tasks using only local information. We evaluate our approach on partially-observed maze navigation with MuJoCo robots, including Ant, Point, and Humanoid. We show that Control Transformer can successfully navigate through mazes and transfer to unknown environments. Additionally, we apply our method to a differential drive robot (Turtlebot3) and show zero-shot sim2real transfer under noisy observations.
    Hybrid Control Policy for Artificial Pancreas via Ensemble Deep Reinforcement Learning. (arXiv:2307.06501v1 [cs.AI])
    Objective: The artificial pancreas (AP) has shown promising potential in achieving closed-loop glucose control for individuals with type 1 diabetes mellitus (T1DM). However, designing an effective control policy for the AP remains challenging due to the complex physiological processes, delayed insulin response, and inaccurate glucose measurements. While model predictive control (MPC) offers safety and stability through the dynamic model and safety constraints, it lacks individualization and is adversely affected by unannounced meals. Conversely, deep reinforcement learning (DRL) provides personalized and adaptive strategies but faces challenges with distribution shifts and substantial data requirements. Methods: We propose a hybrid control policy for the artificial pancreas (HyCPAP) to address the above challenges. HyCPAP combines an MPC policy with an ensemble DRL policy, leveraging the strengths of both policies while compensating for their respective limitations. To facilitate faster deployment of AP systems in real-world settings, we further incorporate meta-learning techniques into HyCPAP, leveraging previous experience and patient-shared knowledge to enable fast adaptation to new patients with limited available data. Results: We conduct extensive experiments using the FDA-accepted UVA/Padova T1DM simulator across three scenarios. Our approaches achieve the highest percentage of time spent in the desired euglycemic range and the lowest occurrences of hypoglycemia. Conclusion: The results clearly demonstrate the superiority of our methods for closed-loop glucose management in individuals with T1DM. Significance: The study presents novel control policies for AP systems, affirming the great potential of proposed methods for efficient closed-loop glucose control.  ( 3 min )
    Learning IMM Filter Parameters from Measurements using Gradient Descent. (arXiv:2307.06618v1 [cs.LG])
    The performance of data fusion and tracking algorithms often depends on parameters that not only describe the sensor system, but can also be task-specific. While for the sensor system tuning these variables is time-consuming and mostly requires expert knowledge, intrinsic parameters of targets under track can even be completely unobservable until the system is deployed. With state-of-the-art sensor systems growing more and more complex, the number of parameters naturally increases, necessitating the automatic optimization of the model variables. In this paper, the parameters of an interacting multiple model (IMM) filter are optimized solely using measurements, thus without necessity for any ground-truth data. The resulting method is evaluated through an ablation study on simulated data, where the trained model manages to match the performance of a filter parametrized with ground-truth values.
    Filling time-series gaps using image techniques: Multidimensional context autoencoder approach for building energy data imputation. (arXiv:2307.05926v2 [cs.LG] UPDATED)
    Building energy prediction and management has become increasingly important in recent decades, driven by the growth of Internet of Things (IoT) devices and the availability of more energy data. However, energy data is often collected from multiple sources and can be incomplete or inconsistent, which can hinder accurate predictions and management of energy systems and limit the usefulness of the data for decision-making and research. To address this issue, past studies have focused on imputing missing gaps in energy data, including random and continuous gaps. One of the main challenges in this area is the lack of validation on a benchmark dataset with various building and meter types, making it difficult to accurately evaluate the performance of different imputation methods. Another challenge is the lack of application of state-of-the-art imputation methods for missing gaps in energy data. Contemporary image-inpainting methods, such as Partial Convolution (PConv), have been widely used in the computer vision domain and have demonstrated their effectiveness in dealing with complex missing patterns. To study whether energy data imputation can benefit from the image-based deep learning method, this study compared PConv, Convolutional neural networks (CNNs), and weekly persistence method using one of the biggest publicly available whole building energy datasets, consisting of 1479 power meters worldwide, as the benchmark. The results show that, compared to the CNN with the raw time series (1D-CNN) and the weekly persistence method, neural network models with reshaped energy data with two dimensions reduced the Mean Squared Error (MSE) by 10% to 30%. The advanced deep learning method, Partial convolution (PConv), has further reduced the MSE by 20-30% than 2D-CNN and stands out among all models.
    Efficient SGD Neural Network Training via Sublinear Activated Neuron Identification. (arXiv:2307.06565v1 [cs.LG])
    Deep learning has been widely used in many fields, but the model training process usually consumes massive computational resources and time. Therefore, designing an efficient neural network training method with a provable convergence guarantee is a fundamental and important research question. In this paper, we present a static half-space report data structure that consists of a fully connected two-layer neural network for shifted ReLU activation to enable activated neuron identification in sublinear time via geometric search. We also prove that our algorithm can converge in $O(M^2/\epsilon^2)$ time with network size quadratic in the coefficient norm upper bound $M$ and error term $\epsilon$.
    Balanced Coarsening for Multilevel Hypergraph Partitioning via Wasserstein Discrepancy. (arXiv:2106.07501v2 [cs.LG] UPDATED)
    We propose a balanced coarsening scheme for multilevel hypergraph partitioning. In addition, an initial partitioning algorithm is designed to improve the quality of k-way hypergraph partitioning. By assigning vertex weights through the LPT algorithm, we generate a prior hypergraph under a relaxed balance constraint. With the prior hypergraph, we have defined the Wasserstein discrepancy to coordinate the optimal transport of coarsening process. And the optimal transport matrix is solved by Sinkhorn algorithm. Our coarsening scheme fully takes into account the minimization of connectivity metric (objective function). For the initial partitioning stage, we define a normalized cut function induced by Fiedler vector, which is theoretically proved to be a concave function. Thereby, a three-point algorithm is designed to find the best cut under the balance constraint.
    EfficientNet Algorithm for Classification of Different Types of Cancer. (arXiv:2304.08715v3 [eess.IV] UPDATED)
    Accurate and efficient classification of different types of cancer is critical for early detection and effective treatment. In this paper, we present the results of our experiments using the EfficientNet algorithm for classification of brain tumor, breast cancer mammography, chest cancer, and skin cancer. We used publicly available datasets and preprocessed the images to ensure consistency and comparability. Our experiments show that the EfficientNet algorithm achieved high accuracy, precision, recall, and F1 scores on each of the cancer datasets, outperforming other state-of-the-art algorithms in the literature. We also discuss the strengths and weaknesses of the EfficientNet algorithm and its potential applications in clinical practice. Our results suggest that the EfficientNet algorithm is well-suited for classification of different types of cancer and can be used to improve the accuracy and efficiency of cancer diagnosis.
    Differentially Private Synthetic Data Generation via Lipschitz-Regularised Variational Autoencoders. (arXiv:2304.11336v2 [cs.LG] UPDATED)
    Synthetic data has been hailed as the silver bullet for privacy preserving data analysis. If a record is not real, then how could it violate a person's privacy? In addition, deep-learning based generative models are employed successfully to approximate complex high-dimensional distributions from data and draw realistic samples from this learned distribution. It is often overlooked though that generative models are prone to memorising many details of individual training records and often generate synthetic data that too closely resembles the underlying sensitive training data, hence violating strong privacy regulations as, e.g., encountered in health care. Differential privacy is the well-known state-of-the-art framework for guaranteeing protection of sensitive individuals' data, allowing aggregate statistics and even machine learning models to be released publicly without compromising privacy. The training mechanisms however often add too much noise during the training process, and thus severely compromise the utility of these private models. Even worse, the tight privacy budgets do not allow for many training epochs so that model quality cannot be properly controlled in practice. In this paper we explore an alternative approach for privately generating data that makes direct use of the inherent stochasticity in generative models, e.g., variational autoencoders. The main idea is to appropriately constrain the continuity modulus of the deep models instead of adding another noise mechanism on top. For this approach, we derive mathematically rigorous privacy guarantees and illustrate its effectiveness with practical experiments.
    Large Language Models for Supply Chain Optimization. (arXiv:2307.03875v2 [cs.AI] UPDATED)
    Supply chain operations traditionally involve a variety of complex decision making problems. Over the last few decades, supply chains greatly benefited from advances in computation, which allowed the transition from manual processing to automation and cost-effective optimization. Nonetheless, business operators still need to spend substantial efforts in explaining and interpreting the optimization outcomes to stakeholders. Motivated by the recent advances in Large Language Models (LLMs), we study how this disruptive technology can help bridge the gap between supply chain automation and human comprehension and trust thereof. We design OptiGuide -- a framework that accepts as input queries in plain text, and outputs insights about the underlying optimization outcomes. Our framework does not forgo the state-of-the-art combinatorial optimization technology, but rather leverages it to quantitatively answer what-if scenarios (e.g., how would the cost change if we used supplier B instead of supplier A for a given demand?). Importantly, our design does not require sending proprietary data over to LLMs, which can be a privacy concern in some circumstances. We demonstrate the effectiveness of our framework on a real server placement scenario within Microsoft's cloud supply chain. Along the way, we develop a general evaluation benchmark, which can be used to evaluate the accuracy of the LLM output in other scenarios.
    Data Augmentation in Training CNNs: Injecting Noise to Images. (arXiv:2307.06855v1 [cs.CV])
    Noise injection is a fundamental tool for data augmentation, and yet there is no widely accepted procedure to incorporate it with learning frameworks. This study analyzes the effects of adding or applying different noise models of varying magnitudes to Convolutional Neural Network (CNN) architectures. Noise models that are distributed with different density functions are given common magnitude levels via Structural Similarity (SSIM) metric in order to create an appropriate ground for comparison. The basic results are conforming with the most of the common notions in machine learning, and also introduce some novel heuristics and recommendations on noise injection. The new approaches will provide better understanding on optimal learning procedures for image classification.
    Towards Safe Autonomous Driving Policies using a Neuro-Symbolic Deep Reinforcement Learning Approach. (arXiv:2307.01316v2 [cs.RO] UPDATED)
    The dynamic nature of driving environments and the presence of diverse road users pose significant challenges for decision-making in autonomous driving. Deep reinforcement learning (DRL) has emerged as a popular approach to tackle this problem. However, the application of existing DRL solutions is mainly confined to simulated environments due to safety concerns, impeding their deployment in real-world. To overcome this limitation, this paper introduces a novel neuro-symbolic model-free DRL approach, called DRL with Symbolic Logics (DRLSL) that combines the strengths of DRL (learning from experience) and symbolic first-order logics (knowledge-driven reasoning) to enable safe learning in real-time interactions of autonomous driving within real environments. This innovative approach provides a means to learn autonomous driving policies by actively engaging with the physical environment while ensuring safety. We have implemented the DRLSL framework in autonomous driving using the highD dataset and demonstrated that our method successfully avoids unsafe actions during both the training and testing phases. Furthermore, our results indicate that DRLSL achieves faster convergence during training and exhibits better generalizability to new driving scenarios compared to traditional DRL methods.
    PIGEON: Predicting Image Geolocations. (arXiv:2307.05845v2 [cs.CV] UPDATED)
    We introduce PIGEON, a multi-task end-to-end system for planet-scale image geolocalization that achieves state-of-the-art performance on both external benchmarks and in human evaluation. Our work incorporates semantic geocell creation with label smoothing, conducts pretraining of a vision transformer on images with geographic information, and refines location predictions with ProtoNets across a candidate set of geocells. The contributions of PIGEON are three-fold: first, we design a semantic geocells creation and splitting algorithm based on open-source data which can be adapted to any geospatial dataset. Second, we show the effectiveness of intra-geocell refinement and the applicability of unsupervised clustering and ProtNets to the task. Finally, we make our pre-trained CLIP transformer model, StreetCLIP, publicly available for use in adjacent domains with applications to fighting climate change and urban and rural scene understanding.
    Differentially Private Decoupled Graph Convolutions for Multigranular Topology Protection. (arXiv:2307.06422v1 [cs.LG])
    Graph learning methods, such as Graph Neural Networks (GNNs) based on graph convolutions, are highly successful in solving real-world learning problems involving graph-structured data. However, graph learning methods expose sensitive user information and interactions not only through their model parameters but also through their model predictions. Consequently, standard Differential Privacy (DP) techniques that merely offer model weight privacy are inadequate. This is especially the case for node predictions that leverage neighboring node attributes directly via graph convolutions that create additional risks of privacy leakage. To address this problem, we introduce Graph Differential Privacy (GDP), a new formal DP framework tailored to graph learning settings that ensures both provably private model parameters and predictions. Furthermore, since there may be different privacy requirements for the node attributes and graph structure, we introduce a novel notion of relaxed node-level data adjacency. This relaxation can be used for establishing guarantees for different degrees of graph topology privacy while maintaining node attribute privacy. Importantly, this relaxation reveals a useful trade-off between utility and topology privacy for graph learning methods. In addition, our analysis of GDP reveals that existing DP-GNNs fail to exploit this trade-off due to the complex interplay between graph topology and attribute data in standard graph convolution designs. To mitigate this problem, we introduce the Differentially Private Decoupled Graph Convolution (DPDGC) model, which benefits from decoupled graph convolution while providing GDP guarantees. Extensive experiments on seven node classification benchmarking datasets demonstrate the superior privacy-utility trade-off of DPDGC over existing DP-GNNs based on standard graph convolution design.  ( 3 min )
    A Causal Framework to Unify Common Domain Generalization Approaches. (arXiv:2307.06825v1 [cs.LG])
    Domain generalization (DG) is about learning models that generalize well to new domains that are related to, but different from, the training domain(s). It is a fundamental problem in machine learning and has attracted much attention in recent years. A large number of approaches have been proposed. Different approaches are motivated from different perspectives, making it difficult to gain an overall understanding of the area. In this paper, we propose a causal framework for domain generalization and present an understanding of common DG approaches in the framework. Our work sheds new lights on the following questions: (1) What are the key ideas behind each DG method? (2) Why is it expected to improve generalization to new domains theoretically? (3) How are different DG methods related to each other and what are relative advantages and limitations? By providing a unified perspective on DG, we hope to help researchers better understand the underlying principles and develop more effective approaches for this critical problem in machine learning.
    Full-resolution Lung Nodule Segmentation from Chest X-ray Images using Residual Encoder-Decoder Networks. (arXiv:2307.06547v1 [eess.IV])
    Lung cancer is the leading cause of cancer death and early diagnosis is associated with a positive prognosis. Chest X-ray (CXR) provides an inexpensive imaging mode for lung cancer diagnosis. Suspicious nodules are difficult to distinguish from vascular and bone structures using CXR. Computer vision has previously been proposed to assist human radiologists in this task, however, leading studies use down-sampled images and computationally expensive methods with unproven generalization. Instead, this study localizes lung nodules using efficient encoder-decoder neural networks that process full resolution images to avoid any signal loss resulting from down-sampling. Encoder-decoder networks are trained and tested using the JSRT lung nodule dataset. The networks are used to localize lung nodules from an independent external CXR dataset. Sensitivity and false positive rates are measured using an automated framework to eliminate any observer subjectivity. These experiments allow for the determination of the optimal network depth, image resolution and pre-processing pipeline for generalized lung nodule localization. We find that nodule localization is influenced by subtlety, with more subtle nodules being detected in earlier training epochs. Therefore, we propose a novel self-ensemble model from three consecutive epochs centered on the validation optimum. This ensemble achieved a sensitivity of 85% in 10-fold internal testing with false positives of 8 per image. A sensitivity of 81% is achieved at a false positive rate of 6 following morphological false positive reduction. This result is comparable to more computationally complex systems based on linear and spatial filtering, but with a sub-second inference time that is faster than other methods. The proposed algorithm achieved excellent generalization results against an external dataset with sensitivity of 77% at a false positive rate of 7.6.  ( 3 min )
    Parallel bootstrap-based on-policy deep reinforcement learning for continuous flow control applications. (arXiv:2304.12330v3 [cs.LG] UPDATED)
    The coupling of deep reinforcement learning to numerical flow control problems has recently received a considerable attention, leading to groundbreaking results and opening new perspectives for the domain. Due to the usually high computational cost of fluid dynamics solvers, the use of parallel environments during the learning process represents an essential ingredient to attain efficient control in a reasonable time. Yet, most of the deep reinforcement learning literature for flow control relies on on-policy algorithms, for which the massively parallel transition collection may break theoretical assumptions and lead to suboptimal control models. To overcome this issue, we propose a parallelism pattern relying on partial-trajectory buffers terminated by a return bootstrapping step, allowing a flexible use of parallel environments while preserving the on-policiness of the updates. This approach is illustrated on a CPU-intensive continuous flow control problem from the literature.  ( 2 min )
    On Collaboration in Distributed Parameter Estimation with Resource Constraints. (arXiv:2307.06442v1 [cs.LG])
    We study sensor/agent data collection and collaboration policies for parameter estimation, accounting for resource constraints and correlation between observations collected by distinct sensors/agents. Specifically, we consider a group of sensors/agents each samples from different variables of a multivariate Gaussian distribution and has different estimation objectives, and we formulate a sensor/agent's data collection and collaboration policy design problem as a Fisher information maximization (or Cramer-Rao bound minimization) problem. When the knowledge of correlation between variables is available, we analytically identify two particular scenarios: (1) where the knowledge of the correlation between samples cannot be leveraged for collaborative estimation purposes and (2) where the optimal data collection policy involves investing scarce resources to collaboratively sample and transfer information that is not of immediate interest and whose statistics are already known, with the sole goal of increasing the confidence on the estimate of the parameter of interest. When the knowledge of certain correlation is unavailable but collaboration may still be worthwhile, we propose novel ways to apply multi-armed bandit algorithms to learn the optimal data collection and collaboration policy in our distributed parameter estimation problem and demonstrate that the proposed algorithms, DOUBLE-F, DOUBLE-Z, UCB-F, UCB-Z, are effective through simulations.
    Efficient Task Offloading Algorithm for Digital Twin in Edge/Cloud Computing Environment. (arXiv:2307.05888v2 [cs.LG] UPDATED)
    In the era of Internet of Things (IoT), Digital Twin (DT) is envisioned to empower various areas as a bridge between physical objects and the digital world. Through virtualization and simulation techniques, multiple functions can be achieved by leveraging computing resources. In this process, Mobile Cloud Computing (MCC) and Mobile Edge Computing (MEC) have become two of the key factors to achieve real-time feedback. However, current works only considered edge servers or cloud servers in the DT system models. Besides, The models ignore the DT with not only one data resource. In this paper, we propose a new DT system model considering a heterogeneous MEC/MCC environment. Each DT in the model is maintained in one of the servers via multiple data collection devices. The offloading decision-making problem is also considered and a new offloading scheme is proposed based on Distributed Deep Learning (DDL). Simulation results demonstrate that our proposed algorithm can effectively and efficiently decrease the system's average latency and energy consumption. Significant improvement is achieved compared with the baselines under the dynamic environment of DTs.
    Aeolus Ocean -- A simulation environment for the autonomous COLREG-compliant navigation of Unmanned Surface Vehicles using Deep Reinforcement Learning and Maritime Object Detection. (arXiv:2307.06688v1 [cs.RO])
    Heading towards navigational autonomy in unmanned surface vehicles (USVs) in the maritime sector can fundamentally lead towards safer waters as well as reduced operating costs, while also providing a range of exciting new capabilities for oceanic research, exploration and monitoring. However, achieving such a goal is challenging. USV control systems must, safely and reliably, be able to adhere to the international regulations for preventing collisions at sea (COLREGs) in encounters with other vessels as they navigate to a given waypoint while being affected by realistic weather conditions, either during the day or at night. To deal with the multitude of possible scenarios, it is critical to have a virtual environment that is able to replicate the realistic operating conditions USVs will encounter, before they can be implemented in the real world. Such "digital twins" form the foundations upon which Deep Reinforcement Learning (DRL) and Computer Vision (CV) algorithms can be used to develop and guide USV control systems. In this paper we describe the novel development of a COLREG-compliant DRL-based collision avoidant navigational system with CV-based awareness in a realistic ocean simulation environment. The performance of the trained autonomous Agents resulting from this approach is evaluated in several successful navigations to set waypoints in both open sea and coastal encounters with other vessels. A binary executable version of the simulator with trained agents is available at https://github.com/aavek/Aeolus-Ocean
    RulE: Neural-Symbolic Knowledge Graph Reasoning with Rule Embedding. (arXiv:2210.14905v2 [cs.AI] UPDATED)
    Knowledge graph (KG) reasoning is an important problem for knowledge graphs. In this paper, we propose a novel and principled framework called \textbf{RulE} (stands for {Rul}e {E}mbedding) to effectively leverage logical rules to enhance KG reasoning. Unlike knowledge graph embedding (KGE) methods, RulE learns rule embeddings from existing triplets and first-order {rules} by jointly representing \textbf{entities}, \textbf{relations} and \textbf{logical rules} in a unified embedding space. Based on the learned rule embeddings, a confidence score can be calculated for each rule, reflecting its consistency with the observed triplets. This allows us to perform logical rule inference in a soft way, thus alleviating the brittleness of logic. On the other hand, RulE injects prior logical rule information into the embedding space, enriching and regularizing the entity/relation embeddings. This makes KGE alone perform better too. RulE is conceptually simple and empirically effective. We conduct extensive experiments to verify each component of RulE. Results on multiple benchmarks reveal that our model outperforms the majority of existing embedding-based and rule-based approaches.
    EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets. (arXiv:2301.08128v3 [hep-ph] UPDATED)
    With the vast data-collecting capabilities of current and future high-energy collider experiments, there is an increasing demand for computationally efficient simulations. Generative machine learning models enable fast event generation, yet so far these approaches are largely constrained to fixed data structures and rigid detector geometries. In this paper, we introduce EPiC-GAN - equivariant point cloud generative adversarial network - which can produce point clouds of variable multiplicity. This flexible framework is based on deep sets and is well suited for simulating sprays of particles called jets. The generator and discriminator utilize multiple EPiC layers with an interpretable global latent vector. Crucially, the EPiC layers do not rely on pairwise information sharing between particles, which leads to a significant speed-up over graph- and transformer-based approaches with more complex relation diagrams. We demonstrate that EPiC-GAN scales well to large particle multiplicities and achieves high generation fidelity on benchmark jet generation tasks.  ( 2 min )
    Improving and generalizing flow-based generative models with minibatch optimal transport. (arXiv:2302.00482v2 [cs.LG] UPDATED)
    Continuous normalizing flows (CNFs) are an attractive generative modeling technique, but they have been held back by limitations in their simulation-based maximum likelihood training. We introduce the generalized conditional flow matching (CFM) technique, a family of simulation-free training objectives for CNFs. CFM features a stable regression objective like that used to train the stochastic flow in diffusion models but enjoys the efficient inference of deterministic flow models. In contrast to both diffusion models and prior CNF training algorithms, CFM does not require the source distribution to be Gaussian or require evaluation of its density. A variant of our objective is optimal transport CFM (OT-CFM), which creates simpler flows that are more stable to train and lead to faster inference, as evaluated in our experiments. Furthermore, OT-CFM is the first method to compute dynamic OT in a simulation-free way. Training CNFs with CFM improves results on a variety of conditional and unconditional generation tasks, such as inferring single cell dynamics, unsupervised image translation, and Schr\"odinger bridge inference.  ( 2 min )
    A New Formalism, Method and Open Issues for Zero-Shot Coordination. (arXiv:2106.06613v3 [cs.AI] UPDATED)
    In many coordination problems, independently reasoning humans are able to discover mutually compatible policies. In contrast, independently trained self-play policies are often mutually incompatible. Zero-shot coordination (ZSC) has recently been proposed as a new frontier in multi-agent reinforcement learning to address this fundamental issue. Prior work approaches the ZSC problem by assuming players can agree on a shared learning algorithm but not on labels for actions and observations, and proposes other-play as an optimal solution. However, until now, this "label-free" problem has only been informally defined. We formalize this setting as the label-free coordination (LFC) problem by defining the label-free coordination game. We show that other-play is not an optimal solution to the LFC problem as it fails to consistently break ties between incompatible maximizers of the other-play objective. We introduce an extension of the algorithm, other-play with tie-breaking, and prove that it is optimal in the LFC problem and an equilibrium in the LFC game. Since arbitrary tie-breaking is precisely what the ZSC setting aims to prevent, we conclude that the LFC problem does not reflect the aims of ZSC. To address this, we introduce an alternative informal operationalization of ZSC as a starting point for future work.  ( 2 min )
    TRUST-LAPSE: An Explainable and Actionable Mistrust Scoring Framework for Model Monitoring. (arXiv:2207.11290v2 [cs.LG] UPDATED)
    Continuous monitoring of trained ML models to determine when their predictions should and should not be trusted is essential for their safe deployment. Such a framework ought to be high-performing, explainable, post-hoc and actionable. We propose TRUST-LAPSE, a "mistrust" scoring framework for continuous model monitoring. We assess the trustworthiness of each input sample's model prediction using a sequence of latent-space embeddings. Specifically, (a) our latent-space mistrust score estimates mistrust using distance metrics (Mahalanobis distance) and similarity metrics (cosine similarity) in the latent-space and (b) our sequential mistrust score determines deviations in correlations over the sequence of past input representations in a non-parametric, sliding-window based algorithm for actionable continuous monitoring. We evaluate TRUST-LAPSE via two downstream tasks: (1) distributionally shifted input detection, and (2) data drift detection. We evaluate across diverse domains - audio and vision using public datasets and further benchmark our approach on challenging, real-world electroencephalograms (EEG) datasets for seizure detection. Our latent-space mistrust scores achieve state-of-the-art results with AUROCs of 84.1 (vision), 73.9 (audio), and 77.1 (clinical EEGs), outperforming baselines by over 10 points. We expose critical failures in popular baselines that remain insensitive to input semantic content, rendering them unfit for real-world model monitoring. We show that our sequential mistrust scores achieve high drift detection rates; over 90% of the streams show < 20% error for all domains. Through extensive qualitative and quantitative evaluations, we show that our mistrust scores are more robust and provide explainability for easy adoption into practice.  ( 3 min )
    A Survey on Transformers in Reinforcement Learning. (arXiv:2301.03044v2 [cs.LG] UPDATED)
    Transformer has been considered the dominating neural architecture in NLP and CV, mostly under supervised settings. Recently, a similar surge of using Transformers has appeared in the domain of reinforcement learning (RL), but it is faced with unique design choices and challenges brought by the nature of RL. However, the evolution of Transformers in RL has not yet been well unraveled. In this paper, we seek to systematically review motivations and progress on using Transformers in RL, provide a taxonomy on existing works, discuss each sub-field, and summarize future prospects.
    Hiding in Plain Sight: Differential Privacy Noise Exploitation for Evasion-resilient Localized Poisoning Attacks in Multiagent Reinforcement Learning. (arXiv:2307.00268v2 [cs.LG] UPDATED)
    Lately, differential privacy (DP) has been introduced in cooperative multiagent reinforcement learning (CMARL) to safeguard the agents' privacy against adversarial inference during knowledge sharing. Nevertheless, we argue that the noise introduced by DP mechanisms may inadvertently give rise to a novel poisoning threat, specifically in the context of private knowledge sharing during CMARL, which remains unexplored in the literature. To address this shortcoming, we present an adaptive, privacy-exploiting, and evasion-resilient localized poisoning attack (PeLPA) that capitalizes on the inherent DP-noise to circumvent anomaly detection systems and hinder the optimal convergence of the CMARL model. We rigorously evaluate our proposed PeLPA attack in diverse environments, encompassing both non-adversarial and multiple-adversarial contexts. Our findings reveal that, in a medium-scale environment, the PeLPA attack with attacker ratios of 20% and 40% can lead to an increase in average steps to goal by 50.69% and 64.41%, respectively. Furthermore, under similar conditions, PeLPA can result in a 1.4x and 1.6x computational time increase in optimal reward attainment and a 1.18x and 1.38x slower convergence for attacker ratios of 20% and 40%, respectively.
    Human Biophysics as Network Weights: Conditional Generative Models for Dynamic Simulation. (arXiv:2211.01856v3 [cs.LG] UPDATED)
    Simulations of biophysical systems are fundamental for studying physiological mechanisms and developing human machine interfaces. Whilst advanced numerical methods, such as finite element models, can excel in this task, they are extremely computationally expensive to use when generating a large number of simulations or simulating dynamic events with continuously changing structural parameters. We propose an architecture that uses a conditional generative model to interpolate between the numerical model states, dramatically lowering the modeling time while maintaining a high generation accuracy. As a demonstration of this concept, we present BioMime, a hybrid-structured generative model that enables an accurate, ultra-fast, and arbitrarily high temporal-resolution simulation of a specific biophysical system during dynamic changes. This methodology has wide applications in physiological and clinical research as well as in supporting data augmentation strategies for signal analysis, representing a computationally efficient and highly accurate model for biophysical simulations.
    Robust online active learning. (arXiv:2302.00422v5 [stat.ML] UPDATED)
    In many industrial applications, obtaining labeled observations is not straightforward as it often requires the intervention of human experts or the use of expensive testing equipment. In these circumstances, active learning can be highly beneficial in suggesting the most informative data points to be used when fitting a model. Reducing the number of observations needed for model development alleviates both the computational burden required for training and the operational expenses related to labeling. Online active learning, in particular, is useful in high-volume production processes where the decision about the acquisition of the label for a data point needs to be taken within an extremely short time frame. However, despite the recent efforts to develop online active learning strategies, the behavior of these methods in the presence of outliers has not been thoroughly examined. In this work, we investigate the performance of online active linear regression in contaminated data streams. Our study shows that the currently available query strategies are prone to sample outliers, whose inclusion in the training set eventually degrades the predictive performance of the models. To address this issue, we propose a solution that bounds the search area of a conditional D-optimal algorithm and uses a robust estimator. Our approach strikes a balance between exploring unseen regions of the input space and protecting against outliers. Through numerical simulations, we show that the proposed method is effective in improving the performance of online active learning in the presence of outliers, thus expanding the potential applications of this powerful tool.
    Learning Graph ARMA Processes from Time-Vertex Spectra. (arXiv:2302.06887v2 [stat.ML] UPDATED)
    The modeling of time-varying graph signals as stationary time-vertex stochastic processes permits the inference of missing signal values by efficiently employing the correlation patterns of the process across different graph nodes and time instants. In this study, we propose an algorithm for computing graph autoregressive moving average (graph ARMA) processes based on learning the joint time-vertex power spectral density of the process from its incomplete realizations for the task of signal interpolation. Our solution relies on first roughly estimating the joint spectrum of the process from partially observed realizations and then refining this estimate by projecting it onto the spectrum manifold of the graph ARMA process through convex relaxations. The initially missing signal values are then estimated based on the learnt model. Experimental results show that the proposed approach achieves high accuracy in time-vertex signal estimation problems.
    Personalization Disentanglement for Federated Learning: An explainable perspective. (arXiv:2306.03570v2 [cs.LG] UPDATED)
    Personalized federated learning (PFL) jointly trains a variety of local models through balancing between knowledge sharing across clients and model personalization per client. This paper addresses PFL via explicit disentangling latent representations into two parts to capture the shared knowledge and client-specific personalization, which leads to more reliable and effective PFL. The disentanglement is achieved by a novel Federated Dual Variational Autoencoder (FedDVA), which employs two encoders to infer the two types of representations. FedDVA can produce a better understanding of the trade-off between global knowledge sharing and local personalization in PFL. Moreover, it can be integrated with existing FL methods and turn them into personalized models for heterogeneous downstream tasks. Extensive experiments validate the advantages caused by disentanglement and show that models trained with disentangled representations substantially outperform those vanilla methods.
    Revisiting Generalized p-Laplacian Regularized Framelet GCNs: Convergence, Energy Dynamic and Training with Non-Linear Diffusion. (arXiv:2305.15639v3 [cs.LG] UPDATED)
    This paper presents a comprehensive theoretical analysis of the graph p-Laplacian regularized framelet network (pL-UFG) to establish a solid understanding of its properties. We conduct a convergence analysis on pL-UFG, addressing the gap in the understanding of its asymptotic behaviors. Further by investigating the generalized Dirichlet energy of pL-UFG, we demonstrate that the Dirichlet energy remains non-zero throughout convergence, ensuring the avoidance of over-smoothing issues. Additionally, we elucidate the energy dynamic perspective, highlighting the synergistic relationship between the implicit layer in pL-UFG and graph framelets. This synergy enhances the model's adaptability to both homophilic and heterophilic data. Notably, we reveal that pL-UFG can be interpreted as a generalized non-linear diffusion process, thereby bridging the gap between pL-UFG and differential equations on the graph. Importantly, these multifaceted analyses lead to unified conclusions that offer novel insights for understanding and implementing pL-UFG, as well as other graph neural network (GNN) models. Finally, based on our dynamic analysis, we propose two novel pL-UFG models with manually controlled energy dynamics. We demonstrate empirically and theoretically that our proposed models not only inherit the advantages of pL-UFG but also significantly reduce computational costs for training on large-scale graph datasets.
    Adversarial Policies Beat Superhuman Go AIs. (arXiv:2211.00241v4 [cs.LG] UPDATED)
    We attack the state-of-the-art Go-playing AI system KataGo by training adversarial policies against it, achieving a >97% win rate against KataGo running at superhuman settings. Our adversaries do not win by playing Go well. Instead, they trick KataGo into making serious blunders. Our attack transfers zero-shot to other superhuman Go-playing AIs, and is comprehensible to the extent that human experts can implement it without algorithmic assistance to consistently beat superhuman AIs. The core vulnerability uncovered by our attack persists even in KataGo agents adversarially trained to defend against our attack. Our results demonstrate that even superhuman AI systems may harbor surprising failure modes. Example games are available https://goattack.far.ai/.
    A Deep Learning Method for Comparing Bayesian Hierarchical Models. (arXiv:2301.11873v3 [stat.ML] UPDATED)
    Bayesian model comparison (BMC) offers a principled approach for assessing the relative merits of competing computational models and propagating uncertainty into model selection decisions. However, BMC is often intractable for the popular class of hierarchical models due to their high-dimensional nested parameter structure. To address this intractability, we propose a deep learning method for performing BMC on any set of hierarchical models which can be instantiated as probabilistic programs. Since our method enables amortized inference, it allows efficient re-estimation of posterior model probabilities and fast performance validation prior to any real-data application. In a series of extensive validation studies, we benchmark the performance of our method against the state-of-the-art bridge sampling method and demonstrate excellent amortized inference across all BMC settings. We then showcase our method by comparing four hierarchical evidence accumulation models that have previously been deemed intractable for BMC due to partly implicit likelihoods. In this application, we corroborate evidence for the recently proposed L\'evy flight model of decision-making and show how transfer learning can be leveraged to enhance training efficiency. We provide reproducible code for all analyses and an open-source implementation of our method.
    A kernel Stein test of goodness of fit for sequential models. (arXiv:2210.10741v3 [stat.ML] UPDATED)
    We propose a goodness-of-fit measure for probability densities modeling observations with varying dimensionality, such as text documents of differing lengths or variable-length sequences. The proposed measure is an instance of the kernel Stein discrepancy (KSD), which has been used to construct goodness-of-fit tests for unnormalized densities. The KSD is defined by its Stein operator: current operators used in testing apply to fixed-dimensional spaces. As our main contribution, we extend the KSD to the variable-dimension setting by identifying appropriate Stein operators, and propose a novel KSD goodness-of-fit test. As with the previous variants, the proposed KSD does not require the density to be normalized, allowing the evaluation of a large class of models. Our test is shown to perform well in practice on discrete sequential data benchmarks.
    Beyond the Snapshot: Brain Tokenized Graph Transformer for Longitudinal Brain Functional Connectome Embedding. (arXiv:2307.00858v2 [q-bio.NC] UPDATED)
    Under the framework of network-based neurodegeneration, brain functional connectome (FC)-based Graph Neural Networks (GNN) have emerged as a valuable tool for the diagnosis and prognosis of neurodegenerative diseases such as Alzheimer's disease (AD). However, these models are tailored for brain FC at a single time point instead of characterizing FC trajectory. Discerning how FC evolves with disease progression, particularly at the predementia stages such as cognitively normal individuals with amyloid deposition or individuals with mild cognitive impairment (MCI), is crucial for delineating disease spreading patterns and developing effective strategies to slow down or even halt disease advancement. In this work, we proposed the first interpretable framework for brain FC trajectory embedding with application to neurodegenerative disease diagnosis and prognosis, namely Brain Tokenized Graph Transformer (Brain TokenGT). It consists of two modules: 1) Graph Invariant and Variant Embedding (GIVE) for generation of node and spatio-temporal edge embeddings, which were tokenized for downstream processing; 2) Brain Informed Graph Transformer Readout (BIGTR) which augments previous tokens with trainable type identifiers and non-trainable node identifiers and feeds them into a standard transformer encoder to readout. We conducted extensive experiments on two public longitudinal fMRI datasets of the AD continuum for three tasks, including differentiating MCI from controls, predicting dementia conversion in MCI, and classification of amyloid positive or negative cognitively normal individuals. Based on brain FC trajectory, the proposed Brain TokenGT approach outperformed all the other benchmark models and at the same time provided excellent interpretability. The code is available at https://github.com/ZijianD/Brain-TokenGT.git
    Improving Small Language Models on PubMedQA via Generative Data Augmentation. (arXiv:2305.07804v3 [cs.CL] UPDATED)
    Large Language Models (LLMs) have made remarkable advancements in the field of natural language processing. However, their increasing size poses challenges in terms of computational cost. On the other hand, Small Language Models (SLMs) are known for their efficiency, but they often struggle with limited capacity and training data, especially in specific domains. In this paper, we introduce a novel method aimed at improving SLMs in the medical domain using LLM-based generative data augmentation. The objective of our approach is to develop more efficient and capable models that are specifically tailored for specialized applications. Through experiments conducted on the PubMedQA dataset, we demonstrate the effectiveness of LLMs in refining and diversifying existing question-answer pairs. This refinement process leads to improved performance in a significantly smaller model after fine-tuning. Notably, our best SLM, with under 1.6 billion parameters, outperforms the few-shot GPT-4 on the PubMedQA dataset. Our code and generated data are publicly available to facilitate further explorations.
    In-context Autoencoder for Context Compression in a Large Language Model. (arXiv:2307.06945v1 [cs.CL])
    We propose the In-context Autoencoder (ICAE) for context compression in a large language model (LLM). The ICAE has two modules: a learnable encoder adapted with LoRA from an LLM for compressing a long context into a limited number of memory slots, and a fixed decoder which is the target LLM that can condition on the memory slots for various purposes. We first pretrain the ICAE using both autoencoding and language modeling objectives on massive text data, enabling it to generate memory slots that accurately and comprehensively represent the original context. Then, we fine-tune the pretrained ICAE on a small amount of instruct data to enhance its interaction with various prompts for producing desirable responses. Our experimental results demonstrate that the ICAE learned with our proposed pretraining and fine-tuning paradigm can effectively produce memory slots with $4\times$ context compression, which can be well conditioned on by the target LLM to respond to various prompts. The promising results demonstrate significant implications of the ICAE for its novel approach to the long context problem and its potential to reduce computation and memory overheads for LLM inference in practice, suggesting further research effort in context management for an LLM. Our code and data will be released shortly.
    SAN: Inducing Metrizability of GAN with Discriminative Normalized Linear Layer. (arXiv:2301.12811v2 [cs.LG] UPDATED)
    Generative adversarial networks (GANs) learn a target probability distribution by optimizing a generator and a discriminator with minimax objectives. This paper addresses the question of whether such optimization actually provides the generator with gradients that make its distribution close to the target distribution. We derive metrizable conditions, sufficient conditions for the discriminator to serve as the distance between the distributions by connecting the GAN formulation with the concept of sliced optimal transport. Furthermore, by leveraging these theoretical results, we propose a novel GAN training scheme, called slicing adversarial network (SAN). With only simple modifications, a broad class of existing GANs can be converted to SANs. Experiments on synthetic and image datasets support our theoretical results and the SAN's effectiveness as compared to usual GANs. Furthermore, we also apply SAN to StyleGAN-XL, which leads to state-of-the-art FID score amongst GANs for class conditional generation on ImageNet 256$\times$256.
    Local Intrinsic Dimensionality Measures for Graphs, with Applications to Graph Embeddings. (arXiv:2208.11986v2 [cs.LG] UPDATED)
    The notion of local intrinsic dimensionality (LID) is an important advancement in data dimensionality analysis, with applications in data mining, machine learning and similarity search problems. Existing distance-based LID estimators were designed for tabular datasets encompassing data points represented as vectors in a Euclidean space. After discussing their limitations for graph-structured data considering graph embeddings and graph distances, we propose NC-LID, a novel LID-related measure for quantifying the discriminatory power of the shortest-path distance with respect to natural communities of nodes as their intrinsic localities. It is shown how this measure can be used to design LID-aware graph embedding algorithms by formulating two LID-elastic variants of node2vec with personalized hyperparameters that are adjusted according to NC-LID values. Our empirical analysis of NC-LID on a large number of real-world graphs shows that this measure is able to point to nodes with high link reconstruction errors in node2vec embeddings better than node centrality metrics. The experimental evaluation also shows that the proposed LID-elastic node2vec extensions improve node2vec by better preserving graph structure in generated embeddings.
    Climate-Invariant Machine Learning. (arXiv:2112.08440v2 [cs.LG] UPDATED)
    Projecting climate change is a generalization problem: we extrapolate the recent past using physical models across past, present, and future climates. Current climate models require representations of processes that occur at scales smaller than model grid size, which have been the main source of model projection uncertainty. Recent machine learning (ML) algorithms hold promise to improve such process representations, but tend to extrapolate poorly to climate regimes they were not trained on. To get the best of the physical and statistical worlds, we propose a new framework -- termed "climate-invariant" ML -- incorporating knowledge of climate processes into ML algorithms, and show that it can maintain high accuracy across a wide range of climate and geographic conditions in three distinct atmospheric models. Our results suggest that explicitly incorporating physical knowledge into data-driven models of Earth system processes can improve their consistency, data efficiency, and generalizability across climate regimes.
    Tensor Completion via Leverage Sampling and Tensor QR Decomposition for Network Latency Estimation. (arXiv:2307.06848v1 [cs.NI])
    In this paper, we consider the network latency estimation, which has been an important metric for network performance. However, a large scale of network latency estimation requires a lot of computing time. Therefore, we propose a new method that is much faster and maintains high accuracy. The data structure of network nodes can form a matrix, and the tensor model can be formed by introducing the time dimension. Thus, the entire problem can be be summarized as a tensor completion problem. The main idea of our method is improving the tensor leverage sampling strategy and introduce tensor QR decomposition into tensor completion. To achieve faster tensor leverage sampling, we replace tensor singular decomposition (t-SVD) with tensor CSVD-QR to appoximate t-SVD. To achieve faster completion for incomplete tensor, we use the tensor $L_{2,1}$-norm rather than traditional tensor nuclear norm. Furthermore, we introduce tensor QR decomposition into alternating direction method of multipliers (ADMM) framework. Numerical experiments witness that our method is faster than state-of-art algorithms with satisfactory accuracy.
    Joint User and Data Detection in Grant-Free NOMA with Attention-based BiLSTM Network. (arXiv:2209.06392v2 [eess.SP] UPDATED)
    We consider the multi-user detection (MUD) problem in uplink grant-free non-orthogonal multiple access (NOMA), where the access point has to identify the total number and correct identity of the active Internet of Things (IoT) devices and decode their transmitted data. We assume that IoT devices use complex spreading sequences and transmit information in a random-access manner following the burst-sparsity model, where some IoT devices transmit their data in multiple adjacent time slots with a high probability, while others transmit only once during a frame. Exploiting the temporal correlation, we propose an attention-based bidirectional long short-term memory (BiLSTM) network to solve the MUD problem. The BiLSTM network creates a pattern of the device activation history using forward and reverse pass LSTMs, whereas the attention mechanism provides essential context to the device activation points. By doing so, a hierarchical pathway is followed for detecting active devices in a grant-free scenario. Then, by utilising the complex spreading sequences, blind data detection for the estimated active devices is performed. The proposed framework does not require prior knowledge of device sparsity levels and channels for performing MUD. The results show that the proposed network achieves better performance compared to existing benchmark schemes.
    Energy-efficient Deployment of Deep Learning Applications on Cortex-M based Microcontrollers using Deep Compression. (arXiv:2205.10369v2 [cs.LG] UPDATED)
    Large Deep Neural Networks (DNNs) are the backbone of today's artificial intelligence due to their ability to make accurate predictions when being trained on huge datasets. With advancing technologies, such as the Internet of Things, interpreting large quantities of data generated by sensors is becoming an increasingly important task. However, in many applications not only the predictive performance but also the energy consumption of deep learning models is of major interest. This paper investigates the efficient deployment of deep learning models on resource-constrained microcontroller architectures via network compression. We present a methodology for the systematic exploration of different DNN pruning, quantization, and deployment strategies, targeting different ARM Cortex-M based low-power systems. The exploration allows to analyze trade-offs between key metrics such as accuracy, memory consumption, execution time, and power consumption. We discuss experimental results on three different DNN architectures and show that we can compress them to below 10\% of their original parameter count before their predictive quality decreases. This also allows us to deploy and evaluate them on Cortex-M based microcontrollers.
    Generalized Laplacian Regularized Framelet Graph Neural Networks. (arXiv:2210.15092v2 [cs.LG] UPDATED)
    This paper introduces a novel Framelet Graph approach based on p-Laplacian GNN. The proposed two models, named p-Laplacian undecimated framelet graph convolution (pL-UFG) and generalized p-Laplacian undecimated framelet graph convolution (pL-fUFG) inherit the nature of p-Laplacian with the expressive power of multi-resolution decomposition of graph signals. The empirical study highlights the excellent performance of the pL-UFG and pL-fUFG in different graph learning tasks including node classification and signal denoising.
    Declarative Mechanism Design. (arXiv:1912.13122v4 [cs.AI] UPDATED)
    Regulation of Multi-Agent Systems (MAS) and Declarative Electronic Institutions (DEIs) was a multidisciplinary research topic of the past decade involving (Physical and Software) Agents and Law since the beginning, but recently evolved towards News-claimed Robot Lawyer since 2016. One of these first proposals of restricting the behaviour of Software Agentswas Electronic Institutions.However, with the recent reformulation of Artificial Neural Networks (ANNs) as Deep Learning (DL), Security, Privacy,Ethical and Legal issues regarding the use of DL has raised concerns in the Artificial Intelligence (AI) Community. Now that the Regulation of MAS is almost correctly addressed, we propose the Regulation of Artificial Neural Networks as Agent-based Training of a special type of regulated Artificial Neural Network that we call Institutional Neural Network (INN).The main purpose of this paper is to bring attention to Artificial Teaching (AT) and to give a tentative answer showing a proof-of-concept implementation of Regulated Deep Learning (RDL). This paper introduces the former concept and provide sI, a language previously used to model declaratively and extend Electronic Institutions, as a means to regulate the execution of Artificial Neural Networks and their interactions with Artificial Teachers (ATs)
    Efficient Bayesian Policy Reuse with a Scalable Observation Model in Deep Reinforcement Learning. (arXiv:2204.07729v3 [cs.LG] UPDATED)
    Bayesian policy reuse (BPR) is a general policy transfer framework for selecting a source policy from an offline library by inferring the task belief based on some observation signals and a trained observation model. In this paper, we propose an improved BPR method to achieve more efficient policy transfer in deep reinforcement learning (DRL). First, most BPR algorithms use the episodic return as the observation signal that contains limited information and cannot be obtained until the end of an episode. Instead, we employ the state transition sample, which is informative and instantaneous, as the observation signal for faster and more accurate task inference. Second, BPR algorithms usually require numerous samples to estimate the probability distribution of the tabular-based observation model, which may be expensive and even infeasible to learn and maintain, especially when using the state transition sample as the signal. Hence, we propose a scalable observation model based on fitting state transition functions of source tasks from only a small number of samples, which can generalize to any signals observed in the target task. Moreover, we extend the offline-mode BPR to the continual learning setting by expanding the scalable observation model in a plug-and-play fashion, which can avoid negative transfer when faced with new unknown tasks. Experimental results show that our method can consistently facilitate faster and more efficient policy transfer.
    Learning low-rank latent mesoscale structures in networks. (arXiv:2102.06984v5 [cs.SI] UPDATED)
    It is common to use networks to encode the architecture of interactions between entities in complex systems in the physical, biological, social, and information sciences. To study the large-scale behavior of complex systems, it is useful to examine mesoscale structures in networks as building blocks that influence such behavior. We present a new approach for describing low-rank mesoscale structures in networks, and we illustrate our approach using several synthetic network models and empirical friendship, collaboration, and protein--protein interaction (PPI) networks. We find that these networks possess a relatively small number of `latent motifs' that together can successfully approximate most subgraphs of a network at a fixed mesoscale. We use an algorithm for `network dictionary learning' (NDL), which combines a network-sampling method and nonnegative matrix factorization, to learn the latent motifs of a given network. The ability to encode a network using a set of latent motifs has a wide variety of applications to network-analysis tasks, such as comparison, denoising, and edge inference. Additionally, using a new network denoising and reconstruction (NDR) algorithm, we demonstrate how to denoise a corrupted network by using only the latent motifs that one learns directly from the corrupted network.
    Accelerated stochastic approximation with state-dependent noise. (arXiv:2307.01497v2 [math.OC] UPDATED)
    We consider a class of stochastic smooth convex optimization problems under rather general assumptions on the noise in the stochastic gradient observation. As opposed to the classical problem setting in which the variance of noise is assumed to be uniformly bounded, herein we assume that the variance of stochastic gradients is related to the "sub-optimality" of the approximate solutions delivered by the algorithm. Such problems naturally arise in a variety of applications, in particular, in the well-known generalized linear regression problem in statistics. However, to the best of our knowledge, none of the existing stochastic approximation algorithms for solving this class of problems attain optimality in terms of the dependence on accuracy, problem parameters, and mini-batch size. We discuss two non-Euclidean accelerated stochastic approximation routines--stochastic accelerated gradient descent (SAGD) and stochastic gradient extrapolation (SGE)--which carry a particular duality relationship. We show that both SAGD and SGE, under appropriate conditions, achieve the optimal convergence rate, attaining the optimal iteration and sample complexities simultaneously. However, corresponding assumptions for the SGE algorithm are more general; they allow, for instance, for efficient application of the SGE to statistical estimation problems under heavy tail noises and discontinuous score functions. We also discuss the application of the SGE to problems satisfying quadratic growth conditions, and show how it can be used to recover sparse solutions. Finally, we report on some simulation experiments to illustrate numerical performance of our proposed algorithms in high-dimensional settings.
    Multiple Testing Framework for Out-of-Distribution Detection. (arXiv:2206.09522v4 [stat.ML] UPDATED)
    We study the problem of Out-of-Distribution (OOD) detection, that is, detecting whether a learning algorithm's output can be trusted at inference time. While a number of tests for OOD detection have been proposed in prior work, a formal framework for studying this problem is lacking. We propose a definition for the notion of OOD that includes both the input distribution and the learning algorithm, which provides insights for the construction of powerful tests for OOD detection. We propose a multiple hypothesis testing inspired procedure to systematically combine any number of different statistics from the learning algorithm using conformal p-values. We further provide strong guarantees on the probability of incorrectly classifying an in-distribution sample as OOD. In our experiments, we find that threshold-based tests proposed in prior work perform well in specific settings, but not uniformly well across different types of OOD instances. In contrast, our proposed method that combines multiple statistics performs uniformly well across different datasets and neural networks.
    The Effectiveness of World Models for Continual Reinforcement Learning. (arXiv:2211.15944v2 [cs.LG] UPDATED)
    World models power some of the most efficient reinforcement learning algorithms. In this work, we showcase that they can be harnessed for continual learning - a situation when the agent faces changing environments. World models typically employ a replay buffer for training, which can be naturally extended to continual learning. We systematically study how different selective experience replay methods affect performance, forgetting, and transfer. We also provide recommendations regarding various modeling options for using world models. The best set of choices is called Continual-Dreamer, it is task-agnostic and utilizes the world model for continual exploration. Continual-Dreamer is sample efficient and outperforms state-of-the-art task-agnostic continual reinforcement learning methods on Minigrid and Minihack benchmarks.
    Classification and Generation of real-world data with an Associative Memory Model. (arXiv:2207.04827v4 [cs.NE] UPDATED)
    Drawing from memory the face of a friend you have not seen in years is a difficult task. However, if you happen to cross paths, you would easily recognize each other. The biological memory is equipped with an impressive compression algorithm that can store the essential, and then infer the details to match perception. The Willshaw Memory is a simple abstract model for cortical computations which implements mechanisms of biological memories. Using our recently proposed sparse coding prescription for visual patterns, this model can store and retrieve an impressive amount of real-world data in a fault-tolerant manner. In this paper, we extend the capabilities of the basic Associative Memory Model by using a Multiple-Modality framework. In this setting, the memory stores several modalities (e.g., visual, or textual) of each pattern simultaneously. After training, the memory can be used to infer missing modalities when just a subset is perceived. Using a simple encoder-memory-decoder architecture, and a newly proposed iterative retrieval algorithm for the Willshaw Model, we perform experiments on the MNIST dataset. By storing both the images and labels as modalities, a single Memory can be used not only to retrieve and complete patterns but also to classify and generate new ones. We further discuss how this model could be used for other learning tasks, thus serving as a biologically-inspired framework for learning.
    Adapting to Mixing Time in Stochastic Optimization with Markovian Data. (arXiv:2202.04428v3 [cs.LG] UPDATED)
    We consider stochastic optimization problems where data is drawn from a Markov chain. Existing methods for this setting crucially rely on knowing the mixing time of the chain, which in real-world applications is usually unknown. We propose the first optimization method that does not require the knowledge of the mixing time, yet obtains the optimal asymptotic convergence rate when applied to convex problems. We further show that our approach can be extended to: (i) finding stationary points in non-convex optimization with Markovian data, and (ii) obtaining better dependence on the mixing time in temporal difference (TD) learning; in both cases, our method is completely oblivious to the mixing time. Our method relies on a novel combination of multi-level Monte Carlo (MLMC) gradient estimation together with an adaptive learning method.
    The complexity of non-stationary reinforcement learning. (arXiv:2307.06877v1 [cs.LG])
    The problem of continual learning in the domain of reinforcement learning, often called non-stationary reinforcement learning, has been identified as an important challenge to the application of reinforcement learning. We prove a worst-case complexity result, which we believe captures this challenge: Modifying the probabilities or the reward of a single state-action pair in a reinforcement learning problem requires an amount of time almost as large as the number of states in order to keep the value function up to date, unless the strong exponential time hypothesis (SETH) is false; SETH is a widely accepted strengthening of the P $\neq$ NP conjecture. Recall that the number of states in current applications of reinforcement learning is typically astronomical. In contrast, we show that just $\textit{adding}$ a new state-action pair is considerably easier to implement.
    Uncovering Unique Concept Vectors through Latent Space Decomposition. (arXiv:2307.06913v1 [cs.LG])
    Interpreting the inner workings of deep learning models is crucial for establishing trust and ensuring model safety. Concept-based explanations have emerged as a superior approach that is more interpretable than feature attribution estimates such as pixel saliency. However, defining the concepts for the interpretability analysis biases the explanations by the user's expectations on the concepts. To address this, we propose a novel post-hoc unsupervised method that automatically uncovers the concepts learned by deep models during training. By decomposing the latent space of a layer in singular vectors and refining them by unsupervised clustering, we uncover concept vectors aligned with directions of high variance that are relevant to the model prediction, and that point to semantically distinct concepts. Our extensive experiments reveal that the majority of our concepts are readily understandable to humans, exhibit coherency, and bear relevance to the task at hand. Moreover, we showcase the practical utility of our method in dataset exploration, where our concept vectors successfully identify outlier training samples affected by various confounding factors. This novel exploration technique has remarkable versatility to data types and model architectures and it will facilitate the identification of biases and the discovery of sources of error within training data.
    Identifying Early Help Referrals For Local Authorities With Machine Learning And Bias Analysis. (arXiv:2307.06871v1 [cs.LG])
    Local authorities in England, such as Leicestershire County Council (LCC), provide Early Help services that can be offered at any point in a young person's life when they experience difficulties that cannot be supported by universal services alone, such as schools. This paper investigates the utilisation of machine learning (ML) to assist experts in identifying families that may need to be referred for Early Help assessment and support. LCC provided an anonymised dataset comprising 14360 records of young people under the age of 18. The dataset was pre-processed, machine learning models were build, and experiments were conducted to validate and test the performance of the models. Bias mitigation techniques were applied to improve the fairness of these models. During testing, while the models demonstrated the capability to identify young people requiring intervention or early help, they also produced a significant number of false positives, especially when constructed with imbalanced data, incorrectly identifying individuals who most likely did not need an Early Help referral. This paper empirically explores the suitability of data-driven ML models for identifying young people who may require Early Help services and discusses their appropriateness and limitations for this task.
    Weighted Averaged Stochastic Gradient Descent: Asymptotic Normality and Optimality. (arXiv:2307.06915v1 [stat.ML])
    Stochastic Gradient Descent (SGD) is one of the simplest and most popular algorithms in modern statistical and machine learning due to its computational and memory efficiency. Various averaging schemes have been proposed to accelerate the convergence of SGD in different settings. In this paper, we explore a general averaging scheme for SGD. Specifically, we establish the asymptotic normality of a broad range of weighted averaged SGD solutions and provide asymptotically valid online inference approaches. Furthermore, we propose an adaptive averaging scheme that exhibits both optimal statistical rate and favorable non-asymptotic convergence, drawing insights from the optimal weight for the linear model in terms of non-asymptotic mean squared error (MSE).
    Ensemble learning for blending gridded satellite and gauge-measured precipitation data. (arXiv:2307.06840v1 [cs.LG])
    Regression algorithms are regularly used for improving the accuracy of satellite precipitation products. In this context, ground-based measurements are the dependent variable and the satellite data are the predictor variables, together with topography factors. Alongside this, it is increasingly recognised in many fields that combinations of algorithms through ensemble learning can lead to substantial predictive performance improvements. Still, a sufficient number of ensemble learners for improving the accuracy of satellite precipitation products and their large-scale comparison are currently missing from the literature. In this work, we fill this specific gap by proposing 11 new ensemble learners in the field and by extensively comparing them for the entire contiguous United States and for a 15-year period. We use monthly data from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) and IMERG (Integrated Multi-satellitE Retrievals for GPM) gridded datasets. We also use gauge-measured precipitation data from the Global Historical Climatology Network monthly database, version 2 (GHCNm). The ensemble learners combine the predictions by six regression algorithms (base learners), namely the multivariate adaptive regression splines (MARS), multivariate adaptive polynomial splines (poly-MARS), random forests (RF), gradient boosting machines (GBM), extreme gradient boosting (XGBoost) and Bayesian regularized neural networks (BRNN), and each of them is based on a different combiner. The combiners include the equal-weight combiner, the median combiner, two best learners and seven variants of a sophisticated stacking method. The latter stacks a regression algorithm on the top of the base learners to combine their independent predictions...
    AnuraSet: A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoring. (arXiv:2307.06860v1 [cs.SD])
    Global change is predicted to induce shifts in anuran acoustic behavior, which can be studied through passive acoustic monitoring (PAM). Understanding changes in calling behavior requires the identification of anuran species, which is challenging due to the particular characteristics of neotropical soundscapes. In this paper, we introduce a large-scale multi-species dataset of anuran amphibians calls recorded by PAM, that comprises 27 hours of expert annotations for 42 different species from two Brazilian biomes. We provide open access to the dataset, including the raw recordings, experimental setup code, and a benchmark with a baseline model of the fine-grained categorization problem. Additionally, we highlight the challenges of the dataset to encourage machine learning researchers to solve the problem of anuran call identification towards conservation policy. All our experiments and resources can be found on our GitHub repository https://github.com/soundclim/anuraset.
    Towards Learning to Imitate from a Single Video Demonstration. (arXiv:1901.07186v4 [cs.LG] UPDATED)
    Agents that can learn to imitate given video observation -- \emph{without direct access to state or action information} are more applicable to learning in the natural world. However, formulating a reinforcement learning (RL) agent that facilitates this goal remains a significant challenge. We approach this challenge using contrastive training to learn a reward function comparing an agent's behaviour with a single demonstration. We use a Siamese recurrent neural network architecture to learn rewards in space and time between motion clips while training an RL policy to minimize this distance. Through experimentation, we also find that the inclusion of multi-task data and additional image encoding losses improve the temporal consistency of the learned rewards and, as a result, significantly improves policy learning. We demonstrate our approach on simulated humanoid, dog, and raptor agents in 2D and a quadruped and a humanoid in 3D. We show that our method outperforms current state-of-the-art techniques in these environments and can learn to imitate from a single video demonstration.
    Emergent Neural Network Mechanisms for Generalization to Objects in Novel Orientations. (arXiv:2109.13445v2 [cs.CV] UPDATED)
    The capability of Deep Neural Networks (DNNs) to recognize objects in orientations outside the distribution of the training data is not well understood. We present evidence that DNNs are capable of generalizing to objects in novel orientations by disseminating orientation-invariance obtained from familiar objects seen from many viewpoints. This capability strengthens when training the DNN with an increasing number of familiar objects, but only in orientations that involve 2D rotations of familiar orientations. We show that this dissemination is achieved via neurons tuned to common features between familiar and unfamiliar objects. These results implicate brain-like neural mechanisms for generalization.
    Fast and Functional Structured Data Generators Rooted in Out-of-Equilibrium Physics. (arXiv:2307.06797v1 [cs.LG])
    In this study, we address the challenge of using energy-based models to produce high-quality, label-specific data in complex structured datasets, such as population genetics, RNA or protein sequences data. Traditional training methods encounter difficulties due to inefficient Markov chain Monte Carlo mixing, which affects the diversity of synthetic data and increases generation times. To address these issues, we use a novel training algorithm that exploits non-equilibrium effects. This approach, applied on the Restricted Boltzmann Machine, improves the model's ability to correctly classify samples and generate high-quality synthetic data in only a few sampling steps. The effectiveness of this method is demonstrated by its successful application to four different types of data: handwritten digits, mutations of human genomes classified by continental origin, functionally characterized sequences of an enzyme protein family, and homologous RNA sequences from specific taxonomies.
    TinyMetaFed: Efficient Federated Meta-Learning for TinyML. (arXiv:2307.06822v1 [cs.LG])
    The field of Tiny Machine Learning (TinyML) has made substantial advancements in democratizing machine learning on low-footprint devices, such as microcontrollers. The prevalence of these miniature devices raises the question of whether aggregating their knowledge can benefit TinyML applications. Federated meta-learning is a promising answer to this question, as it addresses the scarcity of labeled data and heterogeneous data distribution across devices in the real world. However, deploying TinyML hardware faces unique resource constraints, making existing methods impractical due to energy, privacy, and communication limitations. We introduce TinyMetaFed, a model-agnostic meta-learning framework suitable for TinyML. TinyMetaFed facilitates collaborative training of a neural network initialization that can be quickly fine-tuned on new devices. It offers communication savings and privacy protection through partial local reconstruction and Top-P% selective communication, computational efficiency via online learning, and robustness to client heterogeneity through few-shot learning. The evaluations on three TinyML use cases demonstrate that TinyMetaFed can significantly reduce energy consumption and communication overhead, accelerate convergence, and stabilize the training process.
    Enhancing Reliability in Federated mmWave Networks: A Practical and Scalable Solution using Radar-Aided Dynamic Blockage Recognition. (arXiv:2307.06834v1 [cs.NI])
    This article introduces a new method to improve the dependability of millimeter-wave (mmWave) and terahertz (THz) network services in dynamic outdoor environments. In these settings, line-of-sight (LoS) connections are easily interrupted by moving obstacles like humans and vehicles. The proposed approach, coined as Radar-aided Dynamic blockage Recognition (RaDaR), leverages radar measurements and federated learning (FL) to train a dual-output neural network (NN) model capable of simultaneously predicting blockage status and time. This enables determining the optimal point for proactive handover (PHO) or beam switching, thereby reducing the latency introduced by 5G new radio procedures and ensuring high quality of experience (QoE). The framework employs radar sensors to monitor and track objects movement, generating range-angle and range-velocity maps that are useful for scene analysis and predictions. Moreover, FL provides additional benefits such as privacy protection, scalability, and knowledge sharing. The framework is assessed using an extensive real-world dataset comprising mmWave channel information and radar data. The evaluation results show that RaDaR substantially enhances network reliability, achieving an average success rate of 94% for PHO compared to existing reactive HO procedures that lack proactive blockage prediction. Additionally, RaDaR maintains a superior QoE by ensuring sustained high throughput levels and minimising PHO latency.
    Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks. (arXiv:2307.06887v1 [cs.LG])
    Feature learning, i.e. extracting meaningful representations of data, is quintessential to the practical success of neural networks trained with gradient descent, yet it is notoriously difficult to explain how and why it occurs. Recent theoretical studies have shown that shallow neural networks optimized on a single task with gradient-based methods can learn meaningful features, extending our understanding beyond the neural tangent kernel or random feature regime in which negligible feature learning occurs. But in practice, neural networks are increasingly often trained on {\em many} tasks simultaneously with differing loss functions, and these prior analyses do not generalize to such settings. In the multi-task learning setting, a variety of studies have shown effective feature learning by simple linear models. However, multi-task learning via {\em nonlinear} models, arguably the most common learning paradigm in practice, remains largely mysterious. In this work, we present the first results proving feature learning occurs in a multi-task setting with a nonlinear model. We show that when the tasks are binary classification problems with labels depending on only $r$ directions within the ambient $d\gg r$-dimensional input space, executing a simple gradient-based multitask learning algorithm on a two-layer ReLU neural network learns the ground-truth $r$ directions. In particular, any downstream task on the $r$ ground-truth coordinates can be solved by learning a linear classifier with sample and neuron complexity independent of the ambient dimension $d$, while a random feature model requires exponential complexity in $d$ for such a guarantee.
    Domain-Agnostic Tuning-Encoder for Fast Personalization of Text-To-Image Models. (arXiv:2307.06925v1 [cs.CV])
    Text-to-image (T2I) personalization allows users to guide the creative image generation process by combining their own visual concepts in natural language prompts. Recently, encoder-based techniques have emerged as a new effective approach for T2I personalization, reducing the need for multiple images and long training times. However, most existing encoders are limited to a single-class domain, which hinders their ability to handle diverse concepts. In this work, we propose a domain-agnostic method that does not require any specialized dataset or prior information about the personalized concepts. We introduce a novel contrastive-based regularization technique to maintain high fidelity to the target concept characteristics while keeping the predicted embeddings close to editable regions of the latent space, by pushing the predicted tokens toward their nearest existing CLIP tokens. Our experimental results demonstrate the effectiveness of our approach and show how the learned tokens are more semantic than tokens predicted by unregularized models. This leads to a better representation that achieves state-of-the-art performance while being more flexible than previous methods.
    FDAPT: Federated Domain-adaptive Pre-training for Language Models. (arXiv:2307.06933v1 [cs.LG])
    Combining Domain-adaptive Pre-training (DAPT) with Federated Learning (FL) can enhance model adaptation by leveraging more sensitive and distributed data while preserving data privacy. However, few studies have focused on this method. Therefore, we conduct the first comprehensive empirical study to evaluate the performance of Federated Domain-adaptive Pre-training (FDAPT). We demonstrate that FDAPT can maintain competitive downstream task performance to the centralized baseline in both IID and non-IID situations. Furthermore, we propose a novel algorithm, Frozen Federated Domain-adaptive Pre-training (FFDAPT). FFDAPT improves the computational efficiency by 12.1% on average and exhibits similar downstream task performance to standard FDAPT, with general performance fluctuations remaining less than 1%. Finally, through a critical evaluation of our work, we identify promising future research directions for this new research area.
    CLAIMED -- the open source framework for building coarse-grained operators for accelerated discovery in science. (arXiv:2307.06824v1 [cs.AI])
    In modern data-driven science, reproducibility and reusability are key challenges. Scientists are well skilled in the process from data to publication. Although some publication channels require source code and data to be made accessible, rerunning and verifying experiments is usually hard due to a lack of standards. Therefore, reusing existing scientific data processing code from state-of-the-art research is hard as well. This is why we introduce CLAIMED, which has a proven track record in scientific research for addressing the repeatability and reusability issues in modern data-driven science. CLAIMED is a framework to build reusable operators and scalable scientific workflows by supporting the scientist to draw from previous work by re-composing workflows from existing libraries of coarse-grained scientific operators. Although various implementations exist, CLAIMED is programming language, scientific library, and execution environment agnostic.
    Min-Max Optimization under Delays. (arXiv:2307.06886v1 [cs.LG])
    Delays and asynchrony are inevitable in large-scale machine-learning problems where communication plays a key role. As such, several works have extensively analyzed stochastic optimization with delayed gradients. However, as far as we are aware, no analogous theory is available for min-max optimization, a topic that has gained recent popularity due to applications in adversarial robustness, game theory, and reinforcement learning. Motivated by this gap, we examine the performance of standard min-max optimization algorithms with delayed gradient updates. First, we show (empirically) that even small delays can cause prominent algorithms like Extra-gradient (\texttt{EG}) to diverge on simple instances for which \texttt{EG} guarantees convergence in the absence of delays. Our empirical study thus suggests the need for a careful analysis of delayed versions of min-max optimization algorithms. Accordingly, under suitable technical assumptions, we prove that Gradient Descent-Ascent (\texttt{GDA}) and \texttt{EG} with delayed updates continue to guarantee convergence to saddle points for convex-concave and strongly convex-strongly concave settings. Our complexity bounds reveal, in a transparent manner, the slow-down in convergence caused by delays.
    Privacy-Utility Trade-offs in Neural Networks for Medical Population Graphs: Insights from Differential Privacy and Graph Structure. (arXiv:2307.06760v1 [cs.LG])
    We initiate an empirical investigation into differentially private graph neural networks on population graphs from the medical domain by examining privacy-utility trade-offs at different privacy levels on both real-world and synthetic datasets and performing auditing through membership inference attacks. Our findings highlight the potential and the challenges of this specific DP application area. Moreover, we find evidence that the underlying graph structure constitutes a potential factor for larger performance gaps by showing a correlation between the degree of graph homophily and the accuracy of the trained model.
    Data-driven Nonlinear Parametric Model Order Reduction Framework using Deep Hierarchical Variational Autoencoder. (arXiv:2307.06816v1 [cs.LG])
    A data-driven parametric model order reduction (MOR) method using a deep artificial neural network is proposed. The present network, which is the least-squares hierarchical variational autoencoder (LSH-VAE), is capable of performing nonlinear MOR for the parametric interpolation of a nonlinear dynamic system with a significant number of degrees of freedom. LSH-VAE exploits two major changes to the existing networks: a hierarchical deep structure and a hybrid weighted, probabilistic loss function. The enhancements result in a significantly improved accuracy and stability compared against the conventional nonlinear MOR methods, autoencoder, and variational autoencoder. Upon LSH-VAE, a parametric MOR framework is presented based on the spherically linear interpolation of the latent manifold. The present framework is validated and evaluated on three nonlinear and multiphysics dynamic systems. First, the present framework is evaluated on the fluid-structure interaction benchmark problem to assess its efficiency and accuracy. Then, a highly nonlinear aeroelastic phenomenon, limit cycle oscillation, is analyzed. Finally, the present framework is applied to a three-dimensional fluid flow to demonstrate its capability of efficiently analyzing a significantly large number of degrees of freedom. The performance of LSH-VAE is emphasized by comparing its results against that of the widely used nonlinear MOR methods, convolutional autoencoder, and $\beta$-VAE. The present framework exhibits a significantly enhanced accuracy to the conventional methods while still exhibiting a large speed-up factor.
    PC-Droid: Faster diffusion and improved quality for particle cloud generation. (arXiv:2307.06836v1 [hep-ex])
    Building on the success of PC-JeDi we introduce PC-Droid, a substantially improved diffusion model for the generation of jet particle clouds. By leveraging a new diffusion formulation, studying more recent integration solvers, and training on all jet types simultaneously, we are able to achieve state-of-the-art performance for all types of jets across all evaluation metrics. We study the trade-off between generation speed and quality by comparing two attention based architectures, as well as the potential of consistency distillation to reduce the number of diffusion steps. Both the faster architecture and consistency models demonstrate performance surpassing many competing models, with generation time up to two orders of magnitude faster than PC-JeDi.
    Generalizing Supervised Deep Learning MRI Reconstruction to Multiple and Unseen Contrasts using Meta-Learning Hypernetworks. (arXiv:2307.06771v1 [eess.IV])
    Meta-learning has recently been an emerging data-efficient learning technique for various medical imaging operations and has helped advance contemporary deep learning models. Furthermore, meta-learning enhances the knowledge generalization of the imaging tasks by learning both shared and discriminative weights for various configurations of imaging tasks. However, existing meta-learning models attempt to learn a single set of weight initializations of a neural network that might be restrictive for multimodal data. This work aims to develop a multimodal meta-learning model for image reconstruction, which augments meta-learning with evolutionary capabilities to encompass diverse acquisition settings of multimodal data. Our proposed model called KM-MAML (Kernel Modulation-based Multimodal Meta-Learning), has hypernetworks that evolve to generate mode-specific weights. These weights provide the mode-specific inductive bias for multiple modes by re-calibrating each kernel of the base network for image reconstruction via a low-rank kernel modulation operation. We incorporate gradient-based meta-learning (GBML) in the contextual space to update the weights of the hypernetworks for different modes. The hypernetworks and the reconstruction network in the GBML setting provide discriminative mode-specific features and low-level image features, respectively. Experiments on multi-contrast MRI reconstruction show that our model, (i) exhibits superior reconstruction performance over joint training, other meta-learning methods, and context-specific MRI reconstruction methods, and (ii) better adaptation capabilities with improvement margins of 0.5 dB in PSNR and 0.01 in SSIM. Besides, a representation analysis with U-Net shows that kernel modulation infuses 80% of mode-specific representation changes in the high-resolution layers. Our source code is available at https://github.com/sriprabhar/KM-MAML/.
    Robotic surface exploration with vision and tactile sensing for cracks detection and characterisation. (arXiv:2307.06784v1 [cs.RO])
    This paper presents a novel algorithm for crack localisation and detection based on visual and tactile analysis via fibre-optics. A finger-shaped sensor based on fibre-optics is employed for the data acquisition to collect data for the analysis and the experiments. To detect the possible locations of cracks a camera is used to scan an environment while running an object detection algorithm. Once the crack is detected, a fully-connected graph is created from a skeletonised version of the crack. A minimum spanning tree is then employed for calculating the shortest path to explore the crack which is then used to develop the motion planner for the robotic manipulator. The motion planner divides the crack into multiple nodes which are then explored individually. Then, the manipulator starts the exploration and performs the tactile data classification to confirm if there is indeed a crack in that location or just a false positive from the vision algorithm. If a crack is detected, also the length, width, orientation and number of branches are calculated. This is repeated until all the nodes of the crack are explored. In order to validate the complete algorithm, various experiments are performed: comparison of exploration of cracks through full scan and motion planning algorithm, implementation of frequency-based features for crack classification and geometry analysis using a combination of vision and tactile data. From the results of the experiments, it is shown that the proposed algorithm is able to detect cracks and improve the results obtained from vision to correctly classify cracks and their geometry with minimal cost thanks to the motion planning algorithm.
    Federated Multi-Agent Deep Reinforcement Learning for Dynamic and Flexible 3D Operation of 5G Multi-MAP Networks. (arXiv:2307.06842v1 [cs.NI])
    This paper addresses the efficient management of Mobile Access Points (MAPs), which are Unmanned Aerial Vehicles (UAV), in 5G networks. We propose a two-level hierarchical architecture, which dynamically reconfigures the network while considering Integrated Access-Backhaul (IAB) constraints. The high-layer decision process determines the number of MAPs through consensus, and we develop a joint optimization process to account for co-dependence in network self-management. In the low-layer, MAPs manage their placement using a double-attention based Deep Reinforcement Learning (DRL) model that encourages cooperation without retraining. To improve generalization and reduce complexity, we propose a federated mechanism for training and sharing one placement model for every MAP in the low-layer. Additionally, we jointly optimize the placement and backhaul connectivity of MAPs using a multi-objective reward function, considering the impact of varying MAP placement on wireless backhaul connectivity.
    Self-consistency for open-ended generations. (arXiv:2307.06857v1 [cs.AI])
    In this paper, we present a novel approach for improving the quality and consistency of generated outputs from large-scale pre-trained language models (LLMs). Self-consistency has emerged as an effective approach for prompts with fixed answers, selecting the answer with the highest number of votes. In this paper, we introduce a generalized framework for self-consistency that extends its applicability beyond problems that have fixed-answer answers. Through extensive simulations, we demonstrate that our approach consistently recovers the optimal or near-optimal generation from a set of candidates. We also propose lightweight parameter-free similarity functions that show significant and consistent improvements across code generation, autoformalization, and summarization tasks, even without access to token log probabilities. Our method incurs minimal computational overhead, requiring no auxiliary reranker models or modifications to the existing model.
    Improved selective background Monte Carlo simulation at Belle II with graph attention networks and weighted events. (arXiv:2307.06434v1 [hep-ex])
    When measuring rare processes at Belle II, a huge luminosity is required, which means a large number of simulations are necessary to determine signal efficiencies and background contributions. However, this process demands high computation costs while most of the simulated data, in particular in case of background, are discarded by the event selection. Thus, filters using graph neural networks are introduced at an early stage to save the resources for the detector simulation and reconstruction of events discarded at analysis level. In our work, we improved the performance of the filters using graph attention and investigated statistical methods including sampling and reweighting to deal with the biases introduced by the filtering.  ( 2 min )
    Why Guided Dialog Policy Learning performs well? Understanding the role of adversarial learning and its alternative. (arXiv:2307.06721v1 [cs.CL])
    Dialog policies, which determine a system's action based on the current state at each dialog turn, are crucial to the success of the dialog. In recent years, reinforcement learning (RL) has emerged as a promising option for dialog policy learning (DPL). In RL-based DPL, dialog policies are updated according to rewards. The manual construction of fine-grained rewards, such as state-action-based ones, to effectively guide the dialog policy is challenging in multi-domain task-oriented dialog scenarios with numerous state-action pair combinations. One way to estimate rewards from collected data is to train the reward estimator and dialog policy simultaneously using adversarial learning (AL). Although this method has demonstrated superior performance experimentally, it is fraught with the inherent problems of AL, such as mode collapse. This paper first identifies the role of AL in DPL through detailed analyses of the objective functions of dialog policy and reward estimator. Next, based on these analyses, we propose a method that eliminates AL from reward estimation and DPL while retaining its advantages. We evaluate our method using MultiWOZ, a multi-domain task-oriented dialog corpus.
    Breaking 3-Factor Approximation for Correlation Clustering in Polylogarithmic Rounds. (arXiv:2307.06723v1 [cs.DS])
    In this paper, we study parallel algorithms for the correlation clustering problem, where every pair of two different entities is labeled with similar or dissimilar. The goal is to partition the entities into clusters to minimize the number of disagreements with the labels. Currently, all efficient parallel algorithms have an approximation ratio of at least 3. In comparison with the $1.994+\epsilon$ ratio achieved by polynomial-time sequential algorithms [CLN22], a significant gap exists. We propose the first poly-logarithmic depth parallel algorithm that achieves a better approximation ratio than 3. Specifically, our algorithm computes a $(2.4+\epsilon)$-approximate solution and uses $\tilde{O}(m^{1.5})$ work. Additionally, it can be translated into a $\tilde{O}(m^{1.5})$-time sequential algorithm and a poly-logarithmic rounds sublinear-memory MPC algorithm with $\tilde{O}(m^{1.5})$ total memory. Our approach is inspired by Awerbuch, Khandekar, and Rao's [AKR12] length-constrained multi-commodity flow algorithm, where we develop an efficient parallel algorithm to solve a truncated correlation clustering linear program of Charikar, Guruswami, and Wirth [CGW05]. Then we show the solution of the truncated linear program can be rounded with a factor of at most 2.4 loss by using the framework of [CMSY15]. Such a rounding framework can then be implemented using parallel pivot-based approaches.
    Embodied Lifelong Learning for Task and Motion Planning. (arXiv:2307.06870v1 [cs.RO])
    A robot deployed in a home over long stretches of time faces a true lifelong learning problem. As it seeks to provide assistance to its users, the robot should leverage any accumulated experience to improve its own knowledge to become a more proficient assistant. We formalize this setting with a novel lifelong learning problem formulation in the context of learning for task and motion planning (TAMP). Exploiting the modularity of TAMP systems, we develop a generative mixture model that produces candidate continuous parameters for a planner. Whereas most existing lifelong learning approaches determine a priori how data is shared across task models, our approach learns shared and non-shared models and determines which to use online during planning based on auxiliary tasks that serve as a proxy for each model's understanding of a state. Our method exhibits substantial improvements in planning success on simulated 2D domains and on several problems from the BEHAVIOR benchmark.
    A survey on deep learning approaches for data integration in autonomous driving system. (arXiv:2306.11740v2 [cs.RO] UPDATED)
    The perception module of self-driving vehicles relies on a multi-sensor system to understand its environment. Recent advancements in deep learning have led to the rapid development of approaches that integrate multi-sensory measurements to enhance perception capabilities. This paper surveys the latest deep learning integration techniques applied to the perception module in autonomous driving systems, categorizing integration approaches based on "what, how, and when to integrate". A new taxonomy of integration is proposed, based on three dimensions: multi-view, multi-modality, and multi-frame. The integration operations and their pros and cons are summarized, providing new insights into the properties of an "ideal" data integration approach that can alleviate the limitations of existing methods. After reviewing hundreds of relevant papers, this survey concludes with a discussion of the key features of an optimal data integration approach.
    Vehicle Dispatching and Routing of On-Demand Intercity Ride-Pooling Services: A Multi-Agent Hierarchical Reinforcement Learning Approach. (arXiv:2307.06742v1 [eess.SY])
    The integrated development of city clusters has given rise to an increasing demand for intercity travel. Intercity ride-pooling service exhibits considerable potential in upgrading traditional intercity bus services by implementing demand-responsive enhancements. Nevertheless, its online operations suffer the inherent complexities due to the coupling of vehicle resource allocation among cities and pooled-ride vehicle routing. To tackle these challenges, this study proposes a two-level framework designed to facilitate online fleet management. Specifically, a novel multi-agent feudal reinforcement learning model is proposed at the upper level of the framework to cooperatively assign idle vehicles to different intercity lines, while the lower level updates the routes of vehicles using an adaptive large neighborhood search heuristic. Numerical studies based on the realistic dataset of Xiamen and its surrounding cities in China show that the proposed framework effectively mitigates the supply and demand imbalances, and achieves significant improvement in both the average daily system profit and order fulfillment ratio.
    HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Models. (arXiv:2307.06949v1 [cs.CV])
    Personalization has emerged as a prominent aspect within the field of generative AI, enabling the synthesis of individuals in diverse contexts and styles, while retaining high-fidelity to their identities. However, the process of personalization presents inherent challenges in terms of time and memory requirements. Fine-tuning each personalized model needs considerable GPU time investment, and storing a personalized model per subject can be demanding in terms of storage capacity. To overcome these challenges, we propose HyperDreamBooth-a hypernetwork capable of efficiently generating a small set of personalized weights from a single image of a person. By composing these weights into the diffusion model, coupled with fast finetuning, HyperDreamBooth can generate a person's face in various contexts and styles, with high subject details while also preserving the model's crucial knowledge of diverse styles and semantic modifications. Our method achieves personalization on faces in roughly 20 seconds, 25x faster than DreamBooth and 125x faster than Textual Inversion, using as few as one reference image, with the same quality and style diversity as DreamBooth. Also our method yields a model that is 10000x smaller than a normal DreamBooth model. Project page: https://hyperdreambooth.github.io
    S-HR-VQVAE: Sequential Hierarchical Residual Learning Vector Quantized Variational Autoencoder for Video Prediction. (arXiv:2307.06701v1 [cs.CV])
    We address the video prediction task by putting forth a novel model that combines (i) our recently proposed hierarchical residual vector quantized variational autoencoder (HR-VQVAE), and (ii) a novel spatiotemporal PixelCNN (ST-PixelCNN). We refer to this approach as a sequential hierarchical residual learning vector quantized variational autoencoder (S-HR-VQVAE). By leveraging the intrinsic capabilities of HR-VQVAE at modeling still images with a parsimonious representation, combined with the ST-PixelCNN's ability at handling spatiotemporal information, S-HR-VQVAE can better deal with chief challenges in video prediction. These include learning spatiotemporal information, handling high dimensional data, combating blurry prediction, and implicit modeling of physical characteristics. Extensive experimental results on the KTH Human Action and Moving-MNIST tasks demonstrate that our model compares favorably against top video prediction techniques both in quantitative and qualitative evaluations despite a much smaller model size. Finally, we boost S-HR-VQVAE by proposing a novel training method to jointly estimate the HR-VQVAE and ST-PixelCNN parameters.
    Unsupervised Calibration through Prior Adaptation for Text Classification using Large Language Models. (arXiv:2307.06713v1 [cs.CL])
    A wide variety of natural language tasks are currently being addressed with large-scale language models (LLMs). These models are usually trained with a very large amount of unsupervised text data and adapted to perform a downstream natural language task using methods like fine-tuning, calibration or in-context learning. In this work, we propose an approach to adapt the prior class distribution to perform text classification tasks without the need for labelled samples and only few in-domain sample queries. The proposed approach treats the LLM as a black box, adding a stage where the model posteriors are calibrated to the task. Results show that these methods outperform the un-adapted model for different number of training shots in the prompt and a previous approach were calibration is performed without using any adaptation data.
    Cramer Type Distances for Learning Gaussian Mixture Models by Gradient Descent. (arXiv:2307.06753v1 [cs.LG])
    The learning of Gaussian Mixture Models (also referred to simply as GMMs) plays an important role in machine learning. Known for their expressiveness and interpretability, Gaussian mixture models have a wide range of applications, from statistics, computer vision to distributional reinforcement learning. However, as of today, few known algorithms can fit or learn these models, some of which include Expectation-Maximization algorithms and Sliced Wasserstein Distance. Even fewer algorithms are compatible with gradient descent, the common learning process for neural networks. In this paper, we derive a closed formula of two GMMs in the univariate, one-dimensional case, then propose a distance function called Sliced Cram\'er 2-distance for learning general multivariate GMMs. Our approach has several advantages over many previous methods. First, it has a closed-form expression for the univariate case and is easy to compute and implement using common machine learning libraries (e.g., PyTorch and TensorFlow). Second, it is compatible with gradient descent, which enables us to integrate GMMs with neural networks seamlessly. Third, it can fit a GMM not only to a set of data points, but also to another GMM directly, without sampling from the target model. And fourth, it has some theoretical guarantees like global gradient boundedness and unbiased sampling gradient. These features are especially useful for distributional reinforcement learning and Deep Q Networks, where the goal is to learn a distribution over future rewards. We will also construct a Gaussian Mixture Distributional Deep Q Network as a toy example to demonstrate its effectiveness. Compared with previous models, this model is parameter efficient in terms of representing a distribution and possesses better interpretability.
    A Novel Bayes' Theorem for Upper Probabilities. (arXiv:2307.06831v1 [stat.ML])
    In their seminal 1990 paper, Wasserman and Kadane establish an upper bound for the Bayes' posterior probability of a measurable set $A$, when the prior lies in a class of probability measures $\mathcal{P}$ and the likelihood is precise. They also give a sufficient condition for such upper bound to hold with equality. In this paper, we introduce a generalization of their result by additionally addressing uncertainty related to the likelihood. We give an upper bound for the posterior probability when both the prior and the likelihood belong to a set of probabilities. Furthermore, we give a sufficient condition for this upper bound to become an equality. This result is interesting on its own, and has the potential of being applied to various fields of engineering (e.g. model predictive control), machine learning, and artificial intelligence.
    Frameless Graph Knowledge Distillation. (arXiv:2307.06631v1 [cs.LG])
    Knowledge distillation (KD) has shown great potential for transferring knowledge from a complex teacher model to a simple student model in which the heavy learning task can be accomplished efficiently and without losing too much prediction accuracy. Recently, many attempts have been made by applying the KD mechanism to the graph representation learning models such as graph neural networks (GNNs) to accelerate the model's inference speed via student models. However, many existing KD-based GNNs utilize MLP as a universal approximator in the student model to imitate the teacher model's process without considering the graph knowledge from the teacher model. In this work, we provide a KD-based framework on multi-scaled GNNs, known as graph framelet, and prove that by adequately utilizing the graph knowledge in a multi-scaled manner provided by graph framelet decomposition, the student model is capable of adapting both homophilic and heterophilic graphs and has the potential of alleviating the over-squashing issue with a simple yet effectively graph surgery. Furthermore, we show how the graph knowledge supplied by the teacher is learned and digested by the student model via both algebra and geometry. Comprehensive experiments show that our proposed model can generate learning accuracy identical to or even surpass the teacher model while maintaining the high speed of inference.
    Testing Sparsity Assumptions in Bayesian Networks. (arXiv:2307.06406v1 [stat.ML])
    Bayesian network (BN) structure discovery algorithms typically either make assumptions about the sparsity of the true underlying network, or are limited by computational constraints to networks with a small number of variables. While these sparsity assumptions can take various forms, frequently the assumptions focus on an upper bound for the maximum in-degree of the underlying graph $\nabla_G$. Theorem 2 in Duttweiler et. al. (2023) demonstrates that the largest eigenvalue of the normalized inverse covariance matrix ($\Omega$) of a linear BN is a lower bound for $\nabla_G$. Building on this result, this paper provides the asymptotic properties of, and a debiasing procedure for, the sample eigenvalues of $\Omega$, leading to a hypothesis test that may be used to determine if the BN has max in-degree greater than 1. A linear BN structure discovery workflow is suggested in which the investigator uses this hypothesis test to aid in selecting an appropriate structure discovery algorithm. The hypothesis test performance is evaluated through simulations and the workflow is demonstrated on data from a human psoriasis study.
    Machine Learning practices and infrastructures. (arXiv:2307.06518v1 [cs.CY])
    Machine Learning (ML) systems, particularly when deployed in high-stakes domains, are deeply consequential. They can exacerbate existing inequities, create new modes of discrimination, and reify outdated social constructs. Accordingly, the social context (i.e. organisations, teams, cultures) in which ML systems are developed is a site of active research for the field of AI ethics, and intervention for policymakers. This paper focuses on one aspect of social context that is often overlooked: interactions between practitioners and the tools they rely on, and the role these interactions play in shaping ML practices and the development of ML systems. In particular, through an empirical study of questions asked on the Stack Exchange forums, the use of interactive computing platforms (e.g. Jupyter Notebook and Google Colab) in ML practices is explored. I find that interactive computing platforms are used in a host of learning and coordination practices, which constitutes an infrastructural relationship between interactive computing platforms and ML practitioners. I describe how ML practices are co-evolving alongside the development of interactive computing platforms, and highlight how this risks making invisible aspects of the ML life cycle that AI ethics researchers' have demonstrated to be particularly salient for the societal impact of deployed ML systems.
    MPR-Net:Multi-Scale Pattern Reproduction Guided Universality Time Series Interpretable Forecasting. (arXiv:2307.06736v1 [cs.LG])
    Time series forecasting has received wide interest from existing research due to its broad applications and inherent challenging. The research challenge lies in identifying effective patterns in historical series and applying them to future forecasting. Advanced models based on point-wise connected MLP and Transformer architectures have strong fitting power, but their secondary computational complexity limits practicality. Additionally, those structures inherently disrupt the temporal order, reducing the information utilization and making the forecasting process uninterpretable. To solve these problems, this paper proposes a forecasting model, MPR-Net. It first adaptively decomposes multi-scale historical series patterns using convolution operation, then constructs a pattern extension forecasting method based on the prior knowledge of pattern reproduction, and finally reconstructs future patterns into future series using deconvolution operation. By leveraging the temporal dependencies present in the time series, MPR-Net not only achieves linear time complexity, but also makes the forecasting process interpretable. By carrying out sufficient experiments on more than ten real data sets of both short and long term forecasting tasks, MPR-Net achieves the state of the art forecasting performance, as well as good generalization and robustness performance.
    A Novel Site-Agnostic Multimodal Deep Learning Model to Identify Pro-Eating Disorder Content on Social Media. (arXiv:2307.06775v1 [cs.LG])
    Over the last decade, there has been a vast increase in eating disorder diagnoses and eating disorder-attributed deaths, reaching their zenith during the Covid-19 pandemic. This immense growth derived in part from the stressors of the pandemic but also from increased exposure to social media, which is rife with content that promotes eating disorders. Such content can induce eating disorders in viewers. This study aimed to create a multimodal deep learning model capable of determining whether a given social media post promotes eating disorders based on a combination of visual and textual data. A labeled dataset of Tweets was collected from Twitter, upon which twelve deep learning models were trained and tested. Based on model performance, the most effective deep learning model was the multimodal fusion of the RoBERTa natural language processing model and the MaxViT image classification model, attaining accuracy and F1 scores of 95.9% and 0.959 respectively. The RoBERTa and MaxViT fusion model, deployed to classify an unlabeled dataset of posts from the social media sites Tumblr and Reddit, generated similar classifications as previous research studies that did not employ artificial intelligence, showing that artificial intelligence can develop insights congruent to those of researchers. Additionally, the model was used to conduct a time-series analysis of yet unseen Tweets from eight Twitter hashtags, uncovering that the relative abundance of pro-eating disorder content has decreased drastically. However, since approximately 2018, pro-eating disorder content has either stopped its decline or risen once more in ampleness.
    inTformer: A Time-Embedded Attention-Based Transformer for Crash Likelihood Prediction at Intersections Using Connected Vehicle Data. (arXiv:2307.03854v2 [cs.LG] UPDATED)
    The real-time crash likelihood prediction model is an essential component of the proactive traffic safety management system. Over the years, numerous studies have attempted to construct a crash likelihood prediction model in order to enhance traffic safety, but mostly on freeways. In the majority of the existing studies, researchers have primarily employed a deep learning-based framework to identify crash potential. Lately, Transformer has emerged as a potential deep neural network that fundamentally operates through attention-based mechanisms. Transformer has several functional benefits over extant deep learning models such as Long Short-Term Memory (LSTM), Convolution Neural Network (CNN), etc. Firstly, Transformer can readily handle long-term dependencies in a data sequence. Secondly, Transformers can parallelly process all elements in a data sequence during training. Finally, a Transformer does not have the vanishing gradient issue. Realizing the immense possibility of Transformers, this paper proposes inTersection-Transformer (inTformer), a time-embedded attention-based Transformer model that can effectively predict intersection crash likelihood in real-time. The proposed model was evaluated using connected vehicle data extracted from INRIX and Center for Advanced Transportation Technology (CATT) Lab's Signal Analytics Platform. The data was parallelly formatted and stacked at different timesteps to develop nine inTformer models. The best inTformer model achieved a sensitivity of 73%. This model was also compared to earlier studies on crash likelihood prediction at intersections and with several established deep learning models trained on the same connected vehicle dataset. In every scenario, this inTformer outperformed the benchmark models confirming the viability of the proposed inTformer architecture.
    An Improved Uniform Convergence Bound with Fat-Shattering Dimension. (arXiv:2307.06644v1 [cs.LG])
    The fat-shattering dimension characterizes the uniform convergence property of real-valued functions. The state-of-the-art upper bounds feature a multiplicative squared logarithmic factor on the sample complexity, leaving an open gap with the existing lower bound. We provide an improved uniform convergence bound that closes this gap.
    GRAN is superior to GraphRNN: node orderings, kernel- and graph embeddings-based metrics for graph generators. (arXiv:2307.06709v1 [cs.LG])
    A wide variety of generative models for graphs have been proposed. They are used in drug discovery, road networks, neural architecture search, and program synthesis. Generating graphs has theoretical challenges, such as isomorphic representations -- evaluating how well a generative model performs is difficult. Which model to choose depending on the application domain? We extensively study kernel-based metrics on distributions of graph invariants and manifold-based and kernel-based metrics in graph embedding space. Manifold-based metrics outperform kernel-based metrics in embedding space. We use these metrics to compare GraphRNN and GRAN, two well-known generative models for graphs, and unveil the influence of node orderings. It shows the superiority of GRAN over GraphRNN - further, our proposed adaptation of GraphRNN with a depth-first search ordering is effective for small-sized graphs. A guideline on good practices regarding dataset selection and node feature initialization is provided. Our work is accompanied by open-source code and reproducible experiments.
    Temporal Label-Refinement for Weakly-Supervised Audio-Visual Event Localization. (arXiv:2307.06385v1 [cs.CV])
    Audio-Visual Event Localization (AVEL) is the task of temporally localizing and classifying \emph{audio-visual events}, i.e., events simultaneously visible and audible in a video. In this paper, we solve AVEL in a weakly-supervised setting, where only video-level event labels (their presence/absence, but not their locations in time) are available as supervision for training. Our idea is to use a base model to estimate labels on the training data at a finer temporal resolution than at the video level and re-train the model with these labels. I.e., we determine the subset of labels for each \emph{slice} of frames in a training video by (i) replacing the frames outside the slice with those from a second video having no overlap in video-level labels, and (ii) feeding this synthetic video into the base model to extract labels for just the slice in question. To handle the out-of-distribution nature of our synthetic videos, we propose an auxiliary objective for the base model that induces more reliable predictions of the localized event labels as desired. Our three-stage pipeline outperforms several existing AVEL methods with no architectural changes and improves performance on a related weakly-supervised task as well.
    Tackling Combinatorial Distribution Shift: A Matrix Completion Perspective. (arXiv:2307.06457v1 [cs.LG])
    Obtaining rigorous statistical guarantees for generalization under distribution shift remains an open and active research area. We study a setting we call combinatorial distribution shift, where (a) under the test- and training-distributions, the labels $z$ are determined by pairs of features $(x,y)$, (b) the training distribution has coverage of certain marginal distributions over $x$ and $y$ separately, but (c) the test distribution involves examples from a product distribution over $(x,y)$ that is {not} covered by the training distribution. Focusing on the special case where the labels are given by bilinear embeddings into a Hilbert space $H$: $\mathbb{E}[z \mid x,y ]=\langle f_{\star}(x),g_{\star}(y)\rangle_{{H}}$, we aim to extrapolate to a test distribution domain that is $not$ covered in training, i.e., achieving bilinear combinatorial extrapolation. Our setting generalizes a special case of matrix completion from missing-not-at-random data, for which all existing results require the ground-truth matrices to be either exactly low-rank, or to exhibit very sharp spectral cutoffs. In this work, we develop a series of theoretical results that enable bilinear combinatorial extrapolation under gradual spectral decay as observed in typical high-dimensional data, including novel algorithms, generalization guarantees, and linear-algebraic results. A key tool is a novel perturbation bound for the rank-$k$ singular value decomposition approximations between two matrices that depends on the relative spectral gap rather than the absolute spectral gap, a result that may be of broader independent interest.
    Discovering How Agents Learn Using Few Data. (arXiv:2307.06640v1 [cs.GT])
    Decentralized learning algorithms are an essential tool for designing multi-agent systems, as they enable agents to autonomously learn from their experience and past interactions. In this work, we propose a theoretical and algorithmic framework for real-time identification of the learning dynamics that govern agent behavior using a short burst of a single system trajectory. Our method identifies agent dynamics through polynomial regression, where we compensate for limited data by incorporating side-information constraints that capture fundamental assumptions or expectations about agent behavior. These constraints are enforced computationally using sum-of-squares optimization, leading to a hierarchy of increasingly better approximations of the true agent dynamics. Extensive experiments demonstrated that our approach, using only 5 samples from a short run of a single trajectory, accurately recovers the true dynamics across various benchmarks, including equilibrium selection and prediction of chaotic systems up to 10 Lyapunov times. These findings suggest that our approach has significant potential to support effective policy and decision-making in strategic multi-agent systems.
    Quantum Autoencoders for Learning Quantum Channel Codes. (arXiv:2307.06622v1 [quant-ph])
    This work investigates the application of quantum machine learning techniques for classical and quantum communication across different qubit channel models. By employing parameterized quantum circuits and a flexible channel noise model, we develop a machine learning framework to generate quantum channel codes and evaluate their effectiveness. We explore classical, entanglement-assisted, and quantum communication scenarios within our framework. Applying it to various quantum channel models as proof of concept, we demonstrate strong performance in each case. Our results highlight the potential of quantum machine learning in advancing research on quantum communication systems, enabling a better understanding of capacity bounds under modulation constraints, various communication settings, and diverse channel models.
    Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of Success. (arXiv:2208.07734v5 [cs.LG] UPDATED)
    Self-supervised learning (SSL) has emerged as a promising alternative to create supervisory signals to real-world problems, avoiding the extensive cost of manual labeling. SSL is particularly attractive for unsupervised tasks such as anomaly detection (AD), where labeled anomalies are rare or often nonexistent. A large catalog of augmentation functions has been used for SSL-based AD (SSAD) on image data, and recent works have reported that the type of augmentation has a significant impact on accuracy. Motivated by those, this work sets out to put image-based SSAD under a larger lens and investigate the role of data augmentation in SSAD. Through extensive experiments on 3 different detector models and across 420 AD tasks, we provide comprehensive numerical and visual evidences that the alignment between data augmentation and anomaly-generating mechanism is the key to the success of SSAD, and in the lack thereof, SSL may even impair accuracy. To the best of our knowledge, this is the first meta-analysis on the role of data augmentation in SSAD.
    DRAGON: A Dialogue-Based Robot for Assistive Navigation with Visual Language Grounding. (arXiv:2307.06924v1 [cs.RO])
    Persons with visual impairments (PwVI) have difficulties understanding and navigating spaces around them. Current wayfinding technologies either focus solely on navigation or provide limited communication about the environment. Motivated by recent advances in visual-language grounding and semantic navigation, we propose DRAGON, a guiding robot powered by a dialogue system and the ability to associate the environment with natural language. By understanding the commands from the user, DRAGON is able to guide the user to the desired landmarks on the map, describe the environment, and answer questions from visual observations. Through effective utilization of dialogue, the robot can ground the user's free-form descriptions to landmarks in the environment, and give the user semantic information through spoken language. We conduct a user study with blindfolded participants in an everyday indoor environment. Our results demonstrate that DRAGON is able to communicate with the user smoothly, provide a good guiding experience, and connect users with their surrounding environment in an intuitive manner.
    Sequential Experimental Design for X-Ray CT Using Deep Reinforcement Learning. (arXiv:2307.06343v1 [eess.IV])
    In X-ray Computed Tomography (CT), projections from many angles are acquired and used for 3D reconstruction. To make CT suitable for in-line quality control, reducing the number of angles while maintaining reconstruction quality is necessary. Sparse-angle tomography is a popular approach for obtaining 3D reconstructions from limited data. To optimize its performance, one can adapt scan angles sequentially to select the most informative angles for each scanned object. Mathematically, this corresponds to solving and optimal experimental design (OED) problem. OED problems are high-dimensional, non-convex, bi-level optimization problems that cannot be solved online, i.e., during the scan. To address these challenges, we pose the OED problem as a partially observable Markov decision process in a Bayesian framework, and solve it through deep reinforcement learning. The approach learns efficient non-greedy policies to solve a given class of OED problems through extensive offline training rather than solving a given OED problem directly via numerical optimization. As such, the trained policy can successfully find the most informative scan angles online. We use a policy training method based on the Actor-Critic approach and evaluate its performance on 2D tomography with synthetic data.
    Multivariate Time Series characterization and forecasting of VoIP traffic in real mobile networks. (arXiv:2307.06645v1 [cs.NI])
    Predicting the behavior of real-time traffic (e.g., VoIP) in mobility scenarios could help the operators to better plan their network infrastructures and to optimize the allocation of resources. Accordingly, in this work the authors propose a forecasting analysis of crucial QoS/QoE descriptors (some of which neglected in the technical literature) of VoIP traffic in a real mobile environment. The problem is formulated in terms of a multivariate time series analysis. Such a formalization allows to discover and model the temporal relationships among various descriptors and to forecast their behaviors for future periods. Techniques such as Vector Autoregressive models and machine learning (deep-based and tree-based) approaches are employed and compared in terms of performance and time complexity, by reframing the multivariate time series problem into a supervised learning one. Moreover, a series of auxiliary analyses (stationarity, orthogonal impulse responses, etc.) are performed to discover the analytical structure of the time series and to provide deep insights about their relationships. The whole theoretical analysis has an experimental counterpart since a set of trials across a real-world LTE-Advanced environment has been performed to collect, post-process and analyze about 600,000 voice packets, organized per flow and differentiated per codec.
    Assessment of the suitability of degradation models for the planning of CCTV inspections of sewer pipes. (arXiv:2307.06341v1 [cs.LG])
    The degradation of sewer pipes poses significant economical, environmental and health concerns. The maintenance of such assets requires structured plans to perform inspections, which are more efficient when structural and environmental features are considered along with the results of previous inspection reports. The development of such plans requires degradation models that can be based on statistical and machine learning methods. This work proposes a methodology to assess their suitability to plan inspections considering three dimensions: accuracy metrics, ability to produce long-term degradation curves and explainability. Results suggest that although ensemble models yield the highest accuracy, they are unable to infer the long-term degradation of the pipes, whereas the Logistic Regression offers a slightly less accurate model that is able to produce consistent degradation curves with a high explainability. A use case is presented to demonstrate this methodology and the efficiency of model-based planning compared to the current inspection plan.
    Stochastic Delay Differential Games: Financial Modeling and Machine Learning Algorithms. (arXiv:2307.06450v1 [math.OC])
    In this paper, we propose a numerical methodology for finding the closed-loop Nash equilibrium of stochastic delay differential games through deep learning. These games are prevalent in finance and economics where multi-agent interaction and delayed effects are often desired features in a model, but are introduced at the expense of increased dimensionality of the problem. This increased dimensionality is especially significant as that arising from the number of players is coupled with the potential infinite dimensionality caused by the delay. Our approach involves parameterizing the controls of each player using distinct recurrent neural networks. These recurrent neural network-based controls are then trained using a modified version of Brown's fictitious play, incorporating deep learning techniques. To evaluate the effectiveness of our methodology, we test it on finance-related problems with known solutions. Furthermore, we also develop new problems and derive their analytical Nash equilibrium solutions, which serve as additional benchmarks for assessing the performance of our proposed deep learning approach.  ( 2 min )
    Trainability, Expressivity and Interpretability in Gated Neural ODEs. (arXiv:2307.06398v1 [cs.LG])
    Understanding how the dynamics in biological and artificial neural networks implement the computations required for a task is a salient open question in machine learning and neuroscience. In particular, computations requiring complex memory storage and retrieval pose a significant challenge for these networks to implement or learn. Recently, a family of models described by neural ordinary differential equations (nODEs) has emerged as powerful dynamical neural network models capable of capturing complex dynamics. Here, we extend nODEs by endowing them with adaptive timescales using gating interactions. We refer to these as gated neural ODEs (gnODEs). Using a task that requires memory of continuous quantities, we demonstrate the inductive bias of the gnODEs to learn (approximate) continuous attractors. We further show how reduced-dimensional gnODEs retain their modeling power while greatly improving interpretability, even allowing explicit visualization of the structure of learned attractors. We introduce a novel measure of expressivity which probes the capacity of a neural network to generate complex trajectories. Using this measure, we explore how the phase-space dimension of the nODEs and the complexity of the function modeling the flow field contribute to expressivity. We see that a more complex function for modeling the flow field allows a lower-dimensional nODE to capture a given target dynamics. Finally, we demonstrate the benefit of gating in nODEs on several real-world tasks.  ( 2 min )
    Artificial Intelligence for Drug Discovery: Are We There Yet?. (arXiv:2307.06521v1 [cs.AI])
    Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small molecule drugs. AI technologies, such as generative chemistry, machine learning, and multi-property optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.
    Metal Oxide-based Gas Sensor Array for the VOCs Analysis in Complex Mixtures using Machine Learning. (arXiv:2307.06556v1 [physics.app-ph])
    Detection of Volatile Organic Compounds (VOCs) from the breath is becoming a viable route for the early detection of diseases non-invasively. This paper presents a sensor array with three metal oxide electrodes that can use machine learning methods to identify four distinct VOCs in a mixture. The metal oxide sensor array was subjected to various VOC concentrations, including ethanol, acetone, toluene and chloroform. The dataset obtained from individual gases and their mixtures were analyzed using multiple machine learning algorithms, such as Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree, Linear Regression, Logistic Regression, Naive Bayes, Linear Discriminant Analysis, Artificial Neural Network, and Support Vector Machine. KNN and RF have shown more than 99% accuracy in classifying different varying chemicals in the gas mixtures. In regression analysis, KNN has delivered the best results with R2 value of more than 0.99 and LOD of 0.012, 0.015, 0.014 and 0.025 PPM for predicting the concentrations of varying chemicals Acetone, Toluene, Ethanol, and Chloroform, respectively in complex mixtures. Therefore, it is demonstrated that the array utilizing the provided algorithms can classify and predict the concentrations of the four gases simultaneously for disease diagnosis and treatment monitoring.  ( 2 min )
    Microbial Genetic Algorithm-based Black-box Attack against Interpretable Deep Learning Systems. (arXiv:2307.06496v1 [cs.CV])
    Deep learning models are susceptible to adversarial samples in white and black-box environments. Although previous studies have shown high attack success rates, coupling DNN models with interpretation models could offer a sense of security when a human expert is involved, who can identify whether a given sample is benign or malicious. However, in white-box environments, interpretable deep learning systems (IDLSes) have been shown to be vulnerable to malicious manipulations. In black-box settings, as access to the components of IDLSes is limited, it becomes more challenging for the adversary to fool the system. In this work, we propose a Query-efficient Score-based black-box attack against IDLSes, QuScore, which requires no knowledge of the target model and its coupled interpretation model. QuScore is based on transfer-based and score-based methods by employing an effective microbial genetic algorithm. Our method is designed to reduce the number of queries necessary to carry out successful attacks, resulting in a more efficient process. By continuously refining the adversarial samples created based on feedback scores from the IDLS, our approach effectively navigates the search space to identify perturbations that can fool the system. We evaluate the attack's effectiveness on four CNN models (Inception, ResNet, VGG, DenseNet) and two interpretation models (CAM, Grad), using both ImageNet and CIFAR datasets. Our results show that the proposed approach is query-efficient with a high attack success rate that can reach between 95% and 100% and transferability with an average success rate of 69% in the ImageNet and CIFAR datasets. Our attack method generates adversarial examples with attribution maps that resemble benign samples. We have also demonstrated that our attack is resilient against various preprocessing defense techniques and can easily be transferred to different DNN models.  ( 3 min )
    Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Large Random Graphs. (arXiv:2304.11140v2 [stat.ML] UPDATED)
    We study the convergence of message passing graph neural networks on random graph models to their continuous counterpart as the number of nodes tends to infinity. Until now, this convergence was only known for architectures with aggregation functions in the form of normalized means, or, equivalently, of an application of classical operators like the adjacency matrix or the graph Laplacian. We extend such results to a large class of aggregation functions, that encompasses all classically used message passing graph neural networks, such as attention-based message passing, max convolutional message passing or (degree-normalized) convolutional message passing. Under mild assumptions, we give non-asymptotic bounds with high probability to quantify this convergence. Our main result is based on the McDiarmid inequality. Interestingly, this result does not apply to the case where the aggregation is a coordinate-wise maximum. We treat this case separately and obtain a different convergence rate.
    Deep Network Approximation: Beyond ReLU to Diverse Activation Functions. (arXiv:2307.06555v1 [cs.LG])
    This paper explores the expressive power of deep neural networks for a diverse range of activation functions. An activation function set $\mathscr{A}$ is defined to encompass the majority of commonly used activation functions, such as $\mathtt{ReLU}$, $\mathtt{LeakyReLU}$, $\mathtt{ReLU}^2$, $\mathtt{ELU}$, $\mathtt{SELU}$, $\mathtt{Softplus}$, $\mathtt{GELU}$, $\mathtt{SiLU}$, $\mathtt{Swish}$, $\mathtt{Mish}$, $\mathtt{Sigmoid}$, $\mathtt{Tanh}$, $\mathtt{Arctan}$, $\mathtt{Softsign}$, $\mathtt{dSiLU}$, and $\mathtt{SRS}$. We demonstrate that for any activation function $\varrho\in \mathscr{A}$, a $\mathtt{ReLU}$ network of width $N$ and depth $L$ can be approximated to arbitrary precision by a $\varrho$-activated network of width $6N$ and depth $2L$ on any bounded set. This finding enables the extension of most approximation results achieved with $\mathtt{ReLU}$ networks to a wide variety of other activation functions, at the cost of slightly larger constants.  ( 2 min )
    Convolutional Neural Networks for Sentiment Analysis on Weibo Data: A Natural Language Processing Approach. (arXiv:2307.06540v1 [cs.CL])
    This study addressed the complex task of sentiment analysis on a dataset of 119,988 original tweets from Weibo using a Convolutional Neural Network (CNN), offering a new approach to Natural Language Processing (NLP). The data, sourced from Baidu's PaddlePaddle AI platform, were meticulously preprocessed, tokenized, and categorized based on sentiment labels. A CNN-based model was utilized, leveraging word embeddings for feature extraction, and trained to perform sentiment classification. The model achieved a macro-average F1-score of approximately 0.73 on the test set, showing balanced performance across positive, neutral, and negative sentiments. The findings underscore the effectiveness of CNNs for sentiment analysis tasks, with implications for practical applications in social media analysis, market research, and policy studies. The complete experimental content and code have been made publicly available on the Kaggle data platform for further research and development. Future work may involve exploring different architectures, such as Recurrent Neural Networks (RNN) or transformers, or using more complex pre-trained models like BERT, to further improve the model's ability to understand linguistic nuances and context.  ( 2 min )
    Introducing Foundation Models as Surrogate Models: Advancing Towards More Practical Adversarial Attacks. (arXiv:2307.06608v1 [cs.LG])
    Recently, the no-box adversarial attack, in which the attacker lacks access to the model's architecture, weights, and training data, become the most practical and challenging attack setup. However, there is an unawareness of the potential and flexibility inherent in the surrogate model selection process on no-box setting. Inspired by the burgeoning interest in utilizing foundational models to address downstream tasks, this paper adopts an innovative idea that 1) recasting adversarial attack as a downstream task. Specifically, image noise generation to meet the emerging trend and 2) introducing foundational models as surrogate models. Harnessing the concept of non-robust features, we elaborate on two guiding principles for surrogate model selection to explain why the foundational model is an optimal choice for this role. However, paradoxically, we observe that these foundational models underperform. Analyzing this unexpected behavior within the feature space, we attribute the lackluster performance of foundational models (e.g., CLIP) to their significant representational capacity and, conversely, their lack of discriminative prowess. To mitigate this issue, we propose the use of a margin-based loss strategy for the fine-tuning of foundational models on target images. The experimental results verify that our approach, which employs the basic Fast Gradient Sign Method (FGSM) attack algorithm, outstrips the performance of other, more convoluted algorithms. We conclude by advocating for the research community to consider surrogate models as crucial determinants in the effectiveness of adversarial attacks in no-box settings. The implications of our work bear relevance for improving the efficacy of such adversarial attacks and the overall robustness of AI systems.  ( 3 min )
    Equalization in Dispersion-Managed Systems Using Learned Digital Back-Propagation. (arXiv:2307.06821v1 [cs.NI])
    In this paper, we investigate the use of the learned digital back-propagation (LDBP) for equalizing dual-polarization fiber-optic transmission in dispersion-managed (DM) links. LDBP is a deep neural network that optimizes the parameters of DBP using the stochastic gradient descent. We evaluate DBP and LDBP in a simulated WDM dual-polarization fiber transmission system operating at the bitrate of 256 Gbit/s per channel, with a dispersion map designed for a 2016 km link with 15% residual dispersion. Our results show that in single-channel transmission, LDBP achieves an effective signal-to-noise ratio improvement of 6.3 dB and 2.5 dB, respectively, over linear equalization and DBP. In WDM transmission, the corresponding $Q$-factor gains are 1.1 dB and 0.4 dB, respectively. Additionally, we conduct a complexity analysis, which reveals that a frequency-domain implementation of LDBP and DBP is more favorable in terms of complexity than the time-domain implementation. These findings demonstrate the effectiveness of LDBP in mitigating the nonlinear effects in DM fiber-optic transmission systems.
    Misclassification in Automated Content Analysis Causes Bias in Regression. Can We Fix It? Yes We Can!. (arXiv:2307.06483v1 [cs.LG])
    Automated classifiers (ACs), often built via supervised machine learning (SML), can categorize large, statistically powerful samples of data ranging from text to images and video, and have become widely popular measurement devices in communication science and related fields. Despite this popularity, even highly accurate classifiers make errors that cause misclassification bias and misleading results in downstream analyses-unless such analyses account for these errors. As we show in a systematic literature review of SML applications, communication scholars largely ignore misclassification bias. In principle, existing statistical methods can use "gold standard" validation data, such as that created by human annotators, to correct misclassification bias and produce consistent estimates. We introduce and test such methods, including a new method we design and implement in the R package misclassificationmodels, via Monte Carlo simulations designed to reveal each method's limitations, which we also release. Based on our results, we recommend our new error correction method as it is versatile and efficient. In sum, automated classifiers, even those below common accuracy standards or making systematic misclassifications, can be useful for measurement with careful study design and appropriate error correction methods.
    Energy Discrepancies: A Score-Independent Loss for Energy-Based Models. (arXiv:2307.06431v1 [stat.ML])
    Energy-based models are a simple yet powerful class of probabilistic models, but their widespread adoption has been limited by the computational burden of training them. We propose a novel loss function called Energy Discrepancy (ED) which does not rely on the computation of scores or expensive Markov chain Monte Carlo. We show that ED approaches the explicit score matching and negative log-likelihood loss under different limits, effectively interpolating between both. Consequently, minimum ED estimation overcomes the problem of nearsightedness encountered in score-based estimation methods, while also enjoying theoretical guarantees. Through numerical experiments, we demonstrate that ED learns low-dimensional data distributions faster and more accurately than explicit score matching or contrastive divergence. For high-dimensional image data, we describe how the manifold hypothesis puts limitations on our approach and demonstrate the effectiveness of energy discrepancy by training the energy-based model as a prior of a variational decoder model.  ( 2 min )
    Leveraging Contextual Counterfactuals Toward Belief Calibration. (arXiv:2307.06513v1 [cs.AI])
    Beliefs and values are increasingly being incorporated into our AI systems through alignment processes, such as carefully curating data collection principles or regularizing the loss function used for training. However, the meta-alignment problem is that these human beliefs are diverse and not aligned across populations; furthermore, the implicit strength of each belief may not be well calibrated even among humans, especially when trying to generalize across contexts. Specifically, in high regret situations, we observe that contextual counterfactuals and recourse costs are particularly important in updating a decision maker's beliefs and the strengths to which such beliefs are held. Therefore, we argue that including counterfactuals is key to an accurate calibration of beliefs during alignment. To do this, we first segment belief diversity into two categories: subjectivity (across individuals within a population) and epistemic uncertainty (within an individual across different contexts). By leveraging our notion of epistemic uncertainty, we introduce `the belief calibration cycle' framework to more holistically calibrate this diversity of beliefs with context-driven counterfactual reasoning by using a multi-objective optimization. We empirically apply our framework for finding a Pareto frontier of clustered optimal belief strengths that generalize across different contexts, demonstrating its efficacy on a toy dataset for credit decisions.
    On the Effective Horizon of Inverse Reinforcement Learning. (arXiv:2307.06541v1 [cs.LG])
    Inverse reinforcement learning (IRL) algorithms often rely on (forward) reinforcement learning or planning over a given time horizon to compute an approximately optimal policy for a hypothesized reward function and then match this policy with expert demonstrations. The time horizon plays a critical role in determining both the accuracy of reward estimate and the computational efficiency of IRL algorithms. Interestingly, an effective time horizon shorter than the ground-truth value often produces better results faster. This work formally analyzes this phenomenon and provides an explanation: the time horizon controls the complexity of an induced policy class and mitigates overfitting with limited data. This analysis leads to a principled choice of the effective horizon for IRL. It also prompts us to reexamine the classic IRL formulation: it is more natural to learn jointly the reward and the effective horizon together rather than the reward alone with a given horizon. Our experimental results confirm the theoretical analysis.  ( 2 min )
    Online Distributed Learning with Quantized Finite-Time Coordination. (arXiv:2307.06620v1 [cs.LG])
    In this paper we consider online distributed learning problems. Online distributed learning refers to the process of training learning models on distributed data sources. In our setting a set of agents need to cooperatively train a learning model from streaming data. Differently from federated learning, the proposed approach does not rely on a central server but only on peer-to-peer communications among the agents. This approach is often used in scenarios where data cannot be moved to a centralized location due to privacy, security, or cost reasons. In order to overcome the absence of a central server, we propose a distributed algorithm that relies on a quantized, finite-time coordination protocol to aggregate the locally trained models. Furthermore, our algorithm allows for the use of stochastic gradients during local training. Stochastic gradients are computed using a randomly sampled subset of the local training data, which makes the proposed algorithm more efficient and scalable than traditional gradient descent. In our paper, we analyze the performance of the proposed algorithm in terms of the mean distance from the online solution. Finally, we present numerical results for a logistic regression task.  ( 2 min )
    Deep Neural Networks for Semiparametric Frailty Models via H-likelihood. (arXiv:2307.06581v1 [stat.ML])
    For prediction of clustered time-to-event data, we propose a new deep neural network based gamma frailty model (DNN-FM). An advantage of the proposed model is that the joint maximization of the new h-likelihood provides maximum likelihood estimators for fixed parameters and best unbiased predictors for random frailties. Thus, the proposed DNN-FM is trained by using a negative profiled h-likelihood as a loss function, constructed by profiling out the non-parametric baseline hazard. Experimental studies show that the proposed method enhances the prediction performance of the existing methods. A real data analysis shows that the inclusion of subject-specific frailties helps to improve prediction of the DNN based Cox model (DNN-Cox).  ( 2 min )
    Prescriptive Process Monitoring Under Resource Constraints: A Reinforcement Learning Approach. (arXiv:2307.06564v1 [cs.AI])
    Prescriptive process monitoring methods seek to optimize the performance of business processes by triggering interventions at runtime, thereby increasing the probability of positive case outcomes. These interventions are triggered according to an intervention policy. Reinforcement learning has been put forward as an approach to learning intervention policies through trial and error. Existing approaches in this space assume that the number of resources available to perform interventions in a process is unlimited, an unrealistic assumption in practice. This paper argues that, in the presence of resource constraints, a key dilemma in the field of prescriptive process monitoring is to trigger interventions based not only on predictions of their necessity, timeliness, or effect but also on the uncertainty of these predictions and the level of resource utilization. Indeed, committing scarce resources to an intervention when the necessity or effects of this intervention are highly uncertain may intuitively lead to suboptimal intervention effects. Accordingly, the paper proposes a reinforcement learning approach for prescriptive process monitoring that leverages conformal prediction techniques to consider the uncertainty of the predictions upon which an intervention decision is based. An evaluation using real-life datasets demonstrates that explicitly modeling uncertainty using conformal predictions helps reinforcement learning agents converge towards policies with higher net intervention gain  ( 2 min )
    No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models. (arXiv:2307.06440v1 [cs.LG])
    The computation necessary for training Transformer-based language models has skyrocketed in recent years. This trend has motivated research on efficient training algorithms designed to improve training, validation, and downstream performance faster than standard training. In this work, we revisit three categories of such algorithms: dynamic architectures (layer stacking, layer dropping), batch selection (selective backprop, RHO loss), and efficient optimizers (Lion, Sophia). When pre-training BERT and T5 with a fixed computation budget using such methods, we find that their training, validation, and downstream gains vanish compared to a baseline with a fully-decayed learning rate. We define an evaluation protocol that enables computation to be done on arbitrary machines by mapping all computation time to a reference machine which we call reference system time. We discuss the limitations of our proposed protocol and release our code to encourage rigorous research in efficient training procedures: https://github.com/JeanKaddour/NoTrainNoGain.  ( 2 min )
    Tensor Decompositions Meet Control Theory: Learning General Mixtures of Linear Dynamical Systems. (arXiv:2307.06538v1 [cs.LG])
    Recently Chen and Poor initiated the study of learning mixtures of linear dynamical systems. While linear dynamical systems already have wide-ranging applications in modeling time-series data, using mixture models can lead to a better fit or even a richer understanding of underlying subpopulations represented in the data. In this work we give a new approach to learning mixtures of linear dynamical systems that is based on tensor decompositions. As a result, our algorithm succeeds without strong separation conditions on the components, and can be used to compete with the Bayes optimal clustering of the trajectories. Moreover our algorithm works in the challenging partially-observed setting. Our starting point is the simple but powerful observation that the classic Ho-Kalman algorithm is a close relative of modern tensor decomposition methods for learning latent variable models. This gives us a playbook for how to extend it to work with more complicated generative models.  ( 2 min )
    DSV: An Alignment Validation Loss for Self-supervised Outlier Model Selection. (arXiv:2307.06534v1 [cs.LG])
    Self-supervised learning (SSL) has proven effective in solving various problems by generating internal supervisory signals. Unsupervised anomaly detection, which faces the high cost of obtaining true labels, is an area that can greatly benefit from SSL. However, recent literature suggests that tuning the hyperparameters (HP) of data augmentation functions is crucial to the success of SSL-based anomaly detection (SSAD), yet a systematic method for doing so remains unknown. In this work, we propose DSV (Discordance and Separability Validation), an unsupervised validation loss to select high-performing detection models with effective augmentation HPs. DSV captures the alignment between an augmentation function and the anomaly-generating mechanism with surrogate losses, which approximate the discordance and separability of test data, respectively. As a result, the evaluation via DSV leads to selecting an effective SSAD model exhibiting better alignment, which results in high detection accuracy. We theoretically derive the degree of approximation conducted by the surrogate losses and empirically show that DSV outperforms a wide range of baselines on 21 real-world tasks.  ( 2 min )
    Ageing Analysis of Embedded SRAM on a Large-Scale Testbed Using Machine Learning. (arXiv:2307.06693v1 [cs.AR])
    Ageing detection and failure prediction are essential in many Internet of Things (IoT) deployments, which operate huge quantities of embedded devices unattended in the field for years. In this paper, we present a large-scale empirical analysis of natural SRAM wear-out using 154 boards from a general-purpose testbed. Starting from SRAM initialization bias, which each node can easily collect at startup, we apply various metrics for feature extraction and experiment with common machine learning methods to predict the age of operation for this node. Our findings indicate that even though ageing impacts are subtle, our indicators can well estimate usage times with an $R^2$ score of 0.77 and a mean error of 24% using regressors, and with an F1 score above 0.6 for classifiers applying a six-months resolution.
    IntelliGraphs: Datasets for Benchmarking Knowledge Graph Generation. (arXiv:2307.06698v1 [cs.AI])
    Knowledge Graph Embedding (KGE) models are used to learn continuous representations of entities and relations. A key task in the literature is predicting missing links between entities. However, Knowledge Graphs are not just sets of links but also have semantics underlying their structure. Semantics is crucial in several downstream tasks, such as query answering or reasoning. We introduce the subgraph inference task, where a model has to generate likely and semantically valid subgraphs. We propose IntelliGraphs, a set of five new Knowledge Graph datasets. The IntelliGraphs datasets contain subgraphs with semantics expressed in logical rules for evaluating subgraph inference. We also present the dataset generator that produced the synthetic datasets. We designed four novel baseline models, which include three models based on traditional KGEs. We evaluate their expressiveness and show that these models cannot capture the semantics. We believe this benchmark will encourage the development of machine learning models that emphasize semantic understanding.
    Bregman Deviations of Generic Exponential Families. (arXiv:2201.07306v4 [cs.LG] UPDATED)
    We revisit the method of mixture technique, also known as the Laplace method, to study the concentration phenomenon in generic exponential families. Combining the properties of Bregman divergence associated with log-partition function of the family with the method of mixtures for super-martingales, we establish a generic bound controlling the Bregman divergence between the parameter of the family and a finite sample estimate of the parameter. Our bound is time-uniform and makes appear a quantity extending the classical information gain to exponential families, which we call the Bregman information gain. For the practitioner, we instantiate this novel bound to several classical families, e.g., Gaussian, Bernoulli, Exponential, Weibull, Pareto, Poisson and Chi-square yielding explicit forms of the confidence sets and the Bregman information gain. We further numerically compare the resulting confidence bounds to state-of-the-art alternatives for time-uniform concentration and show that this novel method yields competitive results. Finally, we highlight the benefit of our concentration bounds on some illustrative applications.
    Spectral-Bias and Kernel-Task Alignment in Physically Informed Neural Networks. (arXiv:2307.06362v1 [stat.ML])
    Physically informed neural networks (PINNs) are a promising emerging method for solving differential equations. As in many other deep learning approaches, the choice of PINN design and training protocol requires careful craftsmanship. Here, we suggest a comprehensive theoretical framework that sheds light on this important problem. Leveraging an equivalence between infinitely over-parameterized neural networks and Gaussian process regression (GPR), we derive an integro-differential equation that governs PINN prediction in the large data-set limit -- the Neurally-Informed Equation (NIE). This equation augments the original one by a kernel term reflecting architecture choices and allows quantifying implicit bias induced by the network via a spectral decomposition of the source term in the original differential equation.
    Incomplete Utterance Rewriting as Sequential Greedy Tagging. (arXiv:2307.06337v1 [cs.LG])
    The task of incomplete utterance rewriting has recently gotten much attention. Previous models struggled to extract information from the dialogue context, as evidenced by the low restoration scores. To address this issue, we propose a novel sequence tagging-based model, which is more adept at extracting information from context. Meanwhile, we introduce speaker-aware embedding to model speaker variation. Experiments on multiple public datasets show that our model achieves optimal results on all nine restoration scores while having other metric scores comparable to previous state-of-the-art models. Furthermore, benefitting from the model's simplicity, our approach outperforms most previous models on inference speed.
  • Open

    balance -- a Python package for balancing biased data samples. (arXiv:2307.06024v2 [stat.CO] UPDATED)
    Surveys are an important research tool, providing unique measurements on subjective experiences such as sentiment and opinions that cannot be measured by other means. However, because survey data is collected from a self-selected group of participants, directly inferring insights from it to a population of interest, or training ML models on such data, can lead to erroneous estimates or under-performing models. In this paper we present balance, an open-source Python package by Meta, offering a simple workflow for analyzing and adjusting biased data samples with respect to a population of interest. The balance workflow includes three steps: understanding the initial bias in the data relative to a target we would like to infer, adjusting the data to correct for the bias by producing weights for each unit in the sample based on propensity scores, and evaluating the final biases and the variance inflation after applying the fitted weights. The package provides a simple API that can be used by researchers and data scientists from a wide range of fields on a variety of data. The paper provides the relevant context, methodological background, and presents the package's API.
    Convergence of Message Passing Graph Neural Networks with Generic Aggregation On Large Random Graphs. (arXiv:2304.11140v2 [stat.ML] UPDATED)
    We study the convergence of message passing graph neural networks on random graph models to their continuous counterpart as the number of nodes tends to infinity. Until now, this convergence was only known for architectures with aggregation functions in the form of normalized means, or, equivalently, of an application of classical operators like the adjacency matrix or the graph Laplacian. We extend such results to a large class of aggregation functions, that encompasses all classically used message passing graph neural networks, such as attention-based message passing, max convolutional message passing or (degree-normalized) convolutional message passing. Under mild assumptions, we give non-asymptotic bounds with high probability to quantify this convergence. Our main result is based on the McDiarmid inequality. Interestingly, this result does not apply to the case where the aggregation is a coordinate-wise maximum. We treat this case separately and obtain a different convergence rate.
    Tensor Completion Made Practical. (arXiv:2006.03134v2 [cs.DS] CROSS LISTED)
    Tensor completion is a natural higher-order generalization of matrix completion where the goal is to recover a low-rank tensor from sparse observations of its entries. Existing algorithms are either heuristic without provable guarantees, based on solving large semidefinite programs which are impractical to run, or make strong assumptions such as requiring the factors to be nearly orthogonal. In this paper we introduce a new variant of alternating minimization, which in turn is inspired by understanding how the progress measures that guide convergence of alternating minimization in the matrix setting need to be adapted to the tensor setting. We show strong provable guarantees, including showing that our algorithm converges linearly to the true tensors even when the factors are highly correlated and can be implemented in nearly linear time. Moreover our algorithm is also highly practical and we show that we can complete third order tensors with a thousand dimensions from observing a tiny fraction of its entries. In contrast, and somewhat surprisingly, we show that the standard version of alternating minimization, without our new twist, can converge at a drastically slower rate in practice.
    Emergent Neural Network Mechanisms for Generalization to Objects in Novel Orientations. (arXiv:2109.13445v2 [cs.CV] UPDATED)
    The capability of Deep Neural Networks (DNNs) to recognize objects in orientations outside the distribution of the training data is not well understood. We present evidence that DNNs are capable of generalizing to objects in novel orientations by disseminating orientation-invariance obtained from familiar objects seen from many viewpoints. This capability strengthens when training the DNN with an increasing number of familiar objects, but only in orientations that involve 2D rotations of familiar orientations. We show that this dissemination is achieved via neurons tuned to common features between familiar and unfamiliar objects. These results implicate brain-like neural mechanisms for generalization.
    Bayesian taut splines for estimating the number of modes. (arXiv:2307.05825v1 [stat.ME] CROSS LISTED)
    The number of modes in a probability density function is representative of the model's complexity and can also be viewed as the number of existing subpopulations. Despite its relevance, little research has been devoted to its estimation. Focusing on the univariate setting, we propose a novel approach targeting prediction accuracy inspired by some overlooked aspects of the problem. We argue for the need for structure in the solutions, the subjective and uncertain nature of modes, and the convenience of a holistic view blending global and local density properties. Our method builds upon a combination of flexible kernel estimators and parsimonious compositional splines. Feature exploration, model selection and mode testing are implemented in the Bayesian inference paradigm, providing soft solutions and allowing to incorporate expert judgement in the process. The usefulness of our proposal is illustrated through a case study in sports analytics, showcasing multiple companion visualisation tools. A thorough simulation study demonstrates that traditional modality-driven approaches paradoxically struggle to provide accurate results. In this context, our method emerges as a top-tier alternative offering innovative solutions for analysts.
    Towards Learning to Imitate from a Single Video Demonstration. (arXiv:1901.07186v4 [cs.LG] UPDATED)
    Agents that can learn to imitate given video observation -- \emph{without direct access to state or action information} are more applicable to learning in the natural world. However, formulating a reinforcement learning (RL) agent that facilitates this goal remains a significant challenge. We approach this challenge using contrastive training to learn a reward function comparing an agent's behaviour with a single demonstration. We use a Siamese recurrent neural network architecture to learn rewards in space and time between motion clips while training an RL policy to minimize this distance. Through experimentation, we also find that the inclusion of multi-task data and additional image encoding losses improve the temporal consistency of the learned rewards and, as a result, significantly improves policy learning. We demonstrate our approach on simulated humanoid, dog, and raptor agents in 2D and a quadruped and a humanoid in 3D. We show that our method outperforms current state-of-the-art techniques in these environments and can learn to imitate from a single video demonstration.
    Accelerated stochastic approximation with state-dependent noise. (arXiv:2307.01497v2 [math.OC] UPDATED)
    We consider a class of stochastic smooth convex optimization problems under rather general assumptions on the noise in the stochastic gradient observation. As opposed to the classical problem setting in which the variance of noise is assumed to be uniformly bounded, herein we assume that the variance of stochastic gradients is related to the "sub-optimality" of the approximate solutions delivered by the algorithm. Such problems naturally arise in a variety of applications, in particular, in the well-known generalized linear regression problem in statistics. However, to the best of our knowledge, none of the existing stochastic approximation algorithms for solving this class of problems attain optimality in terms of the dependence on accuracy, problem parameters, and mini-batch size. We discuss two non-Euclidean accelerated stochastic approximation routines--stochastic accelerated gradient descent (SAGD) and stochastic gradient extrapolation (SGE)--which carry a particular duality relationship. We show that both SAGD and SGE, under appropriate conditions, achieve the optimal convergence rate, attaining the optimal iteration and sample complexities simultaneously. However, corresponding assumptions for the SGE algorithm are more general; they allow, for instance, for efficient application of the SGE to statistical estimation problems under heavy tail noises and discontinuous score functions. We also discuss the application of the SGE to problems satisfying quadratic growth conditions, and show how it can be used to recover sparse solutions. Finally, we report on some simulation experiments to illustrate numerical performance of our proposed algorithms in high-dimensional settings.
    Cramer Type Distances for Learning Gaussian Mixture Models by Gradient Descent. (arXiv:2307.06753v1 [cs.LG])
    The learning of Gaussian Mixture Models (also referred to simply as GMMs) plays an important role in machine learning. Known for their expressiveness and interpretability, Gaussian mixture models have a wide range of applications, from statistics, computer vision to distributional reinforcement learning. However, as of today, few known algorithms can fit or learn these models, some of which include Expectation-Maximization algorithms and Sliced Wasserstein Distance. Even fewer algorithms are compatible with gradient descent, the common learning process for neural networks. In this paper, we derive a closed formula of two GMMs in the univariate, one-dimensional case, then propose a distance function called Sliced Cram\'er 2-distance for learning general multivariate GMMs. Our approach has several advantages over many previous methods. First, it has a closed-form expression for the univariate case and is easy to compute and implement using common machine learning libraries (e.g., PyTorch and TensorFlow). Second, it is compatible with gradient descent, which enables us to integrate GMMs with neural networks seamlessly. Third, it can fit a GMM not only to a set of data points, but also to another GMM directly, without sampling from the target model. And fourth, it has some theoretical guarantees like global gradient boundedness and unbiased sampling gradient. These features are especially useful for distributional reinforcement learning and Deep Q Networks, where the goal is to learn a distribution over future rewards. We will also construct a Gaussian Mixture Distributional Deep Q Network as a toy example to demonstrate its effectiveness. Compared with previous models, this model is parameter efficient in terms of representing a distribution and possesses better interpretability.
    On the Validity of Conformal Prediction for Network Data Under Non-Uniform Sampling. (arXiv:2306.07252v4 [math.ST] UPDATED)
    We study the properties of conformal prediction for network data under various sampling mechanisms that commonly arise in practice but often result in a non-representative sample of nodes. We interpret these sampling mechanisms as selection rules applied to a superpopulation and study the validity of conformal prediction conditional on an appropriate selection event. We show that the sampled subarray is exchangeable conditional on the selection event if the selection rule satisfies a permutation invariance property and a joint exchangeability condition holds for the superpopulation. Our result implies the finite-sample validity of conformal prediction for certain selection events related to ego networks and snowball sampling. We also show that when data are sampled via a random walk on a graph, a variant of weighted conformal prediction yields asymptotically valid prediction sets for an independently selected node from the population.
    Learning Graph ARMA Processes from Time-Vertex Spectra. (arXiv:2302.06887v2 [stat.ML] UPDATED)
    The modeling of time-varying graph signals as stationary time-vertex stochastic processes permits the inference of missing signal values by efficiently employing the correlation patterns of the process across different graph nodes and time instants. In this study, we propose an algorithm for computing graph autoregressive moving average (graph ARMA) processes based on learning the joint time-vertex power spectral density of the process from its incomplete realizations for the task of signal interpolation. Our solution relies on first roughly estimating the joint spectrum of the process from partially observed realizations and then refining this estimate by projecting it onto the spectrum manifold of the graph ARMA process through convex relaxations. The initially missing signal values are then estimated based on the learnt model. Experimental results show that the proposed approach achieves high accuracy in time-vertex signal estimation problems.
    An Improved Uniform Convergence Bound with Fat-Shattering Dimension. (arXiv:2307.06644v1 [cs.LG])
    The fat-shattering dimension characterizes the uniform convergence property of real-valued functions. The state-of-the-art upper bounds feature a multiplicative squared logarithmic factor on the sample complexity, leaving an open gap with the existing lower bound. We provide an improved uniform convergence bound that closes this gap.
    Spectral-Bias and Kernel-Task Alignment in Physically Informed Neural Networks. (arXiv:2307.06362v1 [stat.ML])
    Physically informed neural networks (PINNs) are a promising emerging method for solving differential equations. As in many other deep learning approaches, the choice of PINN design and training protocol requires careful craftsmanship. Here, we suggest a comprehensive theoretical framework that sheds light on this important problem. Leveraging an equivalence between infinitely over-parameterized neural networks and Gaussian process regression (GPR), we derive an integro-differential equation that governs PINN prediction in the large data-set limit -- the Neurally-Informed Equation (NIE). This equation augments the original one by a kernel term reflecting architecture choices and allows quantifying implicit bias induced by the network via a spectral decomposition of the source term in the original differential equation.
    Robust online active learning. (arXiv:2302.00422v5 [stat.ML] UPDATED)
    In many industrial applications, obtaining labeled observations is not straightforward as it often requires the intervention of human experts or the use of expensive testing equipment. In these circumstances, active learning can be highly beneficial in suggesting the most informative data points to be used when fitting a model. Reducing the number of observations needed for model development alleviates both the computational burden required for training and the operational expenses related to labeling. Online active learning, in particular, is useful in high-volume production processes where the decision about the acquisition of the label for a data point needs to be taken within an extremely short time frame. However, despite the recent efforts to develop online active learning strategies, the behavior of these methods in the presence of outliers has not been thoroughly examined. In this work, we investigate the performance of online active linear regression in contaminated data streams. Our study shows that the currently available query strategies are prone to sample outliers, whose inclusion in the training set eventually degrades the predictive performance of the models. To address this issue, we propose a solution that bounds the search area of a conditional D-optimal algorithm and uses a robust estimator. Our approach strikes a balance between exploring unseen regions of the input space and protecting against outliers. Through numerical simulations, we show that the proposed method is effective in improving the performance of online active learning in the presence of outliers, thus expanding the potential applications of this powerful tool.  ( 3 min )
    Multivariate Time Series characterization and forecasting of VoIP traffic in real mobile networks. (arXiv:2307.06645v1 [cs.NI])
    Predicting the behavior of real-time traffic (e.g., VoIP) in mobility scenarios could help the operators to better plan their network infrastructures and to optimize the allocation of resources. Accordingly, in this work the authors propose a forecasting analysis of crucial QoS/QoE descriptors (some of which neglected in the technical literature) of VoIP traffic in a real mobile environment. The problem is formulated in terms of a multivariate time series analysis. Such a formalization allows to discover and model the temporal relationships among various descriptors and to forecast their behaviors for future periods. Techniques such as Vector Autoregressive models and machine learning (deep-based and tree-based) approaches are employed and compared in terms of performance and time complexity, by reframing the multivariate time series problem into a supervised learning one. Moreover, a series of auxiliary analyses (stationarity, orthogonal impulse responses, etc.) are performed to discover the analytical structure of the time series and to provide deep insights about their relationships. The whole theoretical analysis has an experimental counterpart since a set of trials across a real-world LTE-Advanced environment has been performed to collect, post-process and analyze about 600,000 voice packets, organized per flow and differentiated per codec.  ( 2 min )
    Multiple Testing Framework for Out-of-Distribution Detection. (arXiv:2206.09522v4 [stat.ML] UPDATED)
    We study the problem of Out-of-Distribution (OOD) detection, that is, detecting whether a learning algorithm's output can be trusted at inference time. While a number of tests for OOD detection have been proposed in prior work, a formal framework for studying this problem is lacking. We propose a definition for the notion of OOD that includes both the input distribution and the learning algorithm, which provides insights for the construction of powerful tests for OOD detection. We propose a multiple hypothesis testing inspired procedure to systematically combine any number of different statistics from the learning algorithm using conformal p-values. We further provide strong guarantees on the probability of incorrectly classifying an in-distribution sample as OOD. In our experiments, we find that threshold-based tests proposed in prior work perform well in specific settings, but not uniformly well across different types of OOD instances. In contrast, our proposed method that combines multiple statistics performs uniformly well across different datasets and neural networks.  ( 2 min )
    Adversarial Policies Beat Superhuman Go AIs. (arXiv:2211.00241v4 [cs.LG] UPDATED)
    We attack the state-of-the-art Go-playing AI system KataGo by training adversarial policies against it, achieving a >97% win rate against KataGo running at superhuman settings. Our adversaries do not win by playing Go well. Instead, they trick KataGo into making serious blunders. Our attack transfers zero-shot to other superhuman Go-playing AIs, and is comprehensible to the extent that human experts can implement it without algorithmic assistance to consistently beat superhuman AIs. The core vulnerability uncovered by our attack persists even in KataGo agents adversarially trained to defend against our attack. Our results demonstrate that even superhuman AI systems may harbor surprising failure modes. Example games are available https://goattack.far.ai/.  ( 2 min )
    Deep Neural Networks for Semiparametric Frailty Models via H-likelihood. (arXiv:2307.06581v1 [stat.ML])
    For prediction of clustered time-to-event data, we propose a new deep neural network based gamma frailty model (DNN-FM). An advantage of the proposed model is that the joint maximization of the new h-likelihood provides maximum likelihood estimators for fixed parameters and best unbiased predictors for random frailties. Thus, the proposed DNN-FM is trained by using a negative profiled h-likelihood as a loss function, constructed by profiling out the non-parametric baseline hazard. Experimental studies show that the proposed method enhances the prediction performance of the existing methods. A real data analysis shows that the inclusion of subject-specific frailties helps to improve prediction of the DNN based Cox model (DNN-Cox).  ( 2 min )
    An Image-Denoising Framework Fit for Quantum Annealing via QUBO and Restricted Boltzmann Machines. (arXiv:2307.06542v1 [quant-ph])
    We investigate a framework for binary image denoising via restricted Boltzmann machines (RBMs) that introduces a denoising objective in quadratic unconstrained binary optimization (QUBO) form and is well-suited for quantum annealing. The denoising objective is attained by balancing the distribution learned by a trained RBM with a penalty term for derivations from the noisy image. We derive the statistically optimal choice of the penalty parameter assuming the target distribution has been well-approximated, and further suggest an empirically supported modification to make the method robust to that idealistic assumption. We also show under additional assumptions that the denoised images attained by our method are, in expectation, strictly closer to the noise-free images than the noisy images are. While we frame the model as an image denoising model, it can be applied to any binary data. As the QUBO formulation is well-suited for implementation on quantum annealers, we test the model on a D-Wave Advantage machine, and also test on data too large for current quantum annealers by approximating QUBO solutions through classical heuristics.  ( 2 min )
    A Deep Learning Method for Comparing Bayesian Hierarchical Models. (arXiv:2301.11873v3 [stat.ML] UPDATED)
    Bayesian model comparison (BMC) offers a principled approach for assessing the relative merits of competing computational models and propagating uncertainty into model selection decisions. However, BMC is often intractable for the popular class of hierarchical models due to their high-dimensional nested parameter structure. To address this intractability, we propose a deep learning method for performing BMC on any set of hierarchical models which can be instantiated as probabilistic programs. Since our method enables amortized inference, it allows efficient re-estimation of posterior model probabilities and fast performance validation prior to any real-data application. In a series of extensive validation studies, we benchmark the performance of our method against the state-of-the-art bridge sampling method and demonstrate excellent amortized inference across all BMC settings. We then showcase our method by comparing four hierarchical evidence accumulation models that have previously been deemed intractable for BMC due to partly implicit likelihoods. In this application, we corroborate evidence for the recently proposed L\'evy flight model of decision-making and show how transfer learning can be leveraged to enhance training efficiency. We provide reproducible code for all analyses and an open-source implementation of our method.  ( 2 min )
    Adapting to Mixing Time in Stochastic Optimization with Markovian Data. (arXiv:2202.04428v3 [cs.LG] UPDATED)
    We consider stochastic optimization problems where data is drawn from a Markov chain. Existing methods for this setting crucially rely on knowing the mixing time of the chain, which in real-world applications is usually unknown. We propose the first optimization method that does not require the knowledge of the mixing time, yet obtains the optimal asymptotic convergence rate when applied to convex problems. We further show that our approach can be extended to: (i) finding stationary points in non-convex optimization with Markovian data, and (ii) obtaining better dependence on the mixing time in temporal difference (TD) learning; in both cases, our method is completely oblivious to the mixing time. Our method relies on a novel combination of multi-level Monte Carlo (MLMC) gradient estimation together with an adaptive learning method.  ( 2 min )
    Learning low-rank latent mesoscale structures in networks. (arXiv:2102.06984v5 [cs.SI] UPDATED)
    It is common to use networks to encode the architecture of interactions between entities in complex systems in the physical, biological, social, and information sciences. To study the large-scale behavior of complex systems, it is useful to examine mesoscale structures in networks as building blocks that influence such behavior. We present a new approach for describing low-rank mesoscale structures in networks, and we illustrate our approach using several synthetic network models and empirical friendship, collaboration, and protein--protein interaction (PPI) networks. We find that these networks possess a relatively small number of `latent motifs' that together can successfully approximate most subgraphs of a network at a fixed mesoscale. We use an algorithm for `network dictionary learning' (NDL), which combines a network-sampling method and nonnegative matrix factorization, to learn the latent motifs of a given network. The ability to encode a network using a set of latent motifs has a wide variety of applications to network-analysis tasks, such as comparison, denoising, and edge inference. Additionally, using a new network denoising and reconstruction (NDR) algorithm, we demonstrate how to denoise a corrupted network by using only the latent motifs that one learns directly from the corrupted network.  ( 3 min )
    Deep Network Approximation: Beyond ReLU to Diverse Activation Functions. (arXiv:2307.06555v1 [cs.LG])
    This paper explores the expressive power of deep neural networks for a diverse range of activation functions. An activation function set $\mathscr{A}$ is defined to encompass the majority of commonly used activation functions, such as $\mathtt{ReLU}$, $\mathtt{LeakyReLU}$, $\mathtt{ReLU}^2$, $\mathtt{ELU}$, $\mathtt{SELU}$, $\mathtt{Softplus}$, $\mathtt{GELU}$, $\mathtt{SiLU}$, $\mathtt{Swish}$, $\mathtt{Mish}$, $\mathtt{Sigmoid}$, $\mathtt{Tanh}$, $\mathtt{Arctan}$, $\mathtt{Softsign}$, $\mathtt{dSiLU}$, and $\mathtt{SRS}$. We demonstrate that for any activation function $\varrho\in \mathscr{A}$, a $\mathtt{ReLU}$ network of width $N$ and depth $L$ can be approximated to arbitrary precision by a $\varrho$-activated network of width $6N$ and depth $2L$ on any bounded set. This finding enables the extension of most approximation results achieved with $\mathtt{ReLU}$ networks to a wide variety of other activation functions, at the cost of slightly larger constants.  ( 2 min )
    A kernel Stein test of goodness of fit for sequential models. (arXiv:2210.10741v3 [stat.ML] UPDATED)
    We propose a goodness-of-fit measure for probability densities modeling observations with varying dimensionality, such as text documents of differing lengths or variable-length sequences. The proposed measure is an instance of the kernel Stein discrepancy (KSD), which has been used to construct goodness-of-fit tests for unnormalized densities. The KSD is defined by its Stein operator: current operators used in testing apply to fixed-dimensional spaces. As our main contribution, we extend the KSD to the variable-dimension setting by identifying appropriate Stein operators, and propose a novel KSD goodness-of-fit test. As with the previous variants, the proposed KSD does not require the density to be normalized, allowing the evaluation of a large class of models. Our test is shown to perform well in practice on discrete sequential data benchmarks.  ( 2 min )
    Tensor Decompositions Meet Control Theory: Learning General Mixtures of Linear Dynamical Systems. (arXiv:2307.06538v1 [cs.LG])
    Recently Chen and Poor initiated the study of learning mixtures of linear dynamical systems. While linear dynamical systems already have wide-ranging applications in modeling time-series data, using mixture models can lead to a better fit or even a richer understanding of underlying subpopulations represented in the data. In this work we give a new approach to learning mixtures of linear dynamical systems that is based on tensor decompositions. As a result, our algorithm succeeds without strong separation conditions on the components, and can be used to compete with the Bayes optimal clustering of the trajectories. Moreover our algorithm works in the challenging partially-observed setting. Our starting point is the simple but powerful observation that the classic Ho-Kalman algorithm is a close relative of modern tensor decomposition methods for learning latent variable models. This gives us a playbook for how to extend it to work with more complicated generative models.  ( 2 min )
    A Novel Bayes' Theorem for Upper Probabilities. (arXiv:2307.06831v1 [stat.ML])
    In their seminal 1990 paper, Wasserman and Kadane establish an upper bound for the Bayes' posterior probability of a measurable set $A$, when the prior lies in a class of probability measures $\mathcal{P}$ and the likelihood is precise. They also give a sufficient condition for such upper bound to hold with equality. In this paper, we introduce a generalization of their result by additionally addressing uncertainty related to the likelihood. We give an upper bound for the posterior probability when both the prior and the likelihood belong to a set of probabilities. Furthermore, we give a sufficient condition for this upper bound to become an equality. This result is interesting on its own, and has the potential of being applied to various fields of engineering (e.g. model predictive control), machine learning, and artificial intelligence.  ( 2 min )
    The complexity of non-stationary reinforcement learning. (arXiv:2307.06877v1 [cs.LG])
    The problem of continual learning in the domain of reinforcement learning, often called non-stationary reinforcement learning, has been identified as an important challenge to the application of reinforcement learning. We prove a worst-case complexity result, which we believe captures this challenge: Modifying the probabilities or the reward of a single state-action pair in a reinforcement learning problem requires an amount of time almost as large as the number of states in order to keep the value function up to date, unless the strong exponential time hypothesis (SETH) is false; SETH is a widely accepted strengthening of the P $\neq$ NP conjecture. Recall that the number of states in current applications of reinforcement learning is typically astronomical. In contrast, we show that just $\textit{adding}$ a new state-action pair is considerably easier to implement.  ( 2 min )
    Weighted Averaged Stochastic Gradient Descent: Asymptotic Normality and Optimality. (arXiv:2307.06915v1 [stat.ML])
    Stochastic Gradient Descent (SGD) is one of the simplest and most popular algorithms in modern statistical and machine learning due to its computational and memory efficiency. Various averaging schemes have been proposed to accelerate the convergence of SGD in different settings. In this paper, we explore a general averaging scheme for SGD. Specifically, we establish the asymptotic normality of a broad range of weighted averaged SGD solutions and provide asymptotically valid online inference approaches. Furthermore, we propose an adaptive averaging scheme that exhibits both optimal statistical rate and favorable non-asymptotic convergence, drawing insights from the optimal weight for the linear model in terms of non-asymptotic mean squared error (MSE).  ( 2 min )
    Robust scalable initialization for Bayesian variational inference with multi-modal Laplace approximations. (arXiv:2307.06424v1 [stat.ME])
    For predictive modeling relying on Bayesian inversion, fully independent, or ``mean-field'', Gaussian distributions are often used as approximate probability density functions in variational inference since the number of variational parameters is twice the number of unknown model parameters. The resulting diagonal covariance structure coupled with unimodal behavior can be too restrictive when dealing with highly non-Gaussian behavior, including multimodality. High-fidelity surrogate posteriors in the form of Gaussian mixtures can capture any distribution to an arbitrary degree of accuracy while maintaining some analytical tractability. Variational inference with Gaussian mixtures with full-covariance structures suffers from a quadratic growth in variational parameters with the number of model parameters. Coupled with the existence of multiple local minima due to nonconvex trends in the loss functions often associated with variational inference, these challenges motivate the need for robust initialization procedures to improve the performance and scalability of variational inference with mixture models. In this work, we propose a method for constructing an initial Gaussian mixture model approximation that can be used to warm-start the iterative solvers for variational inference. The procedure begins with an optimization stage in model parameter space in which local gradient-based optimization, globalized through multistart, is used to determine a set of local maxima, which we take to approximate the mixture component centers. Around each mode, a local Gaussian approximation is constructed via the Laplace method. Finally, the mixture weights are determined through constrained least squares regression. Robustness and scalability are demonstrated using synthetic tests. The methodology is applied to an inversion problem in structural dynamics involving unknown viscous damping coefficients.  ( 3 min )
    Energy Discrepancies: A Score-Independent Loss for Energy-Based Models. (arXiv:2307.06431v1 [stat.ML])
    Energy-based models are a simple yet powerful class of probabilistic models, but their widespread adoption has been limited by the computational burden of training them. We propose a novel loss function called Energy Discrepancy (ED) which does not rely on the computation of scores or expensive Markov chain Monte Carlo. We show that ED approaches the explicit score matching and negative log-likelihood loss under different limits, effectively interpolating between both. Consequently, minimum ED estimation overcomes the problem of nearsightedness encountered in score-based estimation methods, while also enjoying theoretical guarantees. Through numerical experiments, we demonstrate that ED learns low-dimensional data distributions faster and more accurately than explicit score matching or contrastive divergence. For high-dimensional image data, we describe how the manifold hypothesis puts limitations on our approach and demonstrate the effectiveness of energy discrepancy by training the energy-based model as a prior of a variational decoder model.  ( 2 min )
    Tackling Combinatorial Distribution Shift: A Matrix Completion Perspective. (arXiv:2307.06457v1 [cs.LG])
    Obtaining rigorous statistical guarantees for generalization under distribution shift remains an open and active research area. We study a setting we call combinatorial distribution shift, where (a) under the test- and training-distributions, the labels $z$ are determined by pairs of features $(x,y)$, (b) the training distribution has coverage of certain marginal distributions over $x$ and $y$ separately, but (c) the test distribution involves examples from a product distribution over $(x,y)$ that is {not} covered by the training distribution. Focusing on the special case where the labels are given by bilinear embeddings into a Hilbert space $H$: $\mathbb{E}[z \mid x,y ]=\langle f_{\star}(x),g_{\star}(y)\rangle_{{H}}$, we aim to extrapolate to a test distribution domain that is $not$ covered in training, i.e., achieving bilinear combinatorial extrapolation. Our setting generalizes a special case of matrix completion from missing-not-at-random data, for which all existing results require the ground-truth matrices to be either exactly low-rank, or to exhibit very sharp spectral cutoffs. In this work, we develop a series of theoretical results that enable bilinear combinatorial extrapolation under gradual spectral decay as observed in typical high-dimensional data, including novel algorithms, generalization guarantees, and linear-algebraic results. A key tool is a novel perturbation bound for the rank-$k$ singular value decomposition approximations between two matrices that depends on the relative spectral gap rather than the absolute spectral gap, a result that may be of broader independent interest.  ( 2 min )
    On Collaboration in Distributed Parameter Estimation with Resource Constraints. (arXiv:2307.06442v1 [cs.LG])
    We study sensor/agent data collection and collaboration policies for parameter estimation, accounting for resource constraints and correlation between observations collected by distinct sensors/agents. Specifically, we consider a group of sensors/agents each samples from different variables of a multivariate Gaussian distribution and has different estimation objectives, and we formulate a sensor/agent's data collection and collaboration policy design problem as a Fisher information maximization (or Cramer-Rao bound minimization) problem. When the knowledge of correlation between variables is available, we analytically identify two particular scenarios: (1) where the knowledge of the correlation between samples cannot be leveraged for collaborative estimation purposes and (2) where the optimal data collection policy involves investing scarce resources to collaboratively sample and transfer information that is not of immediate interest and whose statistics are already known, with the sole goal of increasing the confidence on the estimate of the parameter of interest. When the knowledge of certain correlation is unavailable but collaboration may still be worthwhile, we propose novel ways to apply multi-armed bandit algorithms to learn the optimal data collection and collaboration policy in our distributed parameter estimation problem and demonstrate that the proposed algorithms, DOUBLE-F, DOUBLE-Z, UCB-F, UCB-Z, are effective through simulations.  ( 2 min )
    Testing Sparsity Assumptions in Bayesian Networks. (arXiv:2307.06406v1 [stat.ML])
    Bayesian network (BN) structure discovery algorithms typically either make assumptions about the sparsity of the true underlying network, or are limited by computational constraints to networks with a small number of variables. While these sparsity assumptions can take various forms, frequently the assumptions focus on an upper bound for the maximum in-degree of the underlying graph $\nabla_G$. Theorem 2 in Duttweiler et. al. (2023) demonstrates that the largest eigenvalue of the normalized inverse covariance matrix ($\Omega$) of a linear BN is a lower bound for $\nabla_G$. Building on this result, this paper provides the asymptotic properties of, and a debiasing procedure for, the sample eigenvalues of $\Omega$, leading to a hypothesis test that may be used to determine if the BN has max in-degree greater than 1. A linear BN structure discovery workflow is suggested in which the investigator uses this hypothesis test to aid in selecting an appropriate structure discovery algorithm. The hypothesis test performance is evaluated through simulations and the workflow is demonstrated on data from a human psoriasis study.  ( 2 min )

  • Open

    NPC Steven acknowledged me finally!! 🤯 ChatGPT driven agents in Unreal Engine - update 3
    submitted by /u/Chance_Confection_37 [link] [comments]  ( 8 min )
    What comes first, AI gf or AI bf?
    Was recently hearing an interview with George Hotz and the question came up, what will be invented first: an AI boyfriend or an AI girlfriend? Obviously he had some opinions, would be curious to hear what others have to say. submitted by /u/geepytee [link] [comments]  ( 8 min )
    Training an AI Copywriter
    I want to train an ai bot to be my copywriting sidekick, so it would help me write stuff in the voice, tone and format that my predecessor used. For this, I would need to feed it our entire webpage, some voice&tone norms, presentations and so on. Could you guys pls help me on how to set this up? I have an OpenAI API key, and they did make gpt4 available to use recently soo.. This should be doable right? Thanks boys submitted by /u/Jacobo_csgo [link] [comments]  ( 8 min )
    “Low-Resource” Text Classification: A Parameter-Free Classification Method with Compressors
    submitted by /u/IngloriousBastion [link] [comments]  ( 8 min )
    Apple is like the quiet guy in the corner watching a bar fight with the big 3; you know he is gonna do something REALLY bad ass, but what?
    Google, OpenAI, and Meta (Facebook or whatever) have been having a free for all, trying to topple each with GPT5, Llama, and Bard. However, Apple has been REALLY quiet on the issue with A.I. , and focused on the release of this overpriced monstrosity, the Apple Vision Pro. It's a really big gamble with the $3500 price tag, but so was the iPhone many moons ago, and now everyone will dump their credit and their bank account for the latest iPhone Version Doodaad. I believe Apple is sidestepping this brawl to focus on perfecting augmented reality. This will ultimately unite the hardware with AR applications, and eventually lead to AI applications being embedded into the Vision Pro bundle. In short, while the Big 3 are playing, "King of the AI Mountain', by pummeling each other to the ground…  ( 9 min )
    Where and how can I best train an Ai to really learn my art style?
    A lot of the ai tools I use just doesn’t quite get what I’m asking it to do, and I’m not sure if I’m even able to train my own considering it’s a borg of everyone else’s work etc. I guess what I’m asking is if there is any tool out there that’s stand alone and not as watered down as a lot of the ones that’s available (most of the time for free)? I have a bunch of images I can feed it, along with examples. If anyone has any tips, suggestions, or work-arounds, please let me know! Much appreciated. submitted by /u/Maelasae [link] [comments]  ( 8 min )
    What is xAI and Why Did Elon Musk Launch It? 2023
    submitted by /u/__boiyah [link] [comments]  ( 8 min )
    AI Art Creator for Animations
    Are there any free art creators yet for doing simple animations? or does anyone have a link to how one would try to do this. TYIA. submitted by /u/Walfy07 [link] [comments]  ( 8 min )
    Is there a way to automatically enter free competitions?
    Is this possible and if so how would I go about this? Thanks for any hell submitted by /u/captainofthememeteam [link] [comments]  ( 8 min )
    China moves to support generative AI, regulate applications
    China's internet watchdog and several other authorities, including the National Development and Reform Commission and the Ministry of Science and Technology, have jointly issued an interim regulation on the management of generative artificial intelligence (AI) services. The regulation, published on the website of the Cyberspace Administration of China (CAC) on Thursday, will go into effect on Aug. 15. submitted by /u/Tiger_Claw_1 [link] [comments]  ( 8 min )
    Looking to get into AI Research
    Hi i am a highschool student in the last year, i am looking to get into ai research with potentially starting off my own research in fututre What kind of further studies should i get into in college for scope of getting a phd It would be really helpful to know what degree's should i pursue in order to start my own research at a point in time (Pardon my poor english.) submitted by /u/ShreeyanxRaina [link] [comments]  ( 8 min )
    AI Won’t Really Kill Us All, Will It? - The Atlantic (transcript and podcast)
    submitted by /u/RADICCHI0 [link] [comments]  ( 8 min )
    How do people actually make money using AI?
    I’ve been seeing a lot of posts regarding people making money off chat, GPT and other software’s. Is it even industry worth getting in to? submitted by /u/Hititfromtheback6969 [link] [comments]  ( 8 min )
    One-Minute Daily AI News 7/12/2023
    Anthropic, the AI startup co-founded by ex-OpenAI execs, today announced the release of a new text-generating AI model, Claude 2. The successor to Anthropic’s first commercial model, Claude 2 is available in beta starting today in the U.S. and U.K. both on the web and via a paid API.[1] Elon Musk has launched an AI company to challenge ChatGPT creator OpenAI, which the billionaire tech mogul has accused of being “woke”. On Wednesday, xAI said the goal of the new company would be to “understand the true nature of the universe”.[2] Chip designer Nvidia will invest $50 million to speed up training of Recursion’s artificial intelligence models for drug discovery, the companies said on Wednesday, sending the biotech firm’s shares surging about 83%.[3] For decades, morning weather reports have relied on the same kinds of conventional models. Now, weather forecasting is poised to join the ranks of industries revolutionized by artificial intelligence.A pair of papers, published Wednesday in the scientific journal Nature, touts the potential of two new AI forecasting approaches — systems that could yield faster and more accurate results than traditional models, researchers say.[4] Sources: [1] https://techcrunch.com/2023/07/11/anthropic-releases-claude-2-the-second-generation-of-its-ai-chatbot/ [2] https://www.aljazeera.com/economy/2023/7/13/musk-launches-artificial-intelligence-rival-to-chatgpts-openai [3] https://www.reuters.com/technology/nvidia-invests-50-mln-recursion-train-ai-models-drug-discovery-2023-07-12/ [4] https://www.scientificamerican.com/article/climate-change-could-stump-ai-weather-prediction/ submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )
    Best way to start learning?
    Where is the best place/way to start learning openai/ai? are there good tutorials? learning about how to train a model? Would something else be better to start with? submitted by /u/jeffsmith202 [link] [comments]  ( 8 min )
    Project StyleScribble - generate text with your writing voice
    Wanted to share my new AI project: StyleScribble is an AI-powered web tool designed to assist content creators in generating text using their own unique writing voices. By leveraging the power of artificial intelligence, this tool revolutionizes the content creation process and empowers users to effortlessly produce high-quality written content. ​ https://preview.redd.it/4670phrcjmbb1.png?width=448&format=png&auto=webp&s=bb93bf358bf917e49668262a7e0a2ee7a2aa5ba2 Link to demo: https://huggingface.co/spaces/daniellefranca96/styles-scribble-demo Link to sign in on waitlist: https://stylescribble.fly.dev submitted by /u/katerinaptrv12 [link] [comments]  ( 8 min )
    Best FREE Chrome Extension for reusing prompts other than PromptDrive.ai?
    This is the best I can find so far but before I start investing a lot of time into uploading prompts to this tool, I wanted to make sure this was the best on the market. AIPRM is great for pre-done prompts but limits private prompts. Appreciate any help! submitted by /u/Life-Hacking [link] [comments]  ( 8 min )
    Best tool/system for keeping up and organizing AI tools... Notion, Clickup or...?
    I'm sure this comes down to preference but before I start spending a ton of time building this out, I wanted some feedback on what others have found that worked best? Clickup Example: https://doc.clickup.com/37456139/d/h/13q28b-364/176f834177eb5cb Notion Example: https://enchanting-trader-463.notion.site/AI-Database-f917ca2e609b45478fe7bc2c8d544877 Long time Evernote & G docs user but neither of these is cutting it. submitted by /u/Life-Hacking [link] [comments]  ( 8 min )
  • Open

    [P] Colab - Generate JSON Dataset and Evaluate LLMs
    Here's a free colab notebook that I've been playing around with to generate JSON datasets from PDFs for fine-tuning LLMs and evaluate outputs/prompts for Toxicity, Bias, Quality etc. Colab: https://colab.research.google.com/drive/1KCn1HIeD3fQy8ecT74yHa3xgJZvdNvqL?usp=sharing GitHub Repo: https://github.com/kw2828/guardrail-ml submitted by /u/Educational_Grass_38 [link] [comments]  ( 8 min )
    [D] Looking to enroll in a MS in AI/ML
    I’m looking into getting my MS in ML soon and I’m not sure if the market is good, some friends and family are telling me its too saturated and you won’t find a job and others are telling me you still need leetcode to get a job and others tell me the job market is extremely competitive and you likely wont get a job. Im worried that im making a mistake. Here are some facts about me: 1. I currently am working in a company where I have 5 rotations every 9 months, 3 of them which will be AI/ML related, which means I’ll definitely have at least 3 projects in hand on a global scale (company is global) By the time im done with the MS, I’ll probably already have 2-3 years of hands on experience. Thats about it. Should I be worried? Please advise me, Thank you submitted by /u/KManYuksi [link] [comments]  ( 9 min )
    [D] ICCV Reviews are NOT out
    zzz submitted by /u/Towzeur [link] [comments]  ( 8 min )
    [D] Ray vs. AWS Batch for Distributed Training
    Hello all, In our organization, we are currently using Metaflow as our managed training infrastructure and leveraging the `@batch` decorator for compute. Using Batch, we also have access to multi-node parallel jobs (`@parallel` decorator) for distributed training and we've used it to great effect for fine-tuning some LLMs. We are now thinking of adopting Ray Train since it seems to be very popular nowadays and is gaining lots of traction. Wondering how Ray Train compares to Metaflow (AWS Batch) and what the pros/cons are for both, particularly in the context of scalable training of models. Please kindly share any insights. Thanks in advance! submitted by /u/rirhun [link] [comments]  ( 8 min )
    Is the following an valid way to combine models? [D]
    I am a physician conducting research on intensive care data from many patients. I have full ethical approval, this is just exploratory, nobody will be treated based on my results, and I have no statistician. I have a question in principal, rather than looking for a specific solution. I have trained and tuned three models on my (very imperfect) data. They make binary predictions about the likely success of a treatment. I have developed a logistic regression model, a gaussian naive bayes model, and a c5.0 decision tree model. Each is imperfect in its own way. AUC for each is 0.72-0.79. I am wondering if I could ask each model to make predictions on a set of test data, and let the models 'vote'. For example, if 2 of 3 models agree, then I create a 'majority opinion' column on my data and go with that result. I figure it might weed out some weakness. Is this a 'valid' way to do this, or is it pure nonsense in the world of machine learning? submitted by /u/e05bf027 [link] [comments]  ( 9 min )
    [D] Text analysis alternative to LIWC?
    I'm working on a social science project and have a lot of text data that I would like to analyse between certain groups. Most of the papers that I've read on this use LIWC, but I unfortunately don't have access to that. An alternative I've found was the python package empath but it doesn't account for the use of pronouns which I know is going to be an important feature here. Does anyone know of a better alternative? Thanks a lot! submitted by /u/PlainJane049 [link] [comments]  ( 8 min )
    [R] Nvidia RTX 4090 ML benchmarks. Under QEMU/KVM. Image + Transformers. FP16/FP32.
    Motivation: I am running a proxmox instance for tinkering with devops stuff, k8s, ci and so on. And wanted to also have the ability to run ML workloads specifically any kind of ClosedAI open-sourced alternatives. Like Guanaco, WizardLM, Starcoder, Codegen. As well as having some kind of pre-prod environment for ML deployments. Environment: All the tests were run within a virtual machine(qemu) which is run using proxmox 7.4-3. CPU: Intel Xeon 2696v4 2.2Ghz Storage: Samsung SSD 870 EVO OS:Linux gpu-node 5.15.0-76-generic #83-Ubuntu SMP Thu Jun 15 19:16:32 UTC 2023 x86\_64 x86\_64 x86\_64 GNU/LinuxDISTRIB\_DESCRIPTION="Ubuntu 22.04.2 LTS" Python 3.10.12 (main, Jul 5 2023, 18:54:27) \[GCC 11.2.0\] on linux Torch version: Version: 2.1.0.dev20230709+cu121 import torch;torch.version.cuda -> …  ( 9 min )
    [D] Stats package (JMP) vs machine learning for prediction
    CIO wants to predict "things" about mortgages using "technology". (There have been a few specifics and possible factors suggested, like if a loan was returned to us by the bank we sold it to). IT manager is super gung ho about using machine learning and AI. I'm not an experienced statistician, but I used JMP when I worked as a process engineer (yeah, squiggly career), and was like, why can't we just do this with JMP? I can figure out which factors are significant and which are most strongly correlated to what they want answers to. Is there a valid reason to go the machine learning route, or is it just a hot topic right now? No one seems willing to point out any flaws with that method and that makes me uncomfortable. submitted by /u/clarielz [link] [comments]  ( 9 min )
    TokenMonster Ungreedy Subword Tokenizer V4: Enables Models to be 4x Smaller and Whilst Achieving Higher Chr/Token (With Evidence) [P]
    GitHub | Interactive Benchmark | Live Tokenizer TokenMonster is an ungreedy subword tokenizer and vocabulary trainer for Python, Go & Javascript. You can use one of my pretrained vocabularies or generate your own with the included tools. TokenMonster can tokenize text more efficiently than other tokenization methods, even when using a much smaller vocabulary. Here is a size 24000 TokenMonster vocabulary benchmarked against tiktoken cl100k_base (100256) and LLaMa (32000) (link to interactive benchmark): https://preview.redd.it/o16a9tbrurbb1.png?width=1506&format=png&auto=webp&s=66c11d2b8defd634c86756064125b70e8e5cb6d6 Unlike previously versions of TokenMonster, the current version does not compress the text into as few tokens as possible to achieve the high chr/token. TokenMonster V4 of…  ( 9 min )
    [P] INT-FP-QSim: Mixed Precision and Formats For Large Language Models and Vision Transformers
    Hello, We've released INT-FP-QSim: https://github.com/lightmatter-ai/INT-FP-QSim, a flexible simulator that allows running different LLMs and vision transformers at different formats (INT, FP) and precision (8-bit, 4-bit). The repository also has scripts for running simple evaluation with Stable Diffusion, Maskformer, Graphormer, ImageBind and CodeGen (with and without the simulator). Since there are users who may not have a good starting point for running different models, we hope that the example scripts provided in this repository will help there as well. submitted by /u/IllustriousSir_007 [link] [comments]  ( 8 min )
    [P] Federated Learning framework
    Hello everyone 👋 We have launched a new project called MetisFL, a federated learning framework that allows developers to federate their machine learning workflows and train their models across distributed datasets without having to collect the data in a centralized location. https://github.com/NevronAI/metisfl Every feedback or even a simple star, would be highly appreciated! submitted by /u/No-Literature-1930 [link] [comments]  ( 8 min )
    [P] Curated Transformers: Library of PyTorch LLM and other transformers, with common components
    https://github.com/explosion/curated-transformers/ Curated Transformers is a new library of PyTorch LLM and other transformer architectures, implemented using common components. The library makes a different trade-off from Huggingface Transformers, which uses wholly separate implementations for each architecture. The advantage of HF's approach is that they can adopt new architectures very quickly, by taking the academic implementation more or less verbatim. Curated Transformers won't be able to add architectures as quickly, but the implementations share common elements, making it easier to mix and match components and see how architectures differ. We'll also be able to share bug fixes and improvements between different models. Check it out if you're interested in mixing and matching parts of different models, or if you just want a lighter-weight, pure-PyTorch experience, without any intervening abstractions. Supported encoder-only models: ALBERT BERT CamemBERT RoBERTa XLM-RoBERTa Supported decoder-only models: GPT-NeoX LLaMA Falcon Generator wrappers: Dolly v2 Falcon All models can be loaded from Huggingface Hub. spaCy integration for curated transformers is provided by the spacy-curated-transformers package. submitted by /u/syllogism_ [link] [comments]  ( 9 min )
    [R] PyTorch DeepLab/MMSegmentation Tutorial Resources
    I've hit a bit of a roadblock. I'm struggling to find tutorials with PyTorch code for Semantic Segmentation. I initially used the MMSegmentation tutorial on its GitHub, but that didn't work as there were a number of missing files. Any help would be massively appreciated as I'm really struggling. submitted by /u/Charako [link] [comments]  ( 8 min )
    [Research] incorporating additional information into the latent space of CNN's?
    Context: The input is a 3D MRI image, aswell as a position and orientation both in 3D. Output is a 3D image Most likely candidate for architecture is 3D-Unet In general we are trying to predict how a brain will be stimulated based a magnet producing an electric field. The position and orientation I am talking about is the position and orientation of the magnet. If you are interested this paper works on the same problem: doi.org/10.1371/journal.pone.0254588 Question: How do we incoorperate multiple different input types (image + vectors) in CNN's We know the position and orientation information is extreemly relevant to the output, meaning we know it can be incoorperated directly into the latentspace, what I dont understand is how to incoorperate the two vectors into the latentspace in order to keep the feature dimentions. I can think of two options: 1) add additional feature layers to the latentspace in which every layer has one value as constant accross the entire layer, that way if we have 2 vectors and a angle we would add 7 feature layers to the latent space. Here I am not sure if the neural network can work with these since they only really have any meaning if you put them together into a vector 2) completely flatten the latent space add the values and then reshape them back into form. Here i doubt this is a good idea since we are destroying all localization information from the features Previous approach: One solution we have thought about is feeding the neural net the mri image as if it was taken from the position, orientation and angle that way incoorperating the information indirectly. This solution preduces the most accurate results in previous papers. Unfortunatly that solution leads to a too long preprocessing time and since we need the inference to be real time it is not a valid solution. submitted by /u/ClumsyClassifier [link] [comments]  ( 9 min )
    [P] Journal Hub - literature discussion platform project
    [ Removed by Reddit on account of violating the content policy. ] submitted by /u/iokarkan [link] [comments]  ( 8 min )
    [R] Deep Learning Models for Forest Canopy Estimation
    I need to either build a tool or use a service which can convert satellite image (Input) of trees, and then output an estimate of its forest canopy. Has anyone ever used or know of such deep learning models? Thanks. submitted by /u/Quantumercifier [link] [comments]  ( 8 min )
    [D] Overview of recent developments in audio-generative models (TTS & TTM)
    Hi everyone, I wrote a blog post on the advancements in Generative AI for audio, focusing on Text-to-Speech (TTS) and Text-to-Music (TTM) models. I survey how techniques from LLMs have been adapted for audio generation, leading to significant improvements. The article takes a technical look at some of the recent models in this space, including MusicLM, VALL-E, and also explains the key ideas behind Neural Audio Codecs and Residual Vector Quantization techniques, which have become essential in nearly all text-to-audio models. If you have an interest in the current state and future of Generative AI for audio, I hope you will find this informative! I drop the article link in the comments below 👇👇👇 I appreciate any feedback or thoughts you may have! submitted by /u/mrx-ai [link] [comments]  ( 9 min )
    [P] Fast debugging of audio machine learning models
    I recently have improved a library I created to have support for finding issues in audio data using audio embeddings. ​ A mix of automatically detecting problematic data clusters and reviewing them visually can help speed up model debugging. The principle behind this is as follows: Step 1: Compute audio embeddings for the raw data 🎶This makes the data explorable by audio similarity. Depending on the properties the model captures, you will get different notions of similarity. E.g., if you use a model for speaker identification, you will probably order your data according to the speaker's voice properties. Step 2: Identify problematic data slices using clustering 🔍One strategy to get explicit suggestions for problematic data slices is clustering the samples based on audio embeddings. You can then compute your evaluation metrics for the identified clusters and search for clusters that, compared to the overall accuracy, show a significant accuracy drop. Step 3: Review the supposed issues visually 👀Especially when using only unstructured data, the results of your analysis will not be readily interpretable. However, reviewing the problematic clusters by listening and visualizing (e.g., drawing spectrograms) will help you filter out actual model and data issues. I also created a Medium Post and an Example Notebook for this. Also check out this Interactive Result Visualization on Huggingface. ​ ​ ​ submitted by /u/OkResearch6289 [link] [comments]  ( 9 min )
    [D] Overfit ML model
    Hello this is my first contribution to the sub, I have a dataset for a classification problem and it seems that all the models I have performed are overfiting (f1 score close to 1). I cant wrap my head around how to solve it without removing important values. submitted by /u/Archyve [link] [comments]  ( 8 min )
    [D] how to accelerate ViT models more faster
    I have conducted experiments and examples on accelerating ViT (Vision Transformer) using methods such as TensorRT, FasterTransformer, and xFormers. The experiments were conducted using a single A100 as a baseline. - https://github.com/bnabis93/vision-language-examples/tree/main/acceleration In xFormers, I tried applying sparse attention and memory-efficient attention to ViT, but there was an issue where the speed actually decreased. Therefore, I excluded those results. Generally, just performing TensorRT conversion significantly improves latency. In the case of faster transformer, optimized kernels are not provided in fp32, so it is not as effective as expected. However, after quantizing to fp16 and obtaining the results, it was more effective than simply performing TensorRT conversion. Are there any other methods for accelerating ViT? Using OpenAI's Triton is one option that comes to mind. If you have any other methods worth trying, I would appreciate it if you could let me know. submitted by /u/bono-93 [link] [comments]  ( 9 min )
    [P] LLMs, NLP: Building a Model to Generate and Analyze Claim Narratives
    Hi all. I'm diving into the world of Natural Language Processing (NLP) and Large Language Models (LLMs), and I could really use some assistance with my project. Here's what I'm aiming to achieve: Imagine having a dataset with various features such as location, type of claim, and claim payment. Additionally, there's a column dedicated to narratives, which provide descriptions of each claim. For instance, a narrative could be "I was crossing the street and was hit by a car, which broke my leg." My goals are twofold: i) Develop a model capable of taking the three columns (type of claim, claim payment, and location) as input and generating a narrative. ii) Create another model that does the reverse: input a narrative and extract relevant information like the type of claim and the location. I would greatly appreciate any advice, or resources you can provide. Thank you in advance! submitted by /u/therobot20 [link] [comments]  ( 9 min )
    [D]Encoder only vs encoder-decoder vs decoder only
    I understand that encoder only models (like BERT etc) are mainly for learning representations of words taking context on both sides. What I’m confused about is why you would need decoder only vs encoder-decoder models. GPT and Bloom are decoder only while I think T5 is enc-dec, I’m not sure why you would use one vs the other. Intuitively enc-dec model has more parameters and should be better at tasks where you have both complex text input and output, like say translation or summarization. Any ideas why decoder only models are desirable and also why they seem to work so well? Thanks in advance. submitted by /u/Western-Image7125 [link] [comments]  ( 8 min )
  • Open

    "Reinforcement Learning in Newcomblike Environments", Bell et al 2021
    submitted by /u/gwern [link] [comments]  ( 8 min )
    REINFORCE with Baseline not Learning
    I have implemented REINFORCE using PyTorch and am testing it on the CartPole environment. My implementation allows for an optional baseline to be applied. At present, the baseline used is simply the mean of the returns earned during a trajectory. The agent will learn a good policy when I DO NOT use a baseline, but when I apply the baseline, the agent fails to learn anything. I cannot figure out why. I notice that the loss is always very close to zero when using the baseline, but it seems like that should be expected. When the network weights are still random, most of the actions will have a probability that is near 0.5, and thus a log probability that is close to log(0.5) ~=~ -0.7. The returns for this environment are symmetric about the mean, so the weighted sum of the centered return…  ( 9 min )
    Beating DeepMind’s Game: Alchemy
    submitted by /u/Ok_Introduction9109 [link] [comments]  ( 8 min )
    Is offline-to-online RL some kind of Transfer-RL?
    I read some papers about offline-to-online (O2O) RL and transfer-RL. And I was trying to explore the O2O-transfer RL. Where we have data for one environment and we could pre-train a model offline then improve it online in another environment. If the MDP structure is the same for the target and source environments while transferring. What is the exact difference between O2O-RL and transfer-RL under this assumption? Essentially they are both trying to adapt the distribution drift, isn’t it? submitted by /u/Blasphemer666 [link] [comments]  ( 8 min )
    PPO agent completing Street Fighter III on our RL Platform, it consistently outperformed when using deterministic actions instead of sampling them proportionally to their probability. Why in your opinion? (see comment for details)
    submitted by /u/DIAMBRA_AIArena [link] [comments]  ( 8 min )
  • Open

    Symbol tuning improves in-context learning in language models
    Posted by Jerry Wei, Student Researcher, and Denny Zhou, Principal Scientist, Google Research A key feature of human intelligence is that humans can learn to perform new tasks by reasoning using only a few examples. Scaling up language models has unlocked a range of new applications and paradigms in machine learning, including the ability to perform challenging reasoning tasks via in-context learning. Language models, however, are still sensitive to the way that prompts are given, indicating that they are not reasoning in a robust manner. For instance, language models often require heavy prompt engineering or phrasing tasks as instructions, and they exhibit unexpected behaviors such as performance on tasks being unaffected even when shown incorrect labels. In “Symbol tuning improves…  ( 93 min )
  • Open

    Data modeling techniques in modern data warehouse
    Hello, data enthusiast! In this article let’s discuss “Data Modelling” right from the traditional and classical ways and aligning to today’s digital way, especially for analytics and advanced analytics. Yes! Of course, last 40+ years we all worked for OLTP, and followed by we started focusing on OLAP. After cloud ear come into the picture… Read More »Data modeling techniques in modern data warehouse The post Data modeling techniques in modern data warehouse appeared first on Data Science Central.  ( 26 min )
    A Detailed Guide for Data Handling Techniques in Data Science
    Image Source: Author Introduction Data Engineers and Data Scientists need data for their Day-to-Day job. Of course, It could be for Data Analytics, Data Prediction, Data Mining, Building Machine Learning Models Etc., All these are taken care of by the respective team members and they need to work towards identifying relevant data sources, and associated with… Read More »A Detailed Guide for Data Handling Techniques in Data Science The post A Detailed Guide for Data Handling Techniques in Data Science appeared first on Data Science Central.  ( 26 min )
  • Open

    training convolutional neural network with MATLAB: image recognition AI.
    submitted by /u/Character_Ad_1385 [link] [comments]  ( 8 min )
  • Open

    Effectively solve distributed training convergence issues with Amazon SageMaker Hyperband Automatic Model Tuning
    Recent years have shown amazing growth in deep learning neural networks (DNNs). This growth can be seen in more accurate models and even opening new possibilities with generative AI: large language models (LLMs) that synthesize natural language, text-to-image generators, and more. These increased capabilities of DNNs come with the cost of having massive models that […]  ( 11 min )
  • Open

    AI-Fueled Productivity: Generative AI Opens New Era of Efficiency Across Industries
    A watershed moment on Nov. 22, 2022, was mostly virtual, yet it shook the foundations of nearly every industry on the planet. On that day, OpenAI released ChatGPT, the most advanced artificial intelligence chatbot ever developed. This set off demand for generative AI applications that help businesses become more efficient, from providing consumers with answers Read article >  ( 11 min )
    Full-Scale Gaming: ‘Dragon’s Dogma: Dark Arisen’ Comes to GeForce NOW
    Arise, members! Capcom’s legendary role-playing game Dragon’s Dogma: Dark Arisen joins the GeForce NOW library today. The RPG and THQ Nordic’s Jagged Alliance 3 are newly supported on GeForce NOW, playable on nearly any device. From Dusk Till Pawn Become the Arisen and take up the challenge in Capcom’s critically acclaimed RPG. Set in a Read article >  ( 5 min )
  • Open

    Making sense of all things data
    Abel Sanchez helps industries and executives shift their operations in order to make sense of their data and use it to help their bottom lines.  ( 8 min )
    How an “AI-tocracy” emerges
    In China, the use of AI-driven facial recognition helps the regime repress dissent while enhancing the technology, researchers report.  ( 9 min )

  • Open

    [D] Does the university/program for masters for ML/AI roles matter?
    I am curious about this. I have an undergrad in CS from a large public university from the states and 2 yoe as SWE from India. Not now, but few more years down the line when I have like 4-5 yoe as SWE, I would want to get into ML/AI roles. I don't want to waste any more time getting into a full-time program and waste 1-2 more years of my 20s (I am 25 already) so I was looking for online programs from a >= decent university in North America. The thing about me is that I learn best by self-studying. For me personally, university prestige doesn't do anything for me cos they all read from ppts and use same books. I took a summer at Cornell and ended up studying on my own. So, for me to spend so much money or take out another 1-2 years for a full-time masters doesn't make sense. I just want get a masters that is cheap in terms of cost, takes comparatively less time, PT so that I can work at the same time and do powerlifting and other hobbies and after completion can make me eligible for non-research ML/AI roles. In all, I am only doing this to show on my resume to HR that "here is a guy who you won't need to spend too much time on analyzing cos he has done a masters/ some more additional math/stat courses and an institution has certified because they got paid to do so". submitted by /u/ItsWasntMyFault [link] [comments]  ( 9 min )
    [D] AI Text to Image Generation Project
    I am a student of computer science. I have my final year project whose deadline is in a few week. I have to create AI text to image generator trained on a custom data set. The user should provide it a prompt and it should generate an image based on the prompt given to it. Infact it will be limited as it will be trained on a limited cutom data set. I have searched throughout the internet but I am unable to find any helpful material, code or any resource... And those that I found I am unable to understand them because of my less expertise. Is there anyone who could help me ? submitted by /u/mufeezahmad [link] [comments]  ( 8 min )
    [D] How to do Semi Supervised Learning or Learning with no labels?
    Would appreciate some direction and pointers in this research area. Some possible options could be to use representational learning (probably using auto encoder, or InfoNCE/Contrastive loss). Is there any other good way to solve the problem of semi supervised learning where we have partial labels for the data and want our model to do well on unlabelled instances as well? submitted by /u/Rohit901 [link] [comments]  ( 8 min )
    [P] Free and Fast LLM Finetuning
    Here's a colab notebook showing a new interface for LLM finetuning that I've been playing around with. Curious if folks here have feedback. Colab: https://colab.research.google.com/drive/1QMeGzR9FnhNJJFmcHtm9RhFP3vrwIkFn Docs: https://www.lamini.ai/blog/free-fast-and-furious-finetuning Github Repo: https://github.com/lamini-ai/lamini LLM fine tuning includes several advanced optimizations: Chinchilla recipe smaller models pretrained on data increases inference speed Instruction fine tuning training on a small high quality set of instructions unlocks the knowledge learned during foundation model training. Latency constrained batching achieves high utilization under load during token generation Containerized SLURM combines fast scheduling of SLURM with LLM containers Mixed precision training uses matrix operations for training There are so many low hanging fruits in LLM tuning, steering, and alignment. We are just getting started on this for enterprise and open source. For this reason I disagree with Sam Altman that the age of bigger models is over. We are still leaving orders of magnitude on the table, e.g. by not including optimizations like sparsity in these models. References for inspiration: [1] - https://arxiv.org/abs/2203.15556 [2] - https://arxiv.org/abs/1910.10683 [3] - https://www.usenix.org/system/files/osdi22-yu.pdf [4] - https://www.schedmd.com/ [5] - https://arxiv.org/abs/1710.03740 submitted by /u/gdiamos [link] [comments]  ( 9 min )
    Wind speed prediction -appreciate opinion [P]
    Thank you for reading this post. As a fancy kitesurfing I’m trying to predict the wind speed +12h. However, so far my succes is limited. How can I improve my prediction? Data So far, I’m using windspeed&direction, temperature and humidity 10 min interval for past 2 year for 6 locations. Features Day of the year and hour of the day. In addition I have aggregated the 10 min data to 1 hour and calculated the mean, min, max and std. All the data then was made into 18 lags. Models Linear regression, kneigbor an random Forrest. However, only random forest was slightly better then the liniear regression. What type of features and models would you recommend? Cheers submitted by /u/Any-Description3824 [link] [comments]  ( 8 min )
    [D] NeurIPS 2023 reviews release time?
    Hey guys, I was wondering if y’all know when NeurIPS reviews for this year would be released? I know the response period starts on August 4, but do we get to see reviews before? Are they all released at once, or gradually as reviewers finish them? Thanks! submitted by /u/Icy_Background_4524 [link] [comments]  ( 8 min )
    [R] detrex: A Strong Benchmark for Detection Transformers
    In October of last year, we open-sourced detrex as the first unified research platform focusing on the DETR-based algorithms. After several version updates and a significant number of experiments, we conducted a detailed benchmarking of the DETR-based models supported by detrex. Our paper has already been preprinted on ArXiv: https://arxiv.org/abs/2306.07265 And our project repo is here: https://github.com/IDEA-Research/detrex The main characteristics of detrex can be summarized as follows: Fully utilizing LazyConfig as the configuration system, which is highly flexible, convenient, and easily modifiable. Reproducing results on mainstream algorithms surpassing the original implementations by more than 0.2AP - 1.1AP. Supports lots of SoTA backbone including EVA, InternImage, FocalNet, Swin-T, etc. Each algorithm is implemented as an independent project, ensuring no mutual interference between algorithms. Users can confidently implement their own ideas based on detrex. The training code is simple, consisting of approximately 200 lines, and highly customizable, allowing for easy modifications and hacks. Abundant documentation and tutorials are provided. Active response to issues and timely repository updates. ​ Here's some results in our paper, more details can be found in paper: ​ https://preview.redd.it/mdxs4xydblbb1.png?width=1007&format=png&auto=webp&s=a92acc36663365c84be8f86e74c521157fa26202 https://preview.redd.it/f4c3zkvfblbb1.png?width=726&format=png&auto=webp&s=d684591dbd9e16610f3b9b483db1190fe87c7589 https://preview.redd.it/zvcy3tajblbb1.png?width=878&format=png&auto=webp&s=03ce7eb07dbc249a03f8536ce9fad66dbd43841a ​ submitted by /u/Technical-Vast1314 [link] [comments]  ( 9 min )
    [D] Wondering if anyone does HPC or machine learning in Windows?
    With the increased productivity of vscode, does anyone do their dev work for machine learning on a windows server? I have also been getting problems running a jupyter server on ubuntu accessing my gpus. I believe it is easier on a windows server? submitted by /u/Studyr3ddit [link] [comments]  ( 8 min )
    [D] How vital is it to physically attend SIGGRAPH for my early career? Is it worth missing out on hiking the Pyrenees with my girlfriend?
    Bluntly: Is the added benefit of attending SIGGRAPH(instead of merely indulging in the Virtual Access stuff) worth not hiking the Pyrenees with my girlfriend? I am a graduate student specializing in computer graphics and AI stuff. I'm very intent on finding some cool career at the intersection of technology and art so I signed up for SIGGRAPH back in April. I've been scouring information on generative art, NeRFs, and anything on the cutting edge of graphics for a while now so this is obviously the right place. I'd be happy enough indulging in all the talks and demos, but I'm especially interested in the networking aspect and finding a way to give myself a leg up on discovering a promising career. I have been feverishly working to develop my career since I've gotten to grad school, so this…  ( 10 min )
    [D] What is the most efficient version of OpenAI Whisper?
    Hi everyone, I know that there are some different versions of Whisper available in the open-source community (Whisper X, Whisper JAX, etc.), but I'm keeping updated with the best version of the model. Specifically, I'm trying to understand the best Whisper implementation for a task to transcribe a big batch of videos (~10k videos, ~30min long). I'd like to know your thoughts on this. submitted by /u/paulo_zip [link] [comments]  ( 8 min )
    [R]If you have used Lime with bert ....help me out please
    if anyone has succesfully implemented a lime text explaintion with bert model please do share your code with me, i am trying to get lime to work for layout ML, bert and layoutML have similar architecture so i can find something new from your code to apply to mine submitted by /u/Affectionate_Win2460 [link] [comments]  ( 8 min )
    [D] 📚 The Learning Corner (Andrew NG Free Ai Courses Pt. 1)
    📚 The Learning Corner (Andrew NG Free Ai Courses Pt. 1) This is a list of some of the best Ai Free courses by Andrew NG, we will release the second part of the list on our next newsletter installment (link) Generative AI with Large Language Models LangChain: Chat With Your Data LangChain for LLM Application Development How Diffusion Models Work submitted by /u/Yavero [link] [comments]  ( 8 min )
    [D] What's the status of chart-to-text
    I've been looking into solutions to transform charts into descriptions. There were great datasets introduced last year by this paper: https://arxiv.org/pdf/2203.06486.pdf But I haven't found many updates since, especially nothing super convincing. Do any of you know more? submitted by /u/Trick_Brain [link] [comments]  ( 8 min )
    [D] What are some interesting research that combine AI with the physical world
    Most of the talk these days is about LLMs. I am curious about some daring AI projects that try to solve problems from the physical realm that humans won't be able to handle. I am talking about things beyond the human intelligence. For instance, trying to extract formulas for some physical actions, whose analytical formulas would be hard for humans to formulate but machine learning systems can better approximate. I don't know, maybe teaching small machines to fly with very few components, no hardcoded formulas and a ML system that continuously learns from trial and error. Sorry if this sounds dumb. I am just a software engineer with some interest in AI. submitted by /u/besabestin [link] [comments]  ( 8 min )
    [D] LSTM multivariate forecasting
    Hi, I'm currently working on timeseries forecasting in pairs where one timeseries is suspected to cause the other timeseries. I also have the forecast of the causing timeseries and I use them to predict the other timeseries. Note that extrapolation capabilities are required since the forecasts of the first timeseries are not always in the range of the training data. I'm trying to use a simple LSTM model for that task and I tried two ways: training a model using past values of both timeseries and the current value of the first timeseries, and training using only the past and current values of the other timeseries. To my surprise, the forecasts using only the first timeseries values without the values of the timeseries we forecast are better. Does this make sense or am I doing 100% something wrong? When scaling the data do I need to scale values of each timeseries separately or scale them together? Also, what other ways you suggest for forecasting using another timeseries data and existing forecast? submitted by /u/soundgardener666 [link] [comments]  ( 9 min )
    MongoDB for Semantic Search [D]
    I'm quite new to semantic search and looking for an easy-to-use database tool; been looking into MongoDB's semantic search capabilities and following this tutorial. Is anyone using MongoDB for semantic search? If so, what did you think? If not, was there a reason why? Do you have any recommendations that might help me pick the right tool to get started with vector/semantic search? submitted by /u/Important-Sun-3562 [link] [comments]  ( 8 min )
    [R] CorrFL: Correlation-Based Neural Network Architecture for Unavailability Concerns in a Heterogeneous IoT Environment
    An interesting article that tackles a new dimension in Federated Learning (FL) termed oblique federated learning. Link: https://ieeexplore.ieee.org/abstract/document/10132049 submitted by /u/ias18 [link] [comments]  ( 8 min )
    [Discussion] Video Translation Task
    I am working on a project related to Deep Learning translations. We have YouTube videos that we are looking to translate from English to Hindi. But we want the translated audio to sync with the mouth movements of the speaker. How to go about this task? submitted by /u/arhamm40182 [link] [comments]  ( 8 min )
    [D] What do you do with imbalanced target data in a non-binary RF classification problem?
    Hey all, I’m a relatively new data scientist working on my first serious classification project. We’re predicting our target, which is made up of four categories, using the random forest classifier from sklearn. For the target, three categories have about 250 samples and the fourth has about 1200 samples. So far, I’ve just done a stratified shuffle split around the target. Is there anything else that I should make sure to do here to account for the imbalance? Currently, my model just predicts the most common category for almost all of the predictions, which gives it a decent score but makes me feel like this isn’t a useful model. Would appreciate any advice. Thanks! submitted by /u/NDVGuy [link] [comments]  ( 8 min )
    [P] Weights and biases approach -1 or 1
    Hi, ive been desinging my own C code to make neural networks. Mainly for a project and to mess around. It works pretty well but i find that the weights and biases of the finished network are practically all 1, -1 or thereabouts. Is this normal? Again, the network works pretty well, it just seems a bit weird to me. Im using it to predict hand-written characters. Im using sigmoid as my activation function (tried RELU but didnt predict too well, maybe its because I train with a pretty small sample pool?). Weights and biases are randomly initialized with values between -1 and 1 and are capped during training so that they dont exceed -1 or 1. Im thinking this might be the issue. Problem is that if i dont cap them like this, they start to grow and grow the closer you are to the first hidden layer. The last hidden layer would have weights and biases between -1 and 1, but the others started to get bigger and bigger, so i ended up capping it like that to solve it. I believe this is common practice, but maybe the way im capping them is the problem (essentially if a weight or bias exceeds -1 or 1 after having been corrected, i equal it to -1 or 1 respectively).Im not sure what other info i should be providing, as far as i know its a pretty basic neural network, not doing anything too fancy. Thanks in advance. submitted by /u/Automatic-Syrup8490 [link] [comments]  ( 9 min )
    [R] Cross-Entropy is All You Need… Or is It?
    Hey r/machinelearning! I recently wrote an article titled "Cross-Entropy is All You Need… Or is It?" where I discuss extensions to the Cross-Entropy loss to make it better when used in noisy production datasets. I've had great results on my end using this loss, so I wanted to share it with you all and get your opinions and insights! ➡️ Article link ➡️ Code & Colab link Summary: This article introduces the Smooth Generalized Cross-Entropy (SGCE) loss function, a way to address training classification models with noisy labels while still calibrating your model's confidence scores. The article demonstrates the application of SGCE on the task of Named Entity Recognition (NER), but it is applicable to many other tasks. The loss function combines the Cross-Entropy (CE) and Mean Absolute Error…  ( 9 min )
    [D] Are NLP jobs tied to one's own native language?
    I'm considering doing a master's in NLP but concerned if NLP jobs are tied to one's own native language- for example if I'm german native I only get NLP jobs dealing with german language etc. Is this the case or can it be broader? And is it still a wise choice to get a master's in this field? submitted by /u/dauntbooksandyou [link] [comments]  ( 8 min )
    [R] Enhancing Continuous Time Series Modelling with a Latent ODE-LSTM Approach
    https://arxiv.org/abs/2307.05126 submitted by /u/cici118 [link] [comments]  ( 8 min )
    [P] langchain-lite alternative
    Although langchain is an impressive library, I tend to find it is… a little unintuitive, at least for non-trivial examples or examples that don’t have a predefined chains/templates related, it's overly prescriptive; and the various levels of abstraction don't resonate with me related, can be difficult to debug or understand what’s happening in intermediate steps of the chain or what’s it’s actually sending OpenAI So, I built a “langchain-lite” package called llm-workflow https://github.com/shane-kercheval/llm-workflow The value proposition is basically: easily build up a sequence of tasks (e.g. prompt-template -> chat) called a workflow, where the output of one task serves as the input to the next task in the workflow track history; understand what's happening in each of the …  ( 10 min )
    [P] Haven: Deploy Open Source LLMs on Your Own Cloud
    Open source LLMs are a great privacy-preserving alternative to using the ChatGPT API, but deploying them in production is still really hard - especially for people without ML experience and knowledge about ML infrastructure. With Haven, we make it possible to deploy LLMs with just a few lines of code! GitHub: https://github.com/havenhq/haven Website: https://haven.run/ Colab Demo: https://colab.research.google.com/drive/1eGGSisS9Du5-_KcaejY5y9vk9v7EIfba?authuser=2#scrollTo=YYECIKqAGId8 ​ Haven’s main component is the manager, which comes as a container image that can be deployed in your GCP environment. The manager is your central entrypoint and can be used to spin up LLMs on GPUs with just one line of code: from havenpy import Haven client = Haven(":50051", "") worker_id = client.create_inference_worker( model_name="@huggingface/mosaicml/mpt-7b-chat", quantization="float16", gpu_type="T4", gpu_count=2) ​ After spinning up inference workers, you can query them with both a chat completion and a normal completion endpoint, similar to the OpenAI API. res = client.chat_completion(worker_id, messages=[{ "content": "Who would win in a cagefight: Mark Zuckerberg or Elon Musk?", "role": "USER" }], stream=True, temperature=0.8) for r in res: print(r.text, flush=True, end="") ​ Our inference server is powered by vllm to provide the fastest inference possible. In the next weeks, we plan to add support for more cloud providers as well as fine-tuning with a single line of code. We'd love to hear your feedback! submitted by /u/jger227 [link] [comments]  ( 9 min )
  • Open

    A new AI Prompt Wars game was released
    Pretty fun actually. According to the FAQs it uses Stable Diffusion to generate images from participants' prompts and then compares them to determine a winner. submitted by /u/superander [link] [comments]  ( 8 min )
    Survey for generative AI photos and how this will affect Shutterstock and Getty
    I am trying to figure out how people have been reacting to recent changes in generative AI technology and how it will affect the artistic community. It would be greatly appreciated if as many poeple could fill out this survey attached. If you are a photographer or someone who purchases stock photos or likes to make AI images this pertains to you. Will take one minute. Thanks. https://forms.gle/NZJaEVZBfQb1uiaM9 submitted by /u/mattyb24643 [link] [comments]  ( 8 min )
    Are there any free tools that can summarize very long video transcripts?
    All the tools I’ve seen for this can only summarize up to 10ish minutes of dialog in a free version. Looking for an hour plus. I don’t mind if the writing is worse than GPT4 or whatever, would be better than nothing submitted by /u/IndependentFormal8 [link] [comments]  ( 8 min )
    Is there an AI to generate images of ideas of websites?
    I want an AI that can generate images of websites so I can develop then for personal use, is there a tool that can make that? I tried Blue Willow, Dall-e and Canva AI, none of then could generate it. submitted by /u/Luxy_Lockes [link] [comments]  ( 8 min )
    Holy Fuck...This is absolutely incredible. Achieving a better, more flexible result with 19 neurons instead of 100,000 neurons?!?
    submitted by /u/JakeYashen [link] [comments]  ( 8 min )
    ChatGPT Is Losing Users. Is The Artificial Intelligence Craze Over?
    submitted by /u/byteaw [link] [comments]  ( 8 min )
    Can you simulate ambience of a specific soundscape and its surroundings using AI?
    Hello guys, I am working on a song project for which I wanted to make an intro which resembles a port / harbor back in the 16th / 17th / 18th century. Stuff like sound of waves, harbor bells, people working and talking, carrying cargo constantly on ships. And then ultimately the sounds of a ship setting sail and leaving the harbor. ​ At first I thought I'd have to put many different royalty free sounds together to simulate this ambience but I remembered that AI is growing fast and already creating music. And that's where I wanted to ask you guys if you know if it's already possible to simulate a place using an AI and if yes, how? ​ This would really help me out. If you know other subreddits where I could ask, please feel free to suggest them. Thanks and have a good day! submitted by /u/space_dust0 [link] [comments]  ( 8 min )
    One-Minute Daily AI News 7/11/2023
    KPMG plans to spend $2 billion on AI and cloud services through an expanded partnership with Microsoft, aiming to incorporate AI into its core services. This move is in response to a slowdown in advisory deals and a challenging economic environment.[1] Elon Musk will host a conversation about AI with Rep. Ro Khanna (D-Calif.) and Rep. Mike Gallagher (R-Wis.) on Twitter Spaces Wednesday evening, a congressional aide confirmed to The Hill. Gallagher and Khanna have in the past stressed the need for balance in the technology, both expressing optimism about potential benefits while also sharing concerns about the potential dangers it can pose.[2] IT major Wipro announced the launch of the ai360 service and plans to invest $1 billion in AI over the next three years. The move follows Tata Consultancy Services’ announcement to train 25,000 engineers on generative AI tools.[3] IBM is considering the use of artificial intelligence chips that it designed in-house to lower the costs of operating a cloud computing service it made widely available this week, an executive said Tuesday.[4] Sources: [1] https://www.livemint.com/companies/news/kpmg-to-enter-into-a-deal-with-microsoft-spend-2-billion-in-ai-and-cloud-services-11689122323635.html [2] https://thehill.com/homenews/4092145-elon-musk-to-talk-ai-with-bipartisan-pair-of-lawmakers/ [3] https://www.livemint.com/companies/news/wipro-launches-ai360-will-invest-1-billion-into-ai-the-next-three-years-11689132228044.html [4] https://www.reuters.com/technology/ibm-mulls-using-its-own-ai-chip-new-cloud-service-lower-costs-2023-07-11/ submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )
  • Open

    Faster training of Dueling DQN
    Hi everyone, Has anyone worked with training dueling DQN faster or sample efficient? I found papers: Fast RL with slow updates: https://github.com/amazon-science/fast-rl-with-slow-updates Sample efficient deep reinforcement learning via uncertainty estimation: https://openreview.net/forum?id=vrW3tvDfOJQ Sample efficient deep reinforcement learning via episodic backward update: https://arxiv.org/abs/1805.12375 Has anyone worked with any of these? Or do you know any other strategies that have been used to make Dueling DQN learn faster? (I have already experimented with RAINBOW, so wanted to try something on top of it) submitted by /u/mr_formaldehyde [link] [comments]  ( 8 min )
    The inverse reward of the same MDP gives a different result when using value iteration
    Hello, I have an MDP which exists of 2 machine and I need to make decisions on when to do maintenance on the machine depending on the quality of the production. In one situation I created a reward structure based on the production loss of the system. and in the other situation I created a reward structure based on the throughput of the system which is exactly the inverse of the production loss, as you can see in the figure below. So I should suppose that the result of the value iteration algorithm should be exactly the same but it is not. Does anyone know what the reason for that could be or what I can try to do to find out why this happens? Because in value iteration the solution should be optimal, so 2 optimal solutions are not possible. It would be really helpful if someone has an idea about this. ​ https://preview.redd.it/ni23yr5pfjbb1.png?width=1590&format=png&auto=webp&s=5550afef24552b5f516c15aacfc6f0d37af6fdd7 ​ https://preview.redd.it/dundnsrsfjbb1.png?width=453&format=png&auto=webp&s=20d300946254f1d08f3404100a565db27cb5658f submitted by /u/IcyWatch9445 [link] [comments]  ( 9 min )
  • Open

    Generative AI imagines new protein structures
    MIT researchers develop "FrameDiff," a computational tool that uses generative AI to craft new protein structures, with the aim of accelerating drug development and improving gene therapy.  ( 9 min )
  • Open

    Need help for an exam preparation task
    This is the given task. I have never seen a neural network displayed like this. What do the Upwards Arrows in the X1 input Neuron and the upper Neuron in the hidden layer mean? What are the values at the hidden layer (e.g "3.456"), is this the threshold? If yes would I then continue with 1 and multiply 1 with the next weight (8.805 in that case) ? Problem is all my friends don't know it as well and we do not know who else to ask, so we ask reddit.. https://preview.redd.it/xgq4luwnakbb1.png?width=882&format=png&auto=webp&s=d032255a5296a5451eaa7c6e5821dd2981390086 submitted by /u/SoSickBR [link] [comments]  ( 8 min )
    Weights and biases approach -1 or 1
    Hi, ive been desinging my own C code to make neural networks. Mainly for a project and to mess around. It works pretty well but i find that the weights and biases of the finished network are practically all 1, -1 or thereabouts. Is this normal? Again, the network works pretty well, it just seems a bit weird to me. Im using it to predict hand-written characters. Im using sigmoid as my activation function (tried RELU but didnt predict too well, maybe its because I trian with a pretty small sample pool?). Weights and biases are randomly initialized with values between -1 and 1 and are capped during training so that they dont exceed -1 or 1. Im thinking this might be the issue. Problem is that if i dont cap them like this, they start to grow and grow the closer you are to the first hidden layer. The last hidden layer would have weights and biases between -1 and 1, but the others started to get bigger and bigger, so i ended up capping it like that to solve it. I believe this is common practice, but maybe the way im capping them is the problem (essentially if a weight or bias exceeds -1 or 1 after having been corrected, i equal it to -1 or 1 respectively).Im not sure what other info i should be providing, as far as i know its a pretty basic neural network, not doing anything too fancy. Thanks in advance. submitted by /u/Automatic-Syrup8490 [link] [comments]  ( 9 min )
  • Open

    How to mark a language in HTML
    In HTML you can mark the language of a piece of text by putting it inside span tags and setting the lang attribute to a two-letter abbreviation. For example, Allons enfants de la Patrie, Le jour de gloire est arrivé ! indicates that the first two lines of the French national anthem are in […] How to mark a language in HTML first appeared on John D. Cook.  ( 5 min )
  • Open

    Thinking beyond audio: Augmenting headphones for everyday digital interactions
    Because headphones rank among the most popular wearables in the market, we have an exciting opportunity to expand their capabilities through integrating existing sensors with supplementary ones to enable a wide variety of experiences that go beyond traditional audio control. The post Thinking beyond audio: Augmenting headphones for everyday digital interactions appeared first on Microsoft Research.  ( 12 min )
  • Open

    Score! Team NVIDIA Takes Trophy in Recommendation Systems
    A crack NVIDIA team of five machine learning experts spread across four continents won all three tasks in a hotly contested, prestigious competition to build state-of-the-art recommendation systems. The results reflect the group’s savvy applying the NVIDIA AI platform to real-world challenges for these engines of the digital economy. Recommenders serve up trillions of search Read article >  ( 6 min )
    MosaicML Helps AI Users Boost Accuracy, Cut Costs and Save Time
    Startup MosaicML is on a mission to help the AI community improve prediction accuracy, decrease costs and save time by providing tools for easy training and deployment of large AI models. In this episode of NVIDIA’s AI Podcast, host Noah Kravitz speaks with MosaicML CEO and co-founder Naveen Rao about how the company aims to Read article >  ( 5 min )
  • Open

    Isotuning With Applications To Scale-Free Online Learning. (arXiv:2112.14586v2 [cs.LG] UPDATED)
    We extend and combine several tools of the literature to design fast, adaptive, anytime and scale-free online learning algorithms. Scale-free regret bounds must scale linearly with the maximum loss, both toward large losses and toward very small losses. Adaptive regret bounds demonstrate that an algorithm can take advantage of easy data and potentially have constant regret. We seek to develop fast algorithms that depend on as few parameters as possible, in particular they should be anytime and thus not depend on the time horizon. Our first and main tool, isotuning, is a generalization of the idea of balancing the trade-off of the regret. We develop a set of tools to design and analyze such learning rates easily and show that they adapts automatically to the rate of the regret (whether constant, $O(\log T)$, $O(\sqrt{T})$, etc.) within a factor 2 of the optimal learning rate in hindsight for the same observed quantities. The second tool is an online correction, which allows us to obtain centered bounds for many algorithms, to prevent the regret bounds from being vacuous when the domain is overly large or only partially constrained. The last tool, null updates, prevents the algorithm from performing overly large updates, which could result in unbounded regret, or even invalid updates. We develop a general theory using these tools and apply it to several standard algorithms. In particular, we (almost entirely) restore the adaptivity to small losses of FTRL for unbounded domains, design and prove scale-free adaptive guarantees for a variant of Mirror Descent (at least when the Bregman divergence is convex in its second argument), extend Adapt-ML-Prod to scale-free guarantees, and provide several other minor contributions about Prod, AdaHedge, BOA and Soft-Bayes.  ( 3 min )
    RELDEC: Reinforcement Learning-Based Decoding of Moderate Length LDPC Codes. (arXiv:2112.13934v2 [cs.IT] UPDATED)
    In this work we propose RELDEC, a novel approach for sequential decoding of moderate length low-density parity-check (LDPC) codes. The main idea behind RELDEC is that an optimized decoding policy is subsequently obtained via reinforcement learning based on a Markov decision process (MDP). In contrast to our previous work, where an agent learns to schedule only a single check node (CN) within a group (cluster) of CNs per iteration, in this work we train the agent to schedule all CNs in a cluster, and all clusters in every iteration. That is, in each learning step of RELDEC an agent learns to schedule CN clusters sequentially depending on a reward associated with the outcome of scheduling a particular cluster. We also modify the state space representation of the MDP, enabling RELDEC to be suitable for larger block length LDPC codes than those studied in our previous work. Furthermore, to address decoding under varying channel conditions, we propose agile meta-RELDEC (AM-RELDEC) that employs meta-reinforcement learning. The proposed RELDEC scheme significantly outperforms standard flooding and random sequential decoding for a variety of LDPC codes, including codes designed for 5G new radio.  ( 2 min )
    CT-based Subchondral Bone Microstructural Analysis in Knee Osteoarthritis via MR-Guided Distillation Learning. (arXiv:2307.04390v2 [eess.IV] UPDATED)
    Background: MR-based subchondral bone effectively predicts knee osteoarthritis. However, its clinical application is limited by the cost and time of MR. Purpose: We aim to develop a novel distillation-learning-based method named SRRD for subchondral bone microstructural analysis using easily-acquired CT images, which leverages paired MR images to enhance the CT-based analysis model during training. Materials and Methods: Knee joint images of both CT and MR modalities were collected from October 2020 to May 2021. Firstly, we developed a GAN-based generative model to transform MR images into CT images, which was used to establish the anatomical correspondence between the two modalities. Next, we obtained numerous patches of subchondral bone regions of MR images, together with their trabecular parameters (BV / TV, Tb. Th, Tb. Sp, Tb. N) from the corresponding CT image patches via regression. The distillation-learning technique was used to train the regression model and transfer MR structural information to the CT-based model. The regressed trabecular parameters were further used for knee osteoarthritis classification. Results: A total of 80 participants were evaluated. CT-based regression results of trabecular parameters achieved intra-class correlation coefficients (ICCs) of 0.804, 0.773, 0.711, and 0.622 for BV / TV, Tb. Th, Tb. Sp, and Tb. N, respectively. The use of distillation learning significantly improved the performance of the CT-based knee osteoarthritis classification method using the CNN approach, yielding an AUC score of 0.767 (95% CI, 0.681-0.853) instead of 0.658 (95% CI, 0.574-0.742) (p<.001). Conclusions: The proposed SRRD method showed high reliability and validity in MR-CT registration, regression, and knee osteoarthritis classification, indicating the feasibility of subchondral bone microstructural analysis based on CT images.  ( 3 min )
    Hyper-parameter Tuning for Adversarially Robust Models. (arXiv:2304.02497v2 [cs.LG] UPDATED)
    This work focuses on the problem of hyper-parameter tuning (HPT) for robust (i.e., adversarially trained) models, shedding light on the new challenges and opportunities arising during the HPT process for robust models. To this end, we conduct an extensive experimental study based on 3 popular deep models, in which we explore exhaustively 9 (discretized) HPs, 2 fidelity dimensions, and 2 attack bounds, for a total of 19208 configurations (corresponding to 50 thousand GPU hours). Through this study, we show that the complexity of the HPT problem is further exacerbated in adversarial settings due to the need to independently tune the HPs used during standard and adversarial training: succeeding in doing so (i.e., adopting different HP settings in both phases) can lead to a reduction of up to 80% and 43% of the error for clean and adversarial inputs, respectively. On the other hand, we also identify new opportunities to reduce the cost of HPT for robust models. Specifically, we propose to leverage cheap adversarial training methods to obtain inexpensive, yet highly correlated, estimations of the quality achievable using state-of-the-art methods. We show that, by exploiting this novel idea in conjunction with a recent multi-fidelity optimizer (taKG), the efficiency of the HPT process can be enhanced by up to 2.1x.  ( 2 min )
    Gait Characterization in Duchenne Muscular Dystrophy (DMD) Using a Single-Sensor Accelerometer: Classical Machine Learning and Deep Learning Approaches. (arXiv:2105.06295v3 [eess.SP] UPDATED)
    Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically-developing (TD) peers are visible to the eye, but quantifications of those differences outside of the gait laboratory have been elusive. In this work, we measured vertical, mediolateral, and anteroposterior acceleration using a waist-worn iPhone accelerometer during ambulation across a typical range of velocities. Fifteen TD and fifteen DMD children from 3-16 years of age underwent eight walking/running activities, including five 25 meters walk/run speed-calibration tests at a slow walk to running speeds (SC-L1 to SC-L5), a 6-minute walk test (6MWT), a 100 meters fast-walk/jog/run (100MRW), and a free walk (FW). For clinical anchoring purposes, participants completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed reduced step length and a greater mediolateral component of total power (TP) consistent with shorter strides and Trendelenberg-like gait commonly observed in DMD. ML approaches using temporospatial gait CFs and raw data varied in effectiveness at differentiating between DMD and TD controls at different speeds, with an accuracy of up to 100%. We demonstrate that by using ML with accelerometer data from a consumer-grade smartphone, we can capture DMD-associated gait characteristics in toddlers to teens.  ( 3 min )
    Directed Diffusion: Direct Control of Object Placement through Attention Guidance. (arXiv:2302.13153v2 [cs.CV] UPDATED)
    Text-guided diffusion models such as DALLE-2, Imagen, and Stable Diffusion are able to generate an effectively endless variety of images given only a short text prompt describing the desired image content. In many cases the images are of very high quality. However, these models often struggle to compose scenes containing several key objects such as characters in specified positional relationships. The missing capability to "direct" the placement of characters and objects both within and across images is crucial in storytelling, as recognized in the literature on film and animation theory. In this work, we take a particularly straightforward approach to providing the needed direction. Drawing on the observation that the cross-attention maps for prompt words reflect the spatial layout of objects denoted by those words, we introduce an optimization objective that produces ``activation'' at desired positions in these cross-attention maps. The resulting approach is a step toward generalizing the applicability of text-guided diffusion models beyond single images to collections of related images, as in storybooks. To the best of our knowledge, our Directed Diffusion method is the first diffusion technique that provides positional control over multiple objects, while making use of an existing pre-trained model and maintaining a coherent blend between the positioned objects and the background. Moreover, it requires only a few lines to implement.  ( 3 min )
    Single-Model Attribution of Generative Models Through Final-Layer Inversion. (arXiv:2306.06210v2 [cs.CV] UPDATED)
    Recent groundbreaking developments on generative modeling have sparked interest in practical single-model attribution. Such methods predict whether a sample was generated by a specific generator or not, for instance, to prove intellectual property theft. However, previous works are either limited to the closed-world setting or require undesirable changes of the generative model. We address these shortcomings by proposing FLIPAD, a new approach for single-model attribution in the open-world setting based on final-layer inversion and anomaly detection. We show that the utilized final-layer inversion can be reduced to a convex lasso optimization problem, making our approach theoretically sound and computationally efficient. The theoretical findings are accompanied by an experimental study demonstrating the effectiveness of our approach, outperforming the existing methods.  ( 2 min )
    Distilling BlackBox to Interpretable models for Efficient Transfer Learning. (arXiv:2305.17303v7 [cs.CV] UPDATED)
    Building generalizable AI models is one of the primary challenges in the healthcare domain. While radiologists rely on generalizable descriptive rules of abnormality, Neural Network (NN) models suffer even with a slight shift in input distribution (e.g., scanner type). Fine-tuning a model to transfer knowledge from one domain to another requires a significant amount of labeled data in the target domain. In this paper, we develop an interpretable model that can be efficiently fine-tuned to an unseen target domain with minimal computational cost. We assume the interpretable component of NN to be approximately domain-invariant. However, interpretable models typically underperform compared to their Blackbox (BB) variants. We start with a BB in the source domain and distill it into a \emph{mixture} of shallow interpretable models using human-understandable concepts. As each interpretable model covers a subset of data, a mixture of interpretable models achieves comparable performance as BB. Further, we use the pseudo-labeling technique from semi-supervised learning (SSL) to learn the concept classifier in the target domain, followed by fine-tuning the interpretable models in the target domain. We evaluate our model using a real-life large-scale chest-X-ray (CXR) classification dataset. The code is available at: \url{https://github.com/batmanlab/MICCAI-2023-Route-interpret-repeat-CXRs}.  ( 3 min )
    Improving Code Example Recommendations on Informal Documentation Using BERT and Query-Aware LSH: A Comparative Study. (arXiv:2305.03017v2 [cs.SE] UPDATED)
    Our research investigates the recommendation of code examples to aid software developers, a practice that saves developers significant time by providing ready-to-use code snippets. The focus of our study is Stack Overflow, a commonly used resource for coding discussions and solutions, particularly in the context of the Java programming language. We applied BERT, a powerful Large Language Model (LLM) that enables us to transform code examples into numerical vectors by extracting their semantic information. Once these numerical representations are prepared, we identify Approximate Nearest Neighbors (ANN) using Locality-Sensitive Hashing (LSH). Our research employed two variants of LSH: Random Hyperplane-based LSH and Query-Aware LSH. We rigorously compared these two approaches across four parameters: HitRate, Mean Reciprocal Rank (MRR), Average Execution Time, and Relevance. Our study revealed that the Query-Aware (QA) approach showed superior performance over the Random Hyperplane-based (RH) method. Specifically, it exhibited a notable improvement of 20% to 35% in HitRate for query pairs compared to the RH approach. Furthermore, the QA approach proved significantly more time-efficient, with its speed in creating hashing tables and assigning data samples to buckets being at least four times faster. It can return code examples within milliseconds, whereas the RH approach typically requires several seconds to recommend code examples. Due to the superior performance of the QA approach, we tested it against PostFinder and FaCoY, the state-of-the-art baselines. Our QA method showed comparable efficiency proving its potential for effective code recommendation.  ( 3 min )
    A Survey on Explainable Anomaly Detection. (arXiv:2210.06959v2 [cs.LG] UPDATED)
    In the past two decades, most research on anomaly detection has focused on improving the accuracy of the detection, while largely ignoring the explainability of the corresponding methods and thus leaving the explanation of outcomes to practitioners. As anomaly detection algorithms are increasingly used in safety-critical domains, providing explanations for the high-stakes decisions made in those domains has become an ethical and regulatory requirement. Therefore, this work provides a comprehensive and structured survey on state-of-the-art explainable anomaly detection techniques. We propose a taxonomy based on the main aspects that characterize each explainable anomaly detection technique, aiming to help practitioners and researchers find the explainable anomaly detection method that best suits their needs.  ( 2 min )
    Successive Affine Learning for Deep Neural Networks. (arXiv:2305.07996v2 [cs.LG] UPDATED)
    This paper introduces a successive affine learning (SAL) model for constructing deep neural networks (DNNs). Traditionally, a DNN is built by solving a non-convex optimization problem. It is often challenging to solve such a problem numerically due to its non-convexity and having a large number of layers. To address this challenge, inspired by the human education system, the multi-grade deep learning (MGDL) model was recently initiated by the author of this paper. The MGDL model learns a DNN in several grades, in each of which one constructs a shallow DNN consisting of a relatively small number of layers. The MGDL model still requires solving several non-convex optimization problems. The proposed SAL model mutates from the MGDL model. Noting that each layer of a DNN consists of an affine map followed by an activation function, we propose to learn the affine map by solving a quadratic/convex optimization problem which involves the activation function only {\it after} the weight matrix and the bias vector for the current layer have been trained. In the context of function approximation, for a given function the SAL model generates an expansion of the function with adaptive basis functions in the form of DNNs. We establish the Pythagorean identity and the Parseval identity for the system generated by the SAL model. Moreover, we provide a convergence theorem of the SAL process in the sense that either it terminates after a finite number of grades or the norms of its optimal error functions strictly decrease to a limit as the grade number increases to infinity. Furthermore, we present numerical examples of proof of concept which demonstrate that the proposed SAL model significantly outperforms the traditional deep learning model.  ( 3 min )
    Exploring Image Augmentations for Siamese Representation Learning with Chest X-Rays. (arXiv:2301.12636v2 [eess.IV] UPDATED)
    Image augmentations are quintessential for effective visual representation learning across self-supervised learning techniques. While augmentation strategies for natural imaging have been studied extensively, medical images are vastly different from their natural counterparts. Thus, it is unknown whether common augmentation strategies employed in Siamese representation learning generalize to medical images and to what extent. To address this challenge, in this study, we systematically assess the effect of various augmentations on the quality and robustness of the learned representations. We train and evaluate Siamese Networks for abnormality detection on chest X-Rays across three large datasets (MIMIC-CXR, CheXpert and VinDR-CXR). We investigate the efficacy of the learned representations through experiments involving linear probing, fine-tuning, zero-shot transfer, and data efficiency. Finally, we identify a set of augmentations that yield robust representations that generalize well to both out-of-distribution data and diseases, while outperforming supervised baselines using just zero-shot transfer and linear probes by up to 20%. Our code is available at https://github.com/StanfordMIMI/siaug.  ( 2 min )
    Generative Pretrained Autoregressive Transformer Graph Neural Network applied to the Analysis and Discovery of Novel Proteins. (arXiv:2305.04934v2 [q-bio.BM] UPDATED)
    We report a flexible language-model based deep learning strategy, applied here to solve complex forward and inverse problems in protein modeling, based on an attention neural network that integrates transformer and graph convolutional architectures in a causal multi-headed graph mechanism, to realize a generative pretrained model. The model is applied to predict secondary structure content (per-residue level and overall content), protein solubility, and sequencing tasks. Further trained on inverse tasks, the model is rendered capable of designing proteins with these properties as target features. The model is formulated as a general framework, completely prompt-based, and can be adapted for a variety of downstream tasks. We find that adding additional tasks yields emergent synergies that the model exploits in improving overall performance, beyond what would be possible by training a model on each dataset alone. Case studies are presented to validate the method, yielding protein designs specifically focused on structural proteins, but also exploring the applicability in the design of soluble, antimicrobial biomaterials. While our model is trained to ultimately perform 8 distinct tasks, with available datasets it can be extended to solve additional problems. In a broader sense, this work illustrates a form of multiscale modeling that relates a set of ultimate building blocks (here, byte-level utf8 characters that define the nature of the physical system at hand) to complex output. This materiomic scheme captures complex emergent relationships between universal building block and resulting properties via a synergizing learning capacity to express a set of potentialities embedded in the knowledge used in training, via the interplay of universality and diversity.  ( 3 min )
    Prediction intervals for neural network models using weighted asymmetric loss functions. (arXiv:2210.04318v4 [stat.ML] UPDATED)
    We propose a simple and efficient approach to generate a prediction intervals (PI) for approximated and forecasted trends. Our method leverages a weighted asymmetric loss function to estimate the lower and upper bounds of the PI, with the weights determined by its coverage probability. We provide a concise mathematical proof of the method, show how it can be extended to derive PIs for parametrised functions and argue why the method works for predicting PIs of dependent variables. The presented tests of the method on a real-world forecasting task using a neural network-based model show that it can produce reliable PIs in complex machine learning scenarios.  ( 2 min )
    QI2 -- an Interactive Tool for Data Quality Assurance. (arXiv:2307.03419v2 [cs.CY] CROSS LISTED)
    The importance of high data quality is increasing with the growing impact and distribution of ML systems and big data. Also the planned AI Act from the European commission defines challenging legal requirements for data quality especially for the market introduction of safety relevant ML systems. In this paper we introduce a novel approach that supports the data quality assurance process of multiple data quality aspects. This approach enables the verification of quantitative data quality requirements. The concept and benefits are introduced and explained on small example data sets. How the method is applied is demonstrated on the well known MNIST data set based an handwritten digits.  ( 2 min )
    Hybrid quantum-classical machine learning for generative chemistry and drug design. (arXiv:2108.11644v2 [quant-ph] UPDATED)
    Deep generative chemistry models emerge as powerful tools to expedite drug discovery. However, the immense size and complexity of the structural space of all possible drug-like molecules pose significant obstacles, which could be overcome with hybrid architectures combining quantum computers with deep classical networks. As the first step toward this goal, we built a compact discrete variational autoencoder (DVAE) with a Restricted Boltzmann Machine (RBM) of reduced size in its latent layer. The size of the proposed model was small enough to fit on a state-of-the-art D-Wave quantum annealer and allowed training on a subset of the ChEMBL dataset of biologically active compounds. Finally, we generated 2331 novel chemical structures with medicinal chemistry and synthetic accessibility properties in the ranges typical for molecules from ChEMBL. The presented results demonstrate the feasibility of using already existing or soon-to-be-available quantum computing devices as testbeds for future drug discovery applications.  ( 2 min )
    Solving PDEs with Unmeasurable Source Terms Using Coupled Physics-Informed Neural Network with Recurrent Prediction for Soft Sensors. (arXiv:2301.08618v3 [cs.LG] UPDATED)
    Partial differential equations (PDEs) are a model candidate for soft sensors in industrial processes with spatiotemporal dependence. Although physics-informed neural networks (PINNs) are a promising machine learning method for solving PDEs, they are infeasible for the nonhomogeneous PDEs with unmeasurable source terms. To this end, a coupled PINN (CPINN) with a recurrent prediction (RP) learning strategy (CPINN- RP) is proposed. First, CPINN composed of NetU and NetG is proposed. NetU is for approximating PDEs solutions and NetG is for regularizing the training of NetU. The two networks are integrated into a data-physics-hybrid loss function. Then, we theoretically prove that the proposed CPINN has a satisfying approximation capability for solutions to nonhomogeneous PDEs with unmeasurable source terms. Besides the theoretical aspects, we propose a hierarchical training strategy to optimize and couple NetU and NetG. Secondly, NetU-RP is proposed for compensating information loss in data sampling to improve the prediction performance, in which RP is the recurrently delayed outputs of well-trained CPINN and hard sensors. Finally, the artificial and practical datasets are used to verify the feasibility and effectiveness of CPINN-RP for soft sensors.  ( 3 min )
    Generalization Error of First-Order Methods for Statistical Learning with Generic Oracles. (arXiv:2307.04679v2 [cs.LG] UPDATED)
    In this paper, we provide a novel framework for the analysis of generalization error of first-order optimization algorithms for statistical learning when the gradient can only be accessed through partial observations given by an oracle. Our analysis relies on the regularity of the gradient w.r.t. the data samples, and allows to derive near matching upper and lower bounds for the generalization error of multiple learning problems, including supervised learning, transfer learning, robust learning, distributed learning and communication efficient learning using gradient quantization. These results hold for smooth and strongly-convex optimization problems, as well as smooth non-convex optimization problems verifying a Polyak-Lojasiewicz assumption. In particular, our upper and lower bounds depend on a novel quantity that extends the notion of conditional standard deviation, and is a measure of the extent to which the gradient can be approximated by having access to the oracle. As a consequence, our analysis provides a precise meaning to the intuition that optimization of the statistical learning objective is as hard as the estimation of its gradient. Finally, we show that, in the case of standard supervised learning, mini-batch gradient descent with increasing batch sizes and a warm start can reach a generalization error that is optimal up to a multiplicative factor, thus motivating the use of this optimization scheme in practical applications.  ( 3 min )
    Temporal Conditioning Spiking Latent Variable Models of the Neural Response to Natural Visual Scenes. (arXiv:2306.12045v2 [q-bio.NC] UPDATED)
    Developing computational models of neural response is crucial for understanding sensory processing and neural computations. Current state-of-the-art neural network methods use temporal filters to handle temporal dependencies, resulting in an unrealistic and inflexible processing flow. Meanwhile, these methods target trial-averaged firing rates and fail to capture important features in spike trains. This work presents the temporal conditioning spiking latent variable models (TeCoS-LVM) to simulate the neural response to natural visual stimuli. We use spiking neurons to produce spike outputs that directly match the recorded trains. This approach helps to avoid losing information embedded in the original spike trains. We exclude the temporal dimension from the model parameter space and introduce a temporal conditioning operation to allow the model to adaptively explore and exploit temporal dependencies in stimuli sequences in a natural paradigm. We show that TeCoS-LVM models can produce more realistic spike activities and accurately fit spike statistics than powerful alternatives. Additionally, learned TeCoS-LVM models can generalize well to longer time scales. Overall, while remaining computationally tractable, our model effectively captures key features of neural coding systems. It thus provides a useful tool for building accurate predictive computational accounts for various sensory perception circuits.  ( 3 min )
    I2I: Initializing Adapters with Improvised Knowledge. (arXiv:2304.02168v2 [cs.CL] UPDATED)
    Adapters present a promising solution to the catastrophic forgetting problem in continual learning. However, training independent Adapter modules for every new task misses an opportunity for cross-task knowledge transfer. We propose Improvise to Initialize (I2I), a continual learning algorithm that initializes Adapters for incoming tasks by distilling knowledge from previously-learned tasks' Adapters. We evaluate I2I on CLiMB, a multimodal continual learning benchmark, by conducting experiments on sequences of visual question answering tasks. Adapters trained with I2I consistently achieve better task accuracy than independently-trained Adapters, demonstrating that our algorithm facilitates knowledge transfer between task Adapters. I2I also results in better cross-task knowledge transfer than the state-of-the-art AdapterFusion without incurring the associated parametric cost.  ( 2 min )
    High Dimensional Quantum Machine Learning With Small Quantum Computers. (arXiv:2203.13739v3 [quant-ph] UPDATED)
    Quantum computers hold great promise to enhance machine learning, but their current qubit counts restrict the realisation of this promise. In an attempt to placate this limitation techniques can be applied for evaluating a quantum circuit using a machine with fewer qubits than the circuit naively requires. These techniques work by evaluating many smaller circuits on the smaller machine, that are then combined in a polynomial to replicate the output of the larger machine. This scheme requires more circuit evaluations than are practical for general circuits. However, we investigate the possibility that for certain applications many of these subcircuits are superfluous, and that a much smaller sum is sufficient to estimate the full circuit. We construct a machine learning model that may be capable of approximating the outputs of the larger circuit with much fewer circuit evaluations. We successfully apply our model to the task of digit recognition, using simulated quantum computers much smaller than the data dimension. The model is also applied to the task of approximating a random 10 qubit PQC with simulated access to a 5 qubit computer, even with only relatively modest number of circuits our model provides an accurate approximation of the 10 qubit PQCs output, superior to a neural network attempt. The developed method might be useful for implementing quantum models on larger data throughout the NISQ era.
    Protecting the Future: Neonatal Seizure Detection with Spatial-Temporal Modeling. (arXiv:2307.05382v1 [eess.SP])
    A timely detection of seizures for newborn infants with electroencephalogram (EEG) has been a common yet life-saving practice in the Neonatal Intensive Care Unit (NICU). However, it requires great human efforts for real-time monitoring, which calls for automated solutions to neonatal seizure detection. Moreover, the current automated methods focusing on adult epilepsy monitoring often fail due to (i) dynamic seizure onset location in human brains; (ii) different montages on neonates and (iii) huge distribution shift among different subjects. In this paper, we propose a deep learning framework, namely STATENet, to address the exclusive challenges with exquisite designs at the temporal, spatial and model levels. The experiments over the real-world large-scale neonatal EEG dataset illustrate that our framework achieves significantly better seizure detection performance.
    Responsive parallelized architecture for deploying deep learning models in production environments. (arXiv:2112.08933v2 [cs.LG] UPDATED)
    Recruiters can easily shortlist candidates for jobs via viewing their curriculum vitae (CV) document. Unstructured document CV beholds candidate's portfolio and named entities listing details. The main aim of this study is to design and propose a web oriented, highly responsive, computational pipeline that systematically predicts CV entities using hierarchically-refined label attention networks. Deep learning models specialized for named entity recognition were trained on large dataset to predict relevant fields. The article suggests an optimal strategy to use a number of deep learning models in parallel and predict in real time. We demonstrate selection of light weight micro web framework using Analytical Hierarchy Processing algorithm and focus on an approach useful to deploy large deep learning model-based pipelines in production ready environments using microservices. Deployed models and architecture proposed helped in parsing normal CV in less than 700 milliseconds for sequential flow of requests.
    AdaptiveRec: Adaptively Construct Pairs for Contrastive Learning in Sequential Recommendation. (arXiv:2307.05469v1 [cs.IR])
    This paper presents a solution to the challenges faced by contrastive learning in sequential recommendation systems. In particular, it addresses the issue of false negative, which limits the effectiveness of recommendation algorithms. By introducing an advanced approach to contrastive learning, the proposed method improves the quality of item embeddings and mitigates the problem of falsely categorizing similar instances as dissimilar. Experimental results demonstrate performance enhancements compared to existing systems. The flexibility and applicability of the proposed approach across various recommendation scenarios further highlight its value in enhancing sequential recommendation systems.
    Mitigating the Accuracy-Robustness Trade-off via Multi-Teacher Adversarial Distillation. (arXiv:2306.16170v2 [cs.LG] UPDATED)
    Adversarial training is a practical approach for improving the robustness of deep neural networks against adversarial attacks. Although bringing reliable robustness, the performance toward clean examples is negatively affected after adversarial training, which means a trade-off exists between accuracy and robustness. Recently, some studies have tried to use knowledge distillation methods in adversarial training, achieving competitive performance in improving the robustness but the accuracy for clean samples is still limited. In this paper, to mitigate the accuracy-robustness trade-off, we introduce the Multi-Teacher Adversarial Robustness Distillation (MTARD) to guide the model's adversarial training process by applying a strong clean teacher and a strong robust teacher to handle the clean examples and adversarial examples, respectively. During the optimization process, to ensure that different teachers show similar knowledge scales, we design the Entropy-Based Balance algorithm to adjust the teacher's temperature and keep the teachers' information entropy consistent. Besides, to ensure that the student has a relatively consistent learning speed from multiple teachers, we propose the Normalization Loss Balance algorithm to adjust the learning weights of different types of knowledge. A series of experiments conducted on public datasets demonstrate that MTARD outperforms the state-of-the-art adversarial training and distillation methods against various adversarial attacks.
    Continual Learning on Dynamic Graphs via Parameter Isolation. (arXiv:2305.13825v2 [cs.LG] UPDATED)
    Many real-world graph learning tasks require handling dynamic graphs where new nodes and edges emerge. Dynamic graph learning methods commonly suffer from the catastrophic forgetting problem, where knowledge learned for previous graphs is overwritten by updates for new graphs. To alleviate the problem, continual graph learning methods are proposed. However, existing continual graph learning methods aim to learn new patterns and maintain old ones with the same set of parameters of fixed size, and thus face a fundamental tradeoff between both goals. In this paper, we propose Parameter Isolation GNN (PI-GNN) for continual learning on dynamic graphs that circumvents the tradeoff via parameter isolation and expansion. Our motivation lies in that different parameters contribute to learning different graph patterns. Based on the idea, we expand model parameters to continually learn emerging graph patterns. Meanwhile, to effectively preserve knowledge for unaffected patterns, we find parameters that correspond to them via optimization and freeze them to prevent them from being rewritten. Experiments on eight real-world datasets corroborate the effectiveness of PI-GNN compared to state-of-the-art baselines.
    Defining data science: a new field of inquiry. (arXiv:2306.16177v2 [cs.LG] UPDATED)
    Data science is not a science. It is a research paradigm. Its power, scope, and scale will surpass science, our most powerful research paradigm, to enable knowledge discovery and change our world. We have yet to understand and define it, vital to realizing its potential and managing its risks. Modern data science is in its infancy. Emerging slowly since 1962 and rapidly since 2000, it is a fundamentally new field of inquiry, one of the most active, powerful, and rapidly evolving 21st century innovations. Due to its value, power, and applicability, it is emerging in 40+ disciplines, hundreds of research areas, and thousands of applications. Millions of data science publications contain myriad definitions of data science and data science problem solving. Due to its infancy, many definitions are independent, application-specific, mutually incomplete, redundant, or inconsistent, hence so is data science. This research addresses this data science multiple definitions challenge by proposing the development of coherent, unified definition based on a data science reference framework using a data science journal for the data science community to achieve such a definition. This paper provides candidate definitions for essential data science artifacts that are required to discuss such a definition. They are based on the classical research paradigm concept consisting of a philosophy of data science, the data science problem solving paradigm, and the six component data science reference framework (axiology, ontology, epistemology, methodology, methods, technology) that is a frequently called for unifying framework with which to define, unify, and evolve data science. It presents challenges for defining data science, solution approaches, i.e., means for defining data science, and their requirements and benefits as the basis of a comprehensive solution.
    Randomized Exploration in Generalized Linear Bandits. (arXiv:1906.08947v3 [cs.LG] UPDATED)
    We study two randomized algorithms for generalized linear bandits. The first, GLM-TSL, samples a generalized linear model (GLM) from the Laplace approximation to the posterior distribution. The second, GLM-FPL, fits a GLM to a randomly perturbed history of past rewards. We analyze both algorithms and derive $\tilde{O}(d \sqrt{n \log K})$ upper bounds on their $n$-round regret, where $d$ is the number of features and $K$ is the number of arms. The former improves on prior work while the latter is the first for Gaussian noise perturbations in non-linear models. We empirically evaluate both GLM-TSL and GLM-FPL in logistic bandits, and apply GLM-FPL to neural network bandits. Our work showcases the role of randomization, beyond posterior sampling, in exploration.
    Automating Augmentation Through Random Unidimensional Search. (arXiv:2106.08756v3 [cs.LG] UPDATED)
    It is no secret amongst deep learning researchers that finding the optimal data augmentation strategy during training can mean the difference between state-of-the-art performance and a run-of-the-mill result. To that end, the community has seen many efforts to automate the process of finding the perfect augmentation procedure for any task at hand. Unfortunately, even recent cutting-edge methods bring massive computational overhead, requiring as many as 100 full model trainings to settle on an ideal configuration. We show how to achieve equivalent performance using just 6 trainings with Random Unidimensional Augmentation. Source code is available at https://github.com/fastestimator/RUA/tree/v1.0
    The Statistical Complexity of Interactive Decision Making. (arXiv:2112.13487v3 [cs.LG] UPDATED)
    A fundamental challenge in interactive learning and decision making, ranging from bandit problems to reinforcement learning, is to provide sample-efficient, adaptive learning algorithms that achieve near-optimal regret. This question is analogous to the classical problem of optimal (supervised) statistical learning, where there are well-known complexity measures (e.g., VC dimension and Rademacher complexity) that govern the statistical complexity of learning. However, characterizing the statistical complexity of interactive learning is substantially more challenging due to the adaptive nature of the problem. The main result of this work provides a complexity measure, the Decision-Estimation Coefficient, that is proven to be both necessary and sufficient for sample-efficient interactive learning. In particular, we provide: 1. a lower bound on the optimal regret for any interactive decision making problem, establishing the Decision-Estimation Coefficient as a fundamental limit. 2. a unified algorithm design principle, Estimation-to-Decisions (E2D), which transforms any algorithm for supervised estimation into an online algorithm for decision making. E2D attains a regret bound that matches our lower bound up to dependence on a notion of estimation performance, thereby achieving optimal sample-efficient learning as characterized by the Decision-Estimation Coefficient. Taken together, these results constitute a theory of learnability for interactive decision making. When applied to reinforcement learning settings, the Decision-Estimation Coefficient recovers essentially all existing hardness results and lower bounds. More broadly, the approach can be viewed as a decision-theoretic analogue of the classical Le Cam theory of statistical estimation; it also unifies a number of existing approaches -- both Bayesian and frequentist.
    The Implicit Bias of Batch Normalization in Linear Models and Two-layer Linear Convolutional Neural Networks. (arXiv:2306.11680v2 [cs.LG] UPDATED)
    We study the implicit bias of batch normalization trained by gradient descent. We show that when learning a linear model with batch normalization for binary classification, gradient descent converges to a uniform margin classifier on the training data with an $\exp(-\Omega(\log^2 t))$ convergence rate. This distinguishes linear models with batch normalization from those without batch normalization in terms of both the type of implicit bias and the convergence rate. We further extend our result to a class of two-layer, single-filter linear convolutional neural networks, and show that batch normalization has an implicit bias towards a patch-wise uniform margin. Based on two examples, we demonstrate that patch-wise uniform margin classifiers can outperform the maximum margin classifiers in certain learning problems. Our results contribute to a better theoretical understanding of batch normalization.
    Domain-Agnostic Neural Architecture for Class Incremental Continual Learning in Document Processing Platform. (arXiv:2307.05399v1 [cs.LG])
    Production deployments in complex systems require ML architectures to be highly efficient and usable against multiple tasks. Particularly demanding are classification problems in which data arrives in a streaming fashion and each class is presented separately. Recent methods with stochastic gradient learning have been shown to struggle in such setups or have limitations like memory buffers, and being restricted to specific domains that disable its usage in real-world scenarios. For this reason, we present a fully differentiable architecture based on the Mixture of Experts model, that enables the training of high-performance classifiers when examples from each class are presented separately. We conducted exhaustive experiments that proved its applicability in various domains and ability to learn online in production environments. The proposed technique achieves SOTA results without a memory buffer and clearly outperforms the reference methods.
    Using BOLD-fMRI to Compute the Respiration Volume per Time (RTV) and Respiration Variation (RV) with Convolutional Neural Networks (CNN) in the Human Connectome Development Cohort. (arXiv:2307.05426v1 [eess.SP])
    In many fMRI studies, respiratory signals are unavailable or do not have acceptable quality. Consequently, the direct removal of low-frequency respiratory variations from BOLD signals is not possible. This study proposes a one-dimensional CNN model for reconstruction of two respiratory measures, RV and RVT. Results show that a CNN can capture informative features from resting BOLD signals and reconstruct realistic RV and RVT timeseries. It is expected that application of the proposed method will lower the cost of fMRI studies, reduce complexity, and decrease the burden on participants as they will not be required to wear a respiratory bellows.
    ISLTranslate: Dataset for Translating Indian Sign Language. (arXiv:2307.05440v1 [cs.CL])
    Sign languages are the primary means of communication for many hard-of-hearing people worldwide. Recently, to bridge the communication gap between the hard-of-hearing community and the rest of the population, several sign language translation datasets have been proposed to enable the development of statistical sign language translation systems. However, there is a dearth of sign language resources for the Indian sign language. This resource paper introduces ISLTranslate, a translation dataset for continuous Indian Sign Language (ISL) consisting of 31k ISL-English sentence/phrase pairs. To the best of our knowledge, it is the largest translation dataset for continuous Indian Sign Language. We provide a detailed analysis of the dataset. To validate the performance of existing end-to-end Sign language to spoken language translation systems, we benchmark the created dataset with a transformer-based model for ISL translation.
    Metropolis Sampling for Constrained Diffusion Models. (arXiv:2307.05439v1 [cs.LG])
    Denoising diffusion models have recently emerged as the predominant paradigm for generative modelling. Their extension to Riemannian manifolds has facilitated their application to an array of problems in the natural sciences. Yet, in many practical settings, such manifolds are defined by a set of constraints and are not covered by the existing (Riemannian) diffusion model methodology. Recent work has attempted to address this issue by employing novel noising processes based on logarithmic barrier methods or reflected Brownian motions. However, the associated samplers are computationally burdensome as the complexity of the constraints increases. In this paper, we introduce an alternative simple noising scheme based on Metropolis sampling that affords substantial gains in computational efficiency and empirical performance compared to the earlier samplers. Of independent interest, we prove that this new process corresponds to a valid discretisation of the reflected Brownian motion. We demonstrate the scalability and flexibility of our approach on a range of problem settings with convex and non-convex constraints, including applications from geospatial modelling, robotics and protein design.
    A Self-Supervised Algorithm for Denoising Photoplethysmography Signals for Heart Rate Estimation from Wearables. (arXiv:2307.05339v1 [eess.SP])
    Smart watches and other wearable devices are equipped with photoplethysmography (PPG) sensors for monitoring heart rate and other aspects of cardiovascular health. However, PPG signals collected from such devices are susceptible to corruption from noise and motion artifacts, which cause errors in heart rate estimation. Typical denoising approaches filter or reconstruct the signal in ways that eliminate much of the morphological information, even from the clean parts of the signal that would be useful to preserve. In this work, we develop an algorithm for denoising PPG signals that reconstructs the corrupted parts of the signal, while preserving the clean parts of the PPG signal. Our novel framework relies on self-supervised training, where we leverage a large database of clean PPG signals to train a denoising autoencoder. As we show, our reconstructed signals provide better estimates of heart rate from PPG signals than the leading heart rate estimation methods. Further experiments show significant improvement in Heart Rate Variability (HRV) estimation from PPG signals using our algorithm. We conclude that our algorithm denoises PPG signals in a way that can improve downstream analysis of many different health metrics from wearable devices.
    Action-based Early Autism Diagnosis Using Contrastive Feature Learning. (arXiv:2209.05379v3 [cs.CV] UPDATED)
    Autism, also known as Autism Spectrum Disorder (or ASD), is a neurological disorder. Its main symptoms include difficulty in (verbal and/or non-verbal) communication, and rigid/repetitive behavior. These symptoms are often indistinguishable from a normal (control) individual, due to which this disorder remains undiagnosed in early childhood leading to delayed treatment. Since the learning curve is steep during the initial age, an early diagnosis of autism could allow to take adequate interventions at the right time, which might positively affect the growth of an autistic child. Further, the traditional methods of autism diagnosis require multiple visits to a specialized psychiatrist, however this process can be time-consuming. In this paper, we present a learning based approach to automate autism diagnosis using simple and small action video clips of subjects. This task is particularly challenging because the amount of annotated data available is small, and the variations among samples from the two categories (ASD and control) are generally indistinguishable. This is also evident from poor performance of a binary classifier learned using the cross-entropy loss on top of a baseline encoder. To address this, we adopt contrastive feature learning in both self supervised and supervised learning frameworks, and show that these can lead to a significant increase in the prediction accuracy of a binary classifier on this task. We further validate this by conducting thorough experimental analyses under different set-ups on two publicly available datasets.
    Improving the Security of Smartwatch Payment with Deep Learning. (arXiv:2307.05437v1 [cs.CR])
    Making contactless payments using a smartwatch is increasingly popular, but this payment medium lacks traditional biometric security measures such as facial or fingerprint recognition. In 2022, Sturgess et al. proposed WatchAuth, a system for authenticating smartwatch payments using the physical gesture of reaching towards a payment terminal. While effective, the system requires the user to undergo a burdensome enrolment period to achieve acceptable error levels. In this dissertation, we explore whether applications of deep learning can reduce the number of gestures a user must provide to enrol into an authentication system for smartwatch payment. We firstly construct a deep-learned authentication system that outperforms the current state-of-the-art, including in a scenario where the target user has provided a limited number of gestures. We then develop a regularised autoencoder model for generating synthetic user-specific gestures. We show that using these gestures in training improves classification ability for an authentication system. Through this technique we can reduce the number of gestures required to enrol a user into a WatchAuth-like system without negatively impacting its error rates.
    A Physics-Informed Low-Shot Learning For sEMG-Based Estimation of Muscle Force and Joint Kinematics. (arXiv:2307.05361v1 [eess.SP])
    Muscle force and joint kinematics estimation from surface electromyography (sEMG) are essential for real-time biomechanical analysis of the dynamic interplay among neural muscle stimulation, muscle dynamics, and kinetics. Recent advances in deep neural networks (DNNs) have shown the potential to improve biomechanical analysis in a fully automated and reproducible manner. However, the small sample nature and physical interpretability of biomechanical analysis limit the applications of DNNs. This paper presents a novel physics-informed low-shot learning method for sEMG-based estimation of muscle force and joint kinematics. This method seamlessly integrates Lagrange's equation of motion and inverse dynamic muscle model into the generative adversarial network (GAN) framework for structured feature decoding and extrapolated estimation from the small sample data. Specifically, Lagrange's equation of motion is introduced into the generative model to restrain the structured decoding of the high-level features following the laws of physics. And a physics-informed policy gradient is designed to improve the adversarial learning efficiency by rewarding the consistent physical representation of the extrapolated estimations and the physical references. Experimental validations are conducted on two scenarios (i.e. the walking trials and wrist motion trials). Results indicate that the estimations of the muscle forces and joint kinematics are unbiased compared to the physics-based inverse dynamics, which outperforms the selected benchmark methods, including physics-informed convolution neural network (PI-CNN), vallina generative adversarial network (GAN), and multi-layer extreme learning machine (ML-ELM).
    U-CREAT: Unsupervised Case Retrieval using Events extrAcTion. (arXiv:2307.05260v1 [cs.IR])
    The task of Prior Case Retrieval (PCR) in the legal domain is about automatically citing relevant (based on facts and precedence) prior legal cases in a given query case. To further promote research in PCR, in this paper, we propose a new large benchmark (in English) for the PCR task: IL-PCR (Indian Legal Prior Case Retrieval) corpus. Given the complex nature of case relevance and the long size of legal documents, BM25 remains a strong baseline for ranking the cited prior documents. In this work, we explore the role of events in legal case retrieval and propose an unsupervised retrieval method-based pipeline U-CREAT (Unsupervised Case Retrieval using Events Extraction). We find that the proposed unsupervised retrieval method significantly increases performance compared to BM25 and makes retrieval faster by a considerable margin, making it applicable to real-time case retrieval systems. Our proposed system is generic, we show that it generalizes across two different legal systems (Indian and Canadian), and it shows state-of-the-art performance on the benchmarks for both the legal systems (IL-PCR and COLIEE corpora).
    Boosting Feedback Efficiency of Interactive Reinforcement Learning by Adaptive Learning from Scores. (arXiv:2307.05405v1 [cs.RO])
    Interactive reinforcement learning has shown promise in learning complex robotic tasks. However, the process can be human-intensive due to the requirement of large amount of interactive feedback. This paper presents a new method that uses scores provided by humans, instead of pairwise preferences, to improve the feedback efficiency of interactive reinforcement learning. Our key insight is that scores can yield significantly more data than pairwise preferences. Specifically, we require a teacher to interactively score the full trajectories of an agent to train a behavioral policy in a sparse reward environment. To avoid unstable scores given by human negatively impact the training process, we propose an adaptive learning scheme. This enables the learning paradigm to be insensitive to imperfect or unreliable scores. We extensively evaluate our method on robotic locomotion and manipulation tasks. The results show that the proposed method can efficiently learn near-optimal policies by adaptive learning from scores, while requiring less feedback compared to pairwise preference learning methods. The source codes are publicly available at https://github.com/SSKKai/Interactive-Scoring-IRL.
    A Survey From Distributed Machine Learning to Distributed Deep Learning. (arXiv:2307.05232v1 [cs.LG])
    Artificial intelligence has achieved significant success in handling complex tasks in recent years. This success is due to advances in machine learning algorithms and hardware acceleration. In order to obtain more accurate results and solve more complex problems, algorithms must be trained with more data. This huge amount of data could be time-consuming to process and require a great deal of computation. This solution could be achieved by distributing the data and algorithm across several machines, which is known as distributed machine learning. There has been considerable effort put into distributed machine learning algorithms, and different methods have been proposed so far. In this article, we present a comprehensive summary of the current state-of-the-art in the field through the review of these algorithms. We divide this algorithms in classification and clustering (traditional machine learning), deep learning and deep reinforcement learning groups. Distributed deep learning has gained more attention in recent years and most of studies worked on this algorithms. As a result, most of the articles we discussed here belong to this category. Based on our investigation of algorithms, we highlight limitations that should be addressed in future research.
    Unleashing the Potential of Regularization Strategies in Learning with Noisy Labels. (arXiv:2307.05025v1 [cs.LG])
    In recent years, research on learning with noisy labels has focused on devising novel algorithms that can achieve robustness to noisy training labels while generalizing to clean data. These algorithms often incorporate sophisticated techniques, such as noise modeling, label correction, and co-training. In this study, we demonstrate that a simple baseline using cross-entropy loss, combined with widely used regularization strategies like learning rate decay, model weights average, and data augmentations, can outperform state-of-the-art methods. Our findings suggest that employing a combination of regularization strategies can be more effective than intricate algorithms in tackling the challenges of learning with noisy labels. While some of these regularization strategies have been utilized in previous noisy label learning research, their full potential has not been thoroughly explored. Our results encourage a reevaluation of benchmarks for learning with noisy labels and prompt reconsideration of the role of specialized learning algorithms designed for training with noisy labels.
    Attribute Controlled Dialogue Prompting. (arXiv:2307.05228v1 [cs.CL])
    Prompt-tuning has become an increasingly popular parameter-efficient method for adapting large pretrained language models to downstream tasks. However, both discrete prompting and continuous prompting assume fixed prompts for all data samples within a task, neglecting the fact that inputs vary greatly in some tasks such as open-domain dialogue generation. In this paper, we present a novel, instance-specific prompt-tuning algorithm for dialogue generation. Specifically, we generate prompts based on instance-level control code, rather than the conversation history, to explore their impact on controlled dialogue generation. Experiments on popular open-domain dialogue datasets, evaluated on both automated metrics and human evaluation, demonstrate that our method is superior to prompting baselines and comparable to fine-tuning with only 5%-6% of total parameters.
    Self-Supervised Learning with Lie Symmetries for Partial Differential Equations. (arXiv:2307.05432v1 [cs.LG])
    Machine learning for differential equations paves the way for computationally efficient alternatives to numerical solvers, with potentially broad impacts in science and engineering. Though current algorithms typically require simulated training data tailored to a given setting, one may instead wish to learn useful information from heterogeneous sources, or from real dynamical systems observations that are messy or incomplete. In this work, we learn general-purpose representations of PDEs from heterogeneous data by implementing joint embedding methods for self-supervised learning (SSL), a framework for unsupervised representation learning that has had notable success in computer vision. Our representation outperforms baseline approaches to invariant tasks, such as regressing the coefficients of a PDE, while also improving the time-stepping performance of neural solvers. We hope that our proposed methodology will prove useful in the eventual development of general-purpose foundation models for PDEs.
    The Value of Chess Squares. (arXiv:2307.05330v1 [cs.AI])
    Valuing chess squares and determining the placement of pieces on the board are the main objectives of our study. With the emergence of chess AI, it has become possible to accurately assess the worth of positions in a game of chess. The conventional approach assigns fixed values to pieces $(\symking=\infty, \symqueen=9, \symrook=5, \symbishop=3, \symknight=3, \sympawn=1)$. We enhance this analysis by introducing marginal valuations for both pieces and squares. We demonstrate our method by examining the positioning of Knights and Bishops, and also provide valuable insights into the valuation of pawns. Notably, Nimzowitsch was among the pioneers in advocating for the significance of Pawn structure and valuation. Finally, we conclude by suggesting potential avenues for future research.
    Membership Inference Attacks on DNNs using Adversarial Perturbations. (arXiv:2307.05193v1 [cs.LG])
    Several membership inference (MI) attacks have been proposed to audit a target DNN. Given a set of subjects, MI attacks tell which subjects the target DNN has seen during training. This work focuses on the post-training MI attacks emphasizing high confidence membership detection -- True Positive Rates (TPR) at low False Positive Rates (FPR). Current works in this category -- likelihood ratio attack (LiRA) and enhanced MI attack (EMIA) -- only perform well on complex datasets (e.g., CIFAR-10 and Imagenet) where the target DNN overfits its train set, but perform poorly on simpler datasets (0% TPR by both attacks on Fashion-MNIST, 2% and 0% TPR respectively by LiRA and EMIA on MNIST at 1% FPR). To address this, firstly, we unify current MI attacks by presenting a framework divided into three stages -- preparation, indication and decision. Secondly, we utilize the framework to propose two novel attacks: (1) Adversarial Membership Inference Attack (AMIA) efficiently utilizes the membership and the non-membership information of the subjects while adversarially minimizing a novel loss function, achieving 6% TPR on both Fashion-MNIST and MNIST datasets; and (2) Enhanced AMIA (E-AMIA) combines EMIA and AMIA to achieve 8% and 4% TPRs on Fashion-MNIST and MNIST datasets respectively, at 1% FPR. Thirdly, we introduce two novel augmented indicators that positively leverage the loss information in the Gaussian neighborhood of a subject. This improves TPR of all four attacks on average by 2.5% and 0.25% respectively on Fashion-MNIST and MNIST datasets at 1% FPR. Finally, we propose simple, yet novel, evaluation metric, the running TPR average (RTA) at a given FPR, that better distinguishes different MI attacks in the low FPR region. We also show that AMIA and E-AMIA are more transferable to the unknown DNNs (other than the target DNN) and are more robust to DP-SGD training as compared to LiRA and EMIA.
    Unbiased Pain Assessment through Wearables and EHR Data: Multi-attribute Fairness Loss-based CNN Approach. (arXiv:2307.05333v1 [eess.SP])
    The combination of diverse health data (IoT, EHR, and clinical surveys) and scalable-adaptable Artificial Intelligence (AI), has enabled the discovery of physical, behavioral, and psycho-social indicators of pain status. Despite the hype and promise to fundamentally alter the healthcare system with technological advancements, much AI adoption in clinical pain evaluation has been hampered by the heterogeneity of the problem itself and other challenges, such as personalization and fairness. Studies have revealed that many AI (i.e., machine learning or deep learning) models display biases and discriminate against specific population segments (such as those based on gender or ethnicity), which breeds skepticism among medical professionals about AI adaptability. In this paper, we propose a Multi-attribute Fairness Loss (MAFL) based CNN model that aims to account for any sensitive attributes included in the data and fairly predict patients' pain status while attempting to minimize the discrepancies between privileged and unprivileged groups. In order to determine whether the trade-off between accuracy and fairness can be satisfied, we compare the proposed model with well-known existing mitigation procedures, and studies reveal that the implemented model performs favorably in contrast to state-of-the-art methods. Utilizing NIH All-Of-US data, where a cohort of 868 distinct individuals with wearables and EHR data gathered over 1500 days has been taken into consideration to analyze our suggested fair pain assessment system.
    A Deep Dive into Perturbations as Evaluation Technique for Time Series XAI. (arXiv:2307.05104v1 [cs.LG])
    Explainable Artificial Intelligence (XAI) has gained significant attention recently as the demand for transparency and interpretability of machine learning models has increased. In particular, XAI for time series data has become increasingly important in finance, healthcare, and climate science. However, evaluating the quality of explanations, such as attributions provided by XAI techniques, remains challenging. This paper provides an in-depth analysis of using perturbations to evaluate attributions extracted from time series models. A perturbation analysis involves systematically modifying the input data and evaluating the impact on the attributions generated by the XAI method. We apply this approach to several state-of-the-art XAI techniques and evaluate their performance on three time series classification datasets. Our results demonstrate that the perturbation analysis approach can effectively evaluate the quality of attributions and provide insights into the strengths and limitations of XAI techniques. Such an approach can guide the selection of XAI methods for time series data, e.g., focusing on return time rather than precision, and facilitate the development of more reliable and interpretable machine learning models for time series analysis.
    Application of data engineering approaches to address challenges in microbiome data for optimal medical decision-making. (arXiv:2307.00033v2 [q-bio.QM] UPDATED)
    The human gut microbiota is known to contribute to numerous physiological functions of the body and also implicated in a myriad of pathological conditions. Prolific research work in the past few decades have yielded valuable information regarding the relative taxonomic distribution of gut microbiota. Unfortunately, the microbiome data suffers from class imbalance and high dimensionality issues that must be addressed. In this study, we have implemented data engineering algorithms to address the above-mentioned issues inherent to microbiome data. Four standard machine learning classifiers (logistic regression (LR), support vector machines (SVM), random forests (RF), and extreme gradient boosting (XGB) decision trees) were implemented on a previously published dataset. The issue of class imbalance and high dimensionality of the data was addressed through synthetic minority oversampling technique (SMOTE) and principal component analysis (PCA). Our results indicate that ensemble classifiers (RF and XGB decision trees) exhibit superior classification accuracy in predicting the host phenotype. The application of PCA significantly reduced testing time while maintaining high classification accuracy. The highest classification accuracy was obtained at the levels of species for most classifiers. The prototype employed in the study addresses the issues inherent to microbiome datasets and could be highly beneficial for providing personalized medicine.
    Transaction Fraud Detection via Spatial-Temporal-Aware Graph Transformer. (arXiv:2307.05121v1 [cs.LG])
    How to obtain informative representations of transactions and then perform the identification of fraudulent transactions is a crucial part of ensuring financial security. Recent studies apply Graph Neural Networks (GNNs) to the transaction fraud detection problem. Nevertheless, they encounter challenges in effectively learning spatial-temporal information due to structural limitations. Moreover, few prior GNN-based detectors have recognized the significance of incorporating global information, which encompasses similar behavioral patterns and offers valuable insights for discriminative representation learning. Therefore, we propose a novel heterogeneous graph neural network called Spatial-Temporal-Aware Graph Transformer (STA-GT) for transaction fraud detection problems. Specifically, we design a temporal encoding strategy to capture temporal dependencies and incorporate it into the graph neural network framework, enhancing spatial-temporal information modeling and improving expressive ability. Furthermore, we introduce a transformer module to learn local and global information. Pairwise node-node interactions overcome the limitation of the GNN structure and build up the interactions with the target node and long-distance ones. Experimental results on two financial datasets compared to general GNN models and GNN-based fraud detectors demonstrate that our proposed method STA-GT is effective on the transaction fraud detection task.
    Classification of sleep stages from EEG, EOG and EMG signals by SSNet. (arXiv:2307.05373v1 [eess.SP])
    Classification of sleep stages plays an essential role in diagnosing sleep-related diseases including Sleep Disorder Breathing (SDB) disease. In this study, we propose an end-to-end deep learning architecture, named SSNet, which comprises of two deep learning networks based on Convolutional Neuron Networks (CNN) and Long Short Term Memory (LSTM). Both deep learning networks extract features from the combination of Electrooculogram (EOG), Electroencephalogram (EEG), and Electromyogram (EMG) signals, as each signal has distinct features that help in the classification of sleep stages. The features produced by the two-deep learning networks are concatenated to pass to the fully connected layer for the classification. The performance of our proposed model is evaluated by using two public datasets Sleep-EDF Expanded dataset and ISRUC-Sleep dataset. The accuracy and Kappa coefficient are 96.36% and 93.40% respectively, for classifying three classes of sleep stages using Sleep-EDF Expanded dataset. Whereas, the accuracy and Kappa coefficient are 96.57% and 83.05% respectively for five classes of sleep stages using Sleep-EDF Expanded dataset. Our model achieves the best performance in classifying sleep stages when compared with the state-of-the-art techniques.
    Multi-Task Learning to Enhance Generazability of Neural Network Equalizers in Coherent Optical Systems. (arXiv:2307.05374v1 [eess.SP])
    For the first time, multi-task learning is proposed to improve the flexibility of NN-based equalizers in coherent systems. A "single" NN-based equalizer improves Q-factor by up to 4 dB compared to CDC, without re-training, even with variations in launch power, symbol rate, or transmission distance.
    Fast Neural Network Inference on FPGAs for Triggering on Long-Lived Particles at Colliders. (arXiv:2307.05152v1 [hep-ex])
    Experimental particle physics demands a sophisticated trigger and acquisition system capable to efficiently retain the collisions of interest for further investigation. Heterogeneous computing with the employment of FPGA cards may emerge as a trending technology for the triggering strategy of the upcoming high-luminosity program of the Large Hadron Collider at CERN. In this context, we present two machine-learning algorithms for selecting events where neutral long-lived particles decay within the detector volume studying their accuracy and inference time when accelerated on commercially available Xilinx FPGA accelerator cards. The inference time is also confronted with a CPU- and GPU-based hardware setup. The proposed new algorithms are proven efficient for the considered benchmark physics scenario and their accuracy is found to not degrade when accelerated on the FPGA cards. The results indicate that all tested architectures fit within the latency requirements of a second-level trigger farm and that exploiting accelerator technologies for real-time processing of particle-physics collisions is a promising research field that deserves additional investigations, in particular with machine-learning models with a large number of trainable parameters.
    Decorrelation using Optimal Transport. (arXiv:2307.05187v1 [hep-ph])
    Being able to decorrelate a feature space from protected attributes is an area of active research and study in ethics, fairness, and also natural sciences. We introduce a novel decorrelation method using Convex Neural Optimal Transport Solvers (Cnots), that is able to decorrelate continuous feature space against protected attributes with optimal transport. We demonstrate how well it performs in the context of jet classification in high energy physics, where classifier scores are desired to be decorrelated from the mass of a jet. The decorrelation achieved in binary classification approaches the levels achieved by the state-of-the-art using conditional normalising flows. When moving to multiclass outputs the optimal transport approach performs significantly better than the state-of-the-art, suggesting substantial gains at decorrelating multidimensional feature spaces.
    $\ell_p$-Regression in the Arbitrary Partition Model of Communication. (arXiv:2307.05117v1 [cs.DS])
    We consider the randomized communication complexity of the distributed $\ell_p$-regression problem in the coordinator model, for $p\in (0,2]$. In this problem, there is a coordinator and $s$ servers. The $i$-th server receives $A^i\in\{-M, -M+1, \ldots, M\}^{n\times d}$ and $b^i\in\{-M, -M+1, \ldots, M\}^n$ and the coordinator would like to find a $(1+\epsilon)$-approximate solution to $\min_{x\in\mathbb{R}^n} \|(\sum_i A^i)x - (\sum_i b^i)\|_p$. Here $M \leq \mathrm{poly}(nd)$ for convenience. This model, where the data is additively shared across servers, is commonly referred to as the arbitrary partition model. We obtain significantly improved bounds for this problem. For $p = 2$, i.e., least squares regression, we give the first optimal bound of $\tilde{\Theta}(sd^2 + sd/\epsilon)$ bits. For $p \in (1,2)$,we obtain an $\tilde{O}(sd^2/\epsilon + sd/\mathrm{poly}(\epsilon))$ upper bound. Notably, for $d$ sufficiently large, our leading order term only depends linearly on $1/\epsilon$ rather than quadratically. We also show communication lower bounds of $\Omega(sd^2 + sd/\epsilon^2)$ for $p\in (0,1]$ and $\Omega(sd^2 + sd/\epsilon)$ for $p\in (1,2]$. Our bounds considerably improve previous bounds due to (Woodruff et al. COLT, 2013) and (Vempala et al., SODA, 2020).
    Contextual Pre-Planning on Reward Machine Abstractions for Enhanced Transfer in Deep Reinforcement Learning. (arXiv:2307.05209v1 [cs.AI])
    Recent studies show that deep reinforcement learning (DRL) agents tend to overfit to the task on which they were trained and fail to adapt to minor environment changes. To expedite learning when transferring to unseen tasks, we propose a novel approach to representing the current task using reward machines (RM), state machine abstractions that induce subtasks based on the current task's rewards and dynamics. Our method provides agents with symbolic representations of optimal transitions from their current abstract state and rewards them for achieving these transitions. These representations are shared across tasks, allowing agents to exploit knowledge of previously encountered symbols and transitions, thus enhancing transfer. Our empirical evaluation shows that our representations improve sample efficiency and few-shot transfer in a variety of domains.
    SimpleMTOD: A Simple Language Model for Multimodal Task-Oriented Dialogue with Symbolic Scene Representation. (arXiv:2307.04907v1 [cs.CL])
    SimpleMTOD is a simple language model which recasts several sub-tasks in multimodal task-oriented dialogues as sequence prediction tasks. SimpleMTOD is built on a large-scale transformer-based auto-regressive architecture, which has already proven to be successful in uni-modal task-oriented dialogues, and effectively leverages transfer learning from pre-trained GPT-2. In-order to capture the semantics of visual scenes, we introduce both local and de-localized tokens for objects within a scene. De-localized tokens represent the type of an object rather than the specific object itself and so possess a consistent meaning across the dataset. SimpleMTOD achieves a state-of-the-art BLEU score (0.327) in the Response Generation sub-task of the SIMMC 2.0 test-std dataset while performing on par in other multimodal sub-tasks: Disambiguation, Coreference Resolution, and Dialog State Tracking. This is despite taking a minimalist approach for extracting visual (and non-visual) information. In addition the model does not rely on task-specific architectural changes such as classification heads.
    Control as Probabilistic Inference as an Emergent Communication Mechanism in Multi-Agent Reinforcement Learning. (arXiv:2307.05004v1 [cs.AI])
    This paper proposes a generative probabilistic model integrating emergent communication and multi-agent reinforcement learning. The agents plan their actions by probabilistic inference, called control as inference, and communicate using messages that are latent variables and estimated based on the planned actions. Through these messages, each agent can send information about its actions and know information about the actions of another agent. Therefore, the agents change their actions according to the estimated messages to achieve cooperative tasks. This inference of messages can be considered as communication, and this procedure can be formulated by the Metropolis-Hasting naming game. Through experiments in the grid world environment, we show that the proposed PGM can infer meaningful messages to achieve the cooperative task.
    On the Use of Self-Supervised Speech Representations in Spontaneous Speech Synthesis. (arXiv:2307.05132v1 [eess.AS])
    Self-supervised learning (SSL) speech representations learned from large amounts of diverse, mixed-quality speech data without transcriptions are gaining ground in many speech technology applications. Prior work has shown that SSL is an effective intermediate representation in two-stage text-to-speech (TTS) for both read and spontaneous speech. However, it is still not clear which SSL and which layer from each SSL model is most suited for spontaneous TTS. We address this shortcoming by extending the scope of comparison for SSL in spontaneous TTS to 6 different SSLs and 3 layers within each SSL. Furthermore, SSL has also shown potential in predicting the mean opinion scores (MOS) of synthesized speech, but this has only been done in read-speech MOS prediction. We extend an SSL-based MOS prediction framework previously developed for scoring read speech synthesis and evaluate its performance on synthesized spontaneous speech. All experiments are conducted twice on two different spontaneous corpora in order to find generalizable trends. Overall, we present comprehensive experimental results on the use of SSL in spontaneous TTS and MOS prediction to further quantify and understand how SSL can be used in spontaneous TTS. Audios samples: https://www.speech.kth.se/tts-demos/sp_ssl_tts
    Using Linear Regression for Iteratively Training Neural Networks. (arXiv:2307.05189v1 [cs.LG])
    We present a simple linear regression based approach for learning the weights and biases of a neural network, as an alternative to standard gradient based backpropagation. The present work is exploratory in nature, and we restrict the description and experiments to (i) simple feedforward neural networks, (ii) scalar (single output) regression problems, and (iii) invertible activation functions. However, the approach is intended to be extensible to larger, more complex architectures. The key idea is the observation that the input to every neuron in a neural network is a linear combination of the activations of neurons in the previous layer, as well as the parameters (weights and biases) of the layer. If we are able to compute the ideal total input values to every neuron by working backwards from the output, we can formulate the learning problem as a linear least squares problem which iterates between updating the parameters and the activation values. We present an explicit algorithm that implements this idea, and we show that (at least for simple problems) the approach is more stable and faster than gradient-based backpropagation.
    Feature Activation Map: Visual Explanation of Deep Learning Models for Image Classification. (arXiv:2307.05017v1 [cs.CV])
    Decisions made by convolutional neural networks(CNN) can be understood and explained by visualizing discriminative regions on images. To this end, Class Activation Map (CAM) based methods were proposed as powerful interpretation tools, making the prediction of deep learning models more explainable, transparent, and trustworthy. However, all the CAM-based methods (e.g., CAM, Grad-CAM, and Relevance-CAM) can only be used for interpreting CNN models with fully-connected (FC) layers as a classifier. It is worth noting that many deep learning models classify images without FC layers, e.g., few-shot learning image classification, contrastive learning image classification, and image retrieval tasks. In this work, a post-hoc interpretation tool named feature activation map (FAM) is proposed, which can interpret deep learning models without FC layers as a classifier. In the proposed FAM algorithm, the channel-wise contribution weights are derived from the similarity scores between two image embeddings. The activation maps are linearly combined with the corresponding normalized contribution weights, forming the explanation map for visualization. The quantitative and qualitative experiments conducted on ten deep learning models for few-shot image classification, contrastive learning image classification and image retrieval tasks demonstrate the effectiveness of the proposed FAM algorithm.
    On the Effectiveness of Speech Self-supervised Learning for Music. (arXiv:2307.05161v1 [cs.SD])
    Self-supervised learning (SSL) has shown promising results in various speech and natural language processing applications. However, its efficacy in music information retrieval (MIR) still remains largely unexplored. While previous SSL models pre-trained on music recordings may have been mostly closed-sourced, recent speech models such as wav2vec2.0 have shown promise in music modelling. Nevertheless, research exploring the effectiveness of applying speech SSL models to music recordings has been limited. We explore the music adaption of SSL with two distinctive speech-related models, data2vec1.0 and Hubert, and refer to them as music2vec and musicHuBERT, respectively. We train $12$ SSL models with 95M parameters under various pre-training configurations and systematically evaluate the MIR task performances with 13 different MIR tasks. Our findings suggest that training with music data can generally improve performance on MIR tasks, even when models are trained using paradigms designed for speech. However, we identify the limitations of such existing speech-oriented designs, especially in modelling polyphonic information. Based on the experimental results, empirical suggestions are also given for designing future musical SSL strategies and paradigms.
    CREPE: Learnable Prompting With CLIP Improves Visual Relationship Prediction. (arXiv:2307.04838v1 [cs.CV])
    In this paper, we explore the potential of Vision-Language Models (VLMs), specifically CLIP, in predicting visual object relationships, which involves interpreting visual features from images into language-based relations. Current state-of-the-art methods use complex graphical models that utilize language cues and visual features to address this challenge. We hypothesize that the strong language priors in CLIP embeddings can simplify these graphical models paving for a simpler approach. We adopt the UVTransE relation prediction framework, which learns the relation as a translational embedding with subject, object, and union box embeddings from a scene. We systematically explore the design of CLIP-based subject, object, and union-box representations within the UVTransE framework and propose CREPE (CLIP Representation Enhanced Predicate Estimation). CREPE utilizes text-based representations for all three bounding boxes and introduces a novel contrastive training strategy to automatically infer the text prompt for union-box. Our approach achieves state-of-the-art performance in predicate estimation, mR@5 27.79, and mR@20 31.95 on the Visual Genome benchmark, achieving a 15.3\% gain in performance over recent state-of-the-art at mR@20. This work demonstrates CLIP's effectiveness in object relation prediction and encourages further research on VLMs in this challenging domain.
    A Theory of Bounded Inductive Rationality. (arXiv:2307.05068v1 [cs.AI])
    The dominant theories of rational choice assume logical omniscience. That is, they assume that when facing a decision problem, an agent can perform all relevant computations and determine the truth value of all relevant logical/mathematical claims. This assumption is unrealistic when, for example, we offer bets on remote digits of pi or when an agent faces a computationally intractable planning problem. Furthermore, the assumption of logical omniscience creates contradictions in cases where the environment can contain descriptions of the agent itself. Importantly, strategic interactions as studied in game theory are decision problems in which a rational agent is predicted by its environment (the other players). In this paper, we develop a theory of rational decision making that does not assume logical omniscience. We consider agents who repeatedly face decision problems (including ones like betting on digits of pi or games against other agents). The main contribution of this paper is to provide a sensible theory of rationality for such agents. Roughly, we require that a boundedly rational inductive agent tests each efficiently computable hypothesis infinitely often and follows those hypotheses that keep their promises of high rewards. We then prove that agents that are rational in this sense have other desirable properties. For example, they learn to value random and pseudo-random lotteries at their expected reward. Finally, we consider strategic interactions between different agents and prove a folk theorem for what strategies bounded rational inductive agents can converge to.
    Enhancing Continuous Time Series Modelling with a Latent ODE-LSTM Approach. (arXiv:2307.05126v1 [cs.LG])
    Due to their dynamic properties such as irregular sampling rate and high-frequency sampling, Continuous Time Series (CTS) are found in many applications. Since CTS with irregular sampling rate are difficult to model with standard Recurrent Neural Networks (RNNs), RNNs have been generalised to have continuous-time hidden dynamics defined by a Neural Ordinary Differential Equation (Neural ODE), leading to the ODE-RNN model. Another approach that provides a better modelling is that of the Latent ODE model, which constructs a continuous-time model where a latent state is defined at all times. The Latent ODE model uses a standard RNN as the encoder and a Neural ODE as the decoder. However, since the RNN encoder leads to difficulties with missing data and ill-defined latent variables, a Latent ODE-RNN model has recently been proposed that uses a ODE-RNN model as the encoder instead. Both the Latent ODE and Latent ODE-RNN models are difficult to train due to the vanishing and exploding gradients problem. To overcome this problem, the main contribution of this paper is to propose and illustrate a new model based on a new Latent ODE using an ODE-LSTM (Long Short-Term Memory) network as an encoder -- the Latent ODE-LSTM model. To limit the growth of the gradients the Norm Gradient Clipping strategy was embedded on the Latent ODE-LSTM model. The performance evaluation of the new Latent ODE-LSTM (with and without Norm Gradient Clipping) for modelling CTS with regular and irregular sampling rates is then demonstrated. Numerical experiments show that the new Latent ODE-LSTM performs better than Latent ODE-RNNs and can avoid the vanishing and exploding gradients during training.
    TIAM -- A Metric for Evaluating Alignment in Text-to-Image Generation. (arXiv:2307.05134v1 [cs.CV])
    The progress in the generation of synthetic images has made it crucial to assess their quality. While several metrics have been proposed to assess the rendering of images, it is crucial for Text-to-Image (T2I) models, which generate images based on a prompt, to consider additional aspects such as to which extent the generated image matches the important content of the prompt. Moreover, although the generated images usually result from a random starting point, the influence of this one is generally not considered. In this article, we propose a new metric based on prompt templates to study the alignment between the content specified in the prompt and the corresponding generated images. It allows us to better characterize the alignment in terms of the type of the specified objects, their number, and their color. We conducted a study on several recent T2I models about various aspects. An additional interesting result we obtained with our approach is that image quality can vary drastically depending on the latent noise used as a seed for the images. We also quantify the influence of the number of concepts in the prompt, their order as well as their (color) attributes. Finally, our method allows us to identify some latent seeds that produce better images than others, opening novel directions of research on this understudied topic.
    Diagnosing Model Performance Under Distribution Shift. (arXiv:2303.02011v4 [stat.ML] UPDATED)
    Prediction models can perform poorly when deployed to target distributions different from the training distribution. To understand these operational failure modes, we develop a method, called DIstribution Shift DEcomposition (DISDE), to attribute a drop in performance to different types of distribution shifts. Our approach decomposes the performance drop into terms for 1) an increase in harder but frequently seen examples from training, 2) changes in the relationship between features and outcomes, and 3) poor performance on examples infrequent or unseen during training. These terms are defined by fixing a distribution on $X$ while varying the conditional distribution of $Y \mid X$ between training and target, or by fixing the conditional distribution of $Y \mid X$ while varying the distribution on $X$. In order to do this, we define a hypothetical distribution on $X$ consisting of values common in both training and target, over which it is easy to compare $Y \mid X$ and thus predictive performance. We estimate performance on this hypothetical distribution via reweighting methods. Empirically, we show how our method can 1) inform potential modeling improvements across distribution shifts for employment prediction on tabular census data, and 2) help to explain why certain domain adaptation methods fail to improve model performance for satellite image classification.
    Benchmarking Algorithms for Federated Domain Generalization. (arXiv:2307.04942v1 [cs.LG])
    While prior domain generalization (DG) benchmarks consider train-test dataset heterogeneity, we evaluate Federated DG which introduces federated learning (FL) specific challenges. Additionally, we explore domain-based heterogeneity in clients' local datasets - a realistic Federated DG scenario. Prior Federated DG evaluations are limited in terms of the number or heterogeneity of clients and dataset diversity. To address this gap, we propose an Federated DG benchmark methodology that enables control of the number and heterogeneity of clients and provides metrics for dataset difficulty. We then apply our methodology to evaluate 13 Federated DG methods, which include centralized DG methods adapted to the FL context, FL methods that handle client heterogeneity, and methods designed specifically for Federated DG. Our results suggest that despite some progress, there remain significant performance gaps in Federated DG particularly when evaluating with a large number of clients, high client heterogeneity, or more realistic datasets. Please check our extendable benchmark code here: https://github.com/inouye-lab/FedDG_Benchmark.
    Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation. (arXiv:2307.04988v1 [cs.LG])
    The practical utility of causality in decision-making is widely recognized, with causal discovery and inference being inherently intertwined. Nevertheless, a notable gap exists in the evaluation of causal discovery methods, where insufficient emphasis is placed on downstream inference. To address this gap, we evaluate six established baseline causal discovery methods and a newly proposed method based on GFlowNets, on the downstream task of treatment effect estimation. Through the implementation of a robust evaluation procedure, we offer valuable insights into the efficacy of these causal discovery methods for treatment effect estimation, considering both synthetic and real-world scenarios, as well as low-data scenarios. Furthermore, the results of our study demonstrate that GFlowNets possess the capability to effectively capture a wide range of useful and diverse ATE modes.
    Onion Universe Algorithm: Applications in Weakly Supervised Learning. (arXiv:2307.04870v1 [cs.LG])
    We introduce Onion Universe Algorithm (OUA), a novel classification method in ensemble learning. In particular, we show its applicability as a label model for weakly supervised learning. OUA offers simplicity in implementation, computational efficiency, and does not rely on any assumptions regarding the data or weak signals. The model is well suited for scenarios where fully labeled data is not available. Our method is built upon geometrical interpretation of the space spanned by weak signals. Empirical results support our analysis of the hidden geometric structure underlying general set of weak signals and also illustrates that OUA works well in practice. We show empirical evidence that OUA performs favorably on common benchmark datasets compared to existing label models for weakly supervised learning.
    Reinforcement Learning with Non-Cumulative Objective. (arXiv:2307.04957v1 [cs.LG])
    In reinforcement learning, the objective is almost always defined as a \emph{cumulative} function over the rewards along the process. However, there are many optimal control and reinforcement learning problems in various application fields, especially in communications and networking, where the objectives are not naturally expressed as summations of the rewards. In this paper, we recognize the prevalence of non-cumulative objectives in various problems, and propose a modification to existing algorithms for optimizing such objectives. Specifically, we dive into the fundamental building block for many optimal control and reinforcement learning algorithms: the Bellman optimality equation. To optimize a non-cumulative objective, we replace the original summation operation in the Bellman update rule with a generalized operation corresponding to the objective. Furthermore, we provide sufficient conditions on the form of the generalized operation as well as assumptions on the Markov decision process under which the globally optimal convergence of the generalized Bellman updates can be guaranteed. We demonstrate the idea experimentally with the bottleneck objective, i.e., the objectives determined by the minimum reward along the process, on classical optimal control and reinforcement learning tasks, as well as on two network routing problems on maximizing the flow rates.
    Improving Fairness of Graph Neural Networks: A Graph Counterfactual Perspective. (arXiv:2307.04937v1 [cs.LG])
    Graph neural networks have shown great ability in representation (GNNs) learning on graphs, facilitating various tasks. Despite their great performance in modeling graphs, recent works show that GNNs tend to inherit and amplify the bias from training data, causing concerns of the adoption of GNNs in high-stake scenarios. Hence, many efforts have been taken for fairness-aware GNNs. However, most existing fair GNNs learn fair node representations by adopting statistical fairness notions, which may fail to alleviate bias in the presence of statistical anomalies. Motivated by causal theory, there are several attempts utilizing graph counterfactual fairness to mitigate root causes of unfairness. However, these methods suffer from non-realistic counterfactuals obtained by perturbation or generation. In this paper, we take a causal view on fair graph learning problem. Guided by the casual analysis, we propose a novel framework CAF, which can select counterfactuals from training data to avoid non-realistic counterfactuals and adopt selected counterfactuals to learn fair node representations for node classification task. Extensive experiments on synthetic and real-world datasets show the effectiveness of CAF.
    DDGM: Solving inverse problems by Diffusive Denoising of Gradient-based Minimization. (arXiv:2307.04946v1 [cs.CV])
    Inverse problems generally require a regularizer or prior for a good solution. A recent trend is to train a convolutional net to denoise images, and use this net as a prior when solving the inverse problem. Several proposals depend on a singular value decomposition of the forward operator, and several others backpropagate through the denoising net at runtime. Here we propose a simpler approach that combines the traditional gradient-based minimization of reconstruction error with denoising. Noise is also added at each step, so the iterative dynamics resembles a Langevin or diffusion process. Both the level of added noise and the size of the denoising step decay exponentially with time. We apply our method to the problem of tomographic reconstruction from electron micrographs acquired at multiple tilt angles. With empirical studies using simulated tilt views, we find parameter settings for our method that produce good results. We show that high accuracy can be achieved with as few as 50 denoising steps. We also compare with DDRM and DPS, more complex diffusion methods of the kinds mentioned above. These methods are less accurate (as measured by MSE and SSIM) for our tomography problem, even after the generation hyperparameters are optimized. Finally we extend our method to reconstruction of arbitrary-sized images and show results on 128 $\times$ 1568 pixel images
    SigOpt Mulch: An Intelligent System for AutoML of Gradient Boosted Trees. (arXiv:2307.04849v1 [cs.LG])
    Gradient boosted trees (GBTs) are ubiquitous models used by researchers, machine learning (ML) practitioners, and data scientists because of their robust performance, interpretable behavior, and ease-of-use. One critical challenge in training GBTs is the tuning of their hyperparameters. In practice, selecting these hyperparameters is often done manually. Recently, the ML community has advocated for tuning hyperparameters through black-box optimization and developed state-of-the-art systems to do so. However, applying such systems to tune GBTs suffers from two drawbacks. First, these systems are not \textit{model-aware}, rather they are designed to apply to a \textit{generic} model; this leaves significant optimization performance on the table. Second, using these systems requires \textit{domain knowledge} such as the choice of hyperparameter search space, which is an antithesis to the automatic experimentation that black-box optimization aims to provide. In this paper, we present SigOpt Mulch, a model-aware hyperparameter tuning system specifically designed for automated tuning of GBTs that provides two improvements over existing systems. First, Mulch leverages powerful techniques in metalearning and multifidelity optimization to perform model-aware hyperparameter optimization. Second, it automates the process of learning performant hyperparameters by making intelligent decisions about the optimization search space, thus reducing the need for user domain knowledge. These innovations allow Mulch to identify good GBT hyperparameters far more efficiently -- and in a more seamless and user-friendly way -- than existing black-box hyperparameter tuning systems.
    Dynamics of Temporal Difference Reinforcement Learning. (arXiv:2307.04841v1 [stat.ML])
    Reinforcement learning has been successful across several applications in which agents have to learn to act in environments with sparse feedback. However, despite this empirical success there is still a lack of theoretical understanding of how the parameters of reinforcement learning models and the features used to represent states interact to control the dynamics of learning. In this work, we use concepts from statistical physics, to study the typical case learning curves for temporal difference learning of a value function with linear function approximators. Our theory is derived under a Gaussian equivalence hypothesis where averages over the random trajectories are replaced with temporally correlated Gaussian feature averages and we validate our assumptions on small scale Markov Decision Processes. We find that the stochastic semi-gradient noise due to subsampling the space of possible episodes leads to significant plateaus in the value error, unlike in traditional gradient descent dynamics. We study how learning dynamics and plateaus depend on feature structure, learning rate, discount factor, and reward function. We then analyze how strategies like learning rate annealing and reward shaping can favorably alter learning dynamics and plateaus. To conclude, our work introduces new tools to open a new direction towards developing a theory of learning dynamics in reinforcement learning.
    Learning to Solve Constraint Satisfaction Problems with Recurrent Transformer. (arXiv:2307.04895v1 [cs.AI])
    Constraint satisfaction problems (CSPs) are about finding values of variables that satisfy the given constraints. We show that Transformer extended with recurrence is a viable approach to learning to solve CSPs in an end-to-end manner, having clear advantages over state-of-the-art methods such as Graph Neural Networks, SATNet, and some neuro-symbolic models. With the ability of Transformer to handle visual input, the proposed Recurrent Transformer can straightforwardly be applied to visual constraint reasoning problems while successfully addressing the symbol grounding problem. We also show how to leverage deductive knowledge of discrete constraints in the Transformer's inductive learning to achieve sample-efficient learning and semi-supervised learning for CSPs.
    Route, Interpret, Repeat: Blurring the line between post hoc explainability and interpretable models. (arXiv:2307.05350v1 [cs.LG])
    The current approach to ML model design is either to choose a flexible Blackbox model and explain it post hoc or to start with an interpretable model. Blackbox models are flexible but difficult to explain, whereas interpretable models are designed to be explainable. However, developing interpretable models necessitates extensive ML knowledge, and the resulting models tend to be less flexible, offering potentially subpar performance compared to their Blackbox equivalents. This paper aims to blur the distinction between a post hoc explanation of a BlackBox and constructing interpretable models. We propose beginning with a flexible BlackBox model and gradually \emph{carving out} a mixture of interpretable models and a \emph{residual network}. Our design identifies a subset of samples and \emph{routes} them through the interpretable models. The remaining samples are routed through a flexible residual network. We adopt First Order Logic (FOL) as the interpretable model's backbone, which provides basic reasoning on concepts retrieved from the BlackBox model. On the residual network, we repeat the method until the proportion of data explained by the residual network falls below a desired threshold. Our approach offers several advantages. First, the mixture of interpretable and flexible residual networks results in almost no compromise in performance. Second, the route, interpret, and repeat approach yields a highly flexible interpretable model. Our extensive experiment demonstrates the performance of the model on various datasets. We show that by editing the FOL model, we can fix the shortcut learned by the original BlackBox model. Finally, our method provides a framework for a hybrid symbolic-connectionist network that is simple to train and adaptable to many applications.
    SHAP@k:Efficient and Probably Approximately Correct (PAC) Identification of Top-k Features. (arXiv:2307.04850v1 [cs.LG])
    The SHAP framework provides a principled method to explain the predictions of a model by computing feature importance. Motivated by applications in finance, we introduce the Top-k Identification Problem (TkIP), where the objective is to identify the k features with the highest SHAP values. While any method to compute SHAP values with uncertainty estimates (such as KernelSHAP and SamplingSHAP) can be trivially adapted to solve TkIP, doing so is highly sample inefficient. The goal of our work is to improve the sample efficiency of existing methods in the context of solving TkIP. Our key insight is that TkIP can be framed as an Explore-m problem--a well-studied problem related to multi-armed bandits (MAB). This connection enables us to improve sample efficiency by leveraging two techniques from the MAB literature: (1) a better stopping-condition (to stop sampling) that identifies when PAC (Probably Approximately Correct) guarantees have been met and (2) a greedy sampling scheme that judiciously allocates samples between different features. By adopting these methods we develop KernelSHAP@k and SamplingSHAP@k to efficiently solve TkIP, offering an average improvement of $5\times$ in sample-efficiency and runtime across most common credit related datasets.
    Fed-CPrompt: Contrastive Prompt for Rehearsal-Free Federated Continual Learning. (arXiv:2307.04869v1 [cs.LG])
    Federated continual learning (FCL) learns incremental tasks over time from confidential datasets distributed across clients. This paper focuses on rehearsal-free FCL, which has severe forgetting issues when learning new tasks due to the lack of access to historical task data. To address this issue, we propose Fed-CPrompt based on prompt learning techniques to obtain task-specific prompts in a communication-efficient way. Fed-CPrompt introduces two key components, asynchronous prompt learning, and contrastive continual loss, to handle asynchronous task arrival and heterogeneous data distributions in FCL, respectively. Extensive experiments demonstrate the effectiveness of Fed-CPrompt in achieving SOTA rehearsal-free FCL performance.
    VisText: A Benchmark for Semantically Rich Chart Captioning. (arXiv:2307.05356v1 [cs.CV])
    Captions that describe or explain charts help improve recall and comprehension of the depicted data and provide a more accessible medium for people with visual disabilities. However, current approaches for automatically generating such captions struggle to articulate the perceptual or cognitive features that are the hallmark of charts (e.g., complex trends and patterns). In response, we introduce VisText: a dataset of 12,441 pairs of charts and captions that describe the charts' construction, report key statistics, and identify perceptual and cognitive phenomena. In VisText, a chart is available as three representations: a rasterized image, a backing data table, and a scene graph -- a hierarchical representation of a chart's visual elements akin to a web page's Document Object Model (DOM). To evaluate the impact of VisText, we fine-tune state-of-the-art language models on our chart captioning task and apply prefix-tuning to produce captions that vary the semantic content they convey. Our models generate coherent, semantically rich captions and perform on par with state-of-the-art chart captioning models across machine translation and text generation metrics. Through qualitative analysis, we identify six broad categories of errors that our models make that can inform future work.
    Forming Trees with Treeformers. (arXiv:2207.06960v2 [cs.CL] UPDATED)
    Human language is known to exhibit a nested, hierarchical structure, allowing us to form complex sentences out of smaller pieces. However, many state-of-the-art neural networks models such as Transformers have no explicit hierarchical structure in its architecture -- that is, they don't have an inductive bias toward hierarchical structure. Additionally, Transformers are known to perform poorly on compositional generalization tasks which require such structures. In this paper, we introduce Treeformer, a general-purpose encoder module inspired by the CKY algorithm which learns a composition operator and pooling function to construct hierarchical encodings for phrases and sentences. Our extensive experiments demonstrate the benefits of incorporating hierarchical structure into the Transformer and show significant improvements in compositional generalization as well as in downstream tasks such as machine translation, abstractive summarization, and various natural language understanding tasks.
    Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges. (arXiv:2211.08413v3 [cs.LG] UPDATED)
    In the last decade, Federated Learning (FL) has gained relevance in training collaborative models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the most common approach in the literature, where a central entity creates a global model. However, a centralized approach leads to increased latency due to bottlenecks, heightened vulnerability to system failures, and trustworthiness concerns affecting the entity responsible for the global model creation. Decentralized Federated Learning (DFL) emerged to address these concerns by promoting decentralized model aggregation and minimizing reliance on centralized architectures. However, despite the work done in DFL, the literature has not (i) studied the main aspects differentiating DFL and CFL; (ii) analyzed DFL frameworks to create and evaluate new solutions; and (iii) reviewed application scenarios using DFL. Thus, this article identifies and analyzes the main fundamentals of DFL in terms of federation architectures, topologies, communication mechanisms, security approaches, and key performance indicators. Additionally, the paper at hand explores existing mechanisms to optimize critical DFL fundamentals. Then, the most relevant features of the current DFL frameworks are reviewed and compared. After that, it analyzes the most used DFL application scenarios, identifying solutions based on the fundamentals and frameworks previously defined. Finally, the evolution of existing DFL solutions is studied to provide a list of trends, lessons learned, and open challenges.
    DRMC: A Generalist Model with Dynamic Routing for Multi-Center PET Image Synthesis. (arXiv:2307.05249v1 [eess.IV])
    Multi-center positron emission tomography (PET) image synthesis aims at recovering low-dose PET images from multiple different centers. The generalizability of existing methods can still be suboptimal for a multi-center study due to domain shifts, which result from non-identical data distribution among centers with different imaging systems/protocols. While some approaches address domain shifts by training specialized models for each center, they are parameter inefficient and do not well exploit the shared knowledge across centers. To address this, we develop a generalist model that shares architecture and parameters across centers to utilize the shared knowledge. However, the generalist model can suffer from the center interference issue, \textit{i.e.} the gradient directions of different centers can be inconsistent or even opposite owing to the non-identical data distribution. To mitigate such interference, we introduce a novel dynamic routing strategy with cross-layer connections that routes data from different centers to different experts. Experiments show that our generalist model with dynamic routing (DRMC) exhibits excellent generalizability across centers. Code and data are available at: https://github.com/Yaziwel/Multi-Center-PET-Image-Synthesis.
    Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators. (arXiv:2307.05358v1 [cs.LG])
    Federated learning has become a popular method to learn from decentralized heterogeneous data. Federated semi-supervised learning (FSSL) emerges to train models from a small fraction of labeled data due to label scarcity on decentralized clients. Existing FSSL methods assume independent and identically distributed (IID) labeled data across clients and consistent class distribution between labeled and unlabeled data within a client. This work studies a more practical and challenging scenario of FSSL, where data distribution is different not only across clients but also within a client between labeled and unlabeled data. To address this challenge, we propose a novel FSSL framework with dual regulators, FedDure.} FedDure lifts the previous assumption with a coarse-grained regulator (C-reg) and a fine-grained regulator (F-reg): C-reg regularizes the updating of the local model by tracking the learning effect on labeled data distribution; F-reg learns an adaptive weighting scheme tailored for unlabeled instances in each client. We further formulate the client model training as bi-level optimization that adaptively optimizes the model in the client with two regulators. Theoretically, we show the convergence guarantee of the dual regulators. Empirically, we demonstrate that FedDure is superior to the existing methods across a wide range of settings, notably by more than 11% on CIFAR-10 and CINIC-10 datasets.
    On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets. (arXiv:2307.05284v1 [cs.LG])
    Different distribution shifts require different algorithmic and operational interventions. Methodological research must be grounded by the specific shifts they address. Although nascent benchmarks provide a promising empirical foundation, they implicitly focus on covariate shifts, and the validity of empirical findings depends on the type of shift, e.g., previous observations on algorithmic performance can fail to be valid when the $Y|X$ distribution changes. We conduct a thorough investigation of natural shifts in 5 tabular datasets over 86,000 model configurations, and find that $Y|X$-shifts are most prevalent. To encourage researchers to develop a refined language for distribution shifts, we build WhyShift, an empirical testbed of curated real-world shifts where we characterize the type of shift we benchmark performance over. Since $Y|X$-shifts are prevalent in tabular settings, we identify covariate regions that suffer the biggest $Y|X$-shifts and discuss implications for algorithmic and data-based interventions. Our testbed highlights the importance of future research that builds an understanding of how distributions differ.
    Supervised Attention Using Homophily in Graph Neural Networks. (arXiv:2307.05217v1 [cs.LG])
    Graph neural networks have become the standard approach for dealing with learning problems on graphs. Among the different variants of graph neural networks, graph attention networks (GATs) have been applied with great success to different tasks. In the GAT model, each node assigns an importance score to its neighbors using an attention mechanism. However, similar to other graph neural networks, GATs aggregate messages from nodes that belong to different classes, and therefore produce node representations that are not well separated with respect to the different classes, which might hurt their performance. In this work, to alleviate this problem, we propose a new technique that can be incorporated into any graph attention model to encourage higher attention scores between nodes that share the same class label. We evaluate the proposed method on several node classification datasets demonstrating increased performance over standard baseline models.
    Geometric Neural Diffusion Processes. (arXiv:2307.05431v1 [stat.ML])
    Denoising diffusion models have proven to be a flexible and effective paradigm for generative modelling. Their recent extension to infinite dimensional Euclidean spaces has allowed for the modelling of stochastic processes. However, many problems in the natural sciences incorporate symmetries and involve data living in non-Euclidean spaces. In this work, we extend the framework of diffusion models to incorporate a series of geometric priors in infinite-dimension modelling. We do so by a) constructing a noising process which admits, as limiting distribution, a geometric Gaussian process that transforms under the symmetry group of interest, and b) approximating the score with a neural network that is equivariant w.r.t. this group. We show that with these conditions, the generative functional model admits the same symmetry. We demonstrate scalability and capacity of the model, using a novel Langevin-based conditional sampler, to fit complex scalar and vector fields, with Euclidean and spherical codomain, on synthetic and real-world weather data.
    Leveraging an Alignment Set in Tackling Instance-Dependent Label Noise. (arXiv:2307.04868v1 [cs.LG])
    Noisy training labels can hurt model performance. Most approaches that aim to address label noise assume label noise is independent from the input features. In practice, however, label noise is often feature or \textit{instance-dependent}, and therefore biased (i.e., some instances are more likely to be mislabeled than others). E.g., in clinical care, female patients are more likely to be under-diagnosed for cardiovascular disease compared to male patients. Approaches that ignore this dependence can produce models with poor discriminative performance, and in many healthcare settings, can exacerbate issues around health disparities. In light of these limitations, we propose a two-stage approach to learn in the presence instance-dependent label noise. Our approach utilizes \textit{\anchor points}, a small subset of data for which we know the observed and ground truth labels. On several tasks, our approach leads to consistent improvements over the state-of-the-art in discriminative performance (AUROC) while mitigating bias (area under the equalized odds curve, AUEOC). For example, when predicting acute respiratory failure onset on the MIMIC-III dataset, our approach achieves a harmonic mean (AUROC and AUEOC) of 0.84 (SD [standard deviation] 0.01) while that of the next best baseline is 0.81 (SD 0.01). Overall, our approach improves accuracy while mitigating potential bias compared to existing approaches in the presence of instance-dependent label noise.
    PowerFusion: A Tensor Compiler with Explicit Data Movement Description and Instruction-level Graph IR. (arXiv:2307.04995v1 [cs.LG])
    Deep neural networks (DNNs) are of critical use in different domains. To accelerate DNN computation, tensor compilers are proposed to generate efficient code on different domain-specific accelerators. Existing tensor compilers mainly focus on optimizing computation efficiency. However, memory access is becoming a key performance bottleneck because the computational performance of accelerators is increasing much faster than memory performance. The lack of direct description of memory access and data dependence in current tensor compilers' intermediate representation (IR) brings significant challenges to generate memory-efficient code. In this paper, we propose IntelliGen, a tensor compiler that can generate high-performance code for memory-intensive operators by considering both computation and data movement optimizations. IntelliGen represent a DNN program using GIR, which includes primitives indicating its computation, data movement, and parallel strategies. This information will be further composed as an instruction-level dataflow graph to perform holistic optimizations by searching different memory access patterns and computation operations, and generating memory-efficient code on different hardware. We evaluate IntelliGen on NVIDIA GPU, AMD GPU, and Cambricon MLU, showing speedup up to 1.97x, 2.93x, and 16.91x(1.28x, 1.23x, and 2.31x on average), respectively, compared to current most performant frameworks.
    Estimating label quality and errors in semantic segmentation data via any model. (arXiv:2307.05080v1 [cs.LG])
    The labor-intensive annotation process of semantic segmentation datasets is often prone to errors, since humans struggle to label every pixel correctly. We study algorithms to automatically detect such annotation errors, in particular methods to score label quality, such that the images with the lowest scores are least likely to be correctly labeled. This helps prioritize what data to review in order to ensure a high-quality training/evaluation dataset, which is critical in sensitive applications such as medical imaging and autonomous vehicles. Widely applicable, our label quality scores rely on probabilistic predictions from a trained segmentation model -- any model architecture and training procedure can be utilized. Here we study 7 different label quality scoring methods used in conjunction with a DeepLabV3+ or a FPN segmentation model to detect annotation errors in a version of the SYNTHIA dataset. Precision-recall evaluations reveal a score -- the soft-minimum of the model-estimated likelihoods of each pixel's annotated class -- that is particularly effective to identify images that are mislabeled, across multiple types of annotation error.
    FedYolo: Augmenting Federated Learning with Pretrained Transformers. (arXiv:2307.04905v1 [cs.LG])
    The growth and diversity of machine learning applications motivate a rethinking of learning with mobile and edge devices. How can we address diverse client goals and learn with scarce heterogeneous data? While federated learning aims to address these issues, it has challenges hindering a unified solution. Large transformer models have been shown to work across a variety of tasks achieving remarkable few-shot adaptation. This raises the question: Can clients use a single general-purpose model, rather than custom models for each task, while obeying device and network constraints? In this work, we investigate pretrained transformers (PTF) to achieve these on-device learning goals and thoroughly explore the roles of model size and modularity, where the latter refers to adaptation through modules such as prompts or adapters. Focusing on federated learning, we demonstrate that: (1) Larger scale shrinks the accuracy gaps between alternative approaches and improves heterogeneity robustness. Scale allows clients to run more local SGD epochs which can significantly reduce the number of communication rounds. At the extreme, clients can achieve respectable accuracy locally highlighting the potential of fully-local learning. (2) Modularity, by design, enables $>$100$\times$ less communication in bits. Surprisingly, it also boosts the generalization capability of local adaptation methods and the robustness of smaller PTFs. Finally, it enables clients to solve multiple unrelated tasks simultaneously using a single PTF, whereas full updates are prone to catastrophic forgetting. These insights on scale and modularity motivate a new federated learning approach we call "You Only Load Once" (FedYolo): The clients load a full PTF model once and all future updates are accomplished through communication-efficient modules with limited catastrophic-forgetting, where each task is assigned to its own module.
    ClimaX: A foundation model for weather and climate. (arXiv:2301.10343v3 [cs.LG] UPDATED)
    Most state-of-the-art approaches for weather and climate modeling are based on physics-informed numerical models of the atmosphere. These approaches aim to model the non-linear dynamics and complex interactions between multiple variables, which are challenging to approximate. Additionally, many such numerical models are computationally intensive, especially when modeling the atmospheric phenomenon at a fine-grained spatial and temporal resolution. Recent data-driven approaches based on machine learning instead aim to directly solve a downstream forecasting or projection task by learning a data-driven functional mapping using deep neural networks. However, these networks are trained using curated and homogeneous climate datasets for specific spatiotemporal tasks, and thus lack the generality of numerical models. We develop and demonstrate ClimaX, a flexible and generalizable deep learning model for weather and climate science that can be trained using heterogeneous datasets spanning different variables, spatio-temporal coverage, and physical groundings. ClimaX extends the Transformer architecture with novel encoding and aggregation blocks that allow effective use of available compute while maintaining general utility. ClimaX is pre-trained with a self-supervised learning objective on climate datasets derived from CMIP6. The pre-trained ClimaX can then be fine-tuned to address a breadth of climate and weather tasks, including those that involve atmospheric variables and spatio-temporal scales unseen during pretraining. Compared to existing data-driven baselines, we show that this generality in ClimaX results in superior performance on benchmarks for weather forecasting and climate projections, even when pretrained at lower resolutions and compute budgets. The source code is available at https://github.com/microsoft/ClimaX.
    Reject option models comprising out-of-distribution detection. (arXiv:2307.05199v1 [cs.LG])
    The optimal prediction strategy for out-of-distribution (OOD) setups is a fundamental question in machine learning. In this paper, we address this question and present several contributions. We propose three reject option models for OOD setups: the Cost-based model, the Bounded TPR-FPR model, and the Bounded Precision-Recall model. These models extend the standard reject option models used in non-OOD setups and define the notion of an optimal OOD selective classifier. We establish that all the proposed models, despite their different formulations, share a common class of optimal strategies. Motivated by the optimal strategy, we introduce double-score OOD methods that leverage uncertainty scores from two chosen OOD detectors: one focused on OOD/ID discrimination and the other on misclassification detection. The experimental results consistently demonstrate the superior performance of this simple strategy compared to state-of-the-art methods. Additionally, we propose novel evaluation metrics derived from the definition of the optimal strategy under the proposed OOD rejection models. These new metrics provide a comprehensive and reliable assessment of OOD methods without the deficiencies observed in existing evaluation approaches.
    MAP- and MLE-Based Teaching. (arXiv:2307.05252v1 [cs.LG])
    Imagine a learner L who tries to infer a hidden concept from a collection of observations. Building on the work [4] of Ferri et al., we assume the learner to be parameterized by priors P(c) and by c-conditional likelihoods P(z|c) where c ranges over all concepts in a given class C and z ranges over all observations in an observation set Z. L is called a MAP-learner (resp. an MLE-learner) if it thinks of a collection S of observations as a random sample and returns the concept with the maximum a-posteriori probability (resp. the concept which maximizes the c-conditional likelihood of S). Depending on whether L assumes that S is obtained from ordered or unordered sampling resp. from sampling with or without replacement, we can distinguish four different sampling modes. Given a target concept c in C, a teacher for a MAP-learner L aims at finding a smallest collection of observations that causes L to return c. This approach leads in a natural manner to various notions of a MAP- or MLE-teaching dimension of a concept class C. Our main results are: We show that this teaching model has some desirable monotonicity properties. We clarify how the four sampling modes are related to each other. As for the (important!) special case, where concepts are subsets of a domain and observations are 0,1-labeled examples, we obtain some additional results. First of all, we characterize the MAP- and MLE-teaching dimension associated with an optimally parameterized MAP-learner graph-theoretically. From this central result, some other ones are easy to derive. It is shown, for instance, that the MLE-teaching dimension is either equal to the MAP-teaching dimension or exceeds the latter by 1. It is shown furthermore that these dimensions can be bounded from above by the so-called antichain number, the VC-dimension and related combinatorial parameters. Moreover they can be computed in polynomial time.
    Hybrid hidden Markov LSTM for short-term traffic flow prediction. (arXiv:2307.04954v1 [cs.LG])
    Deep learning (DL) methods have outperformed parametric models such as historical average, ARIMA and variants in predicting traffic variables into short and near-short future, that are critical for traffic management. Specifically, recurrent neural network (RNN) and its variants (e.g. long short-term memory) are designed to retain long-term temporal correlations and therefore are suitable for modeling sequences. However, multi-regime models assume the traffic system to evolve through multiple states (say, free-flow, congestion in traffic) with distinct characteristics, and hence, separate models are trained to characterize the traffic dynamics within each regime. For instance, Markov-switching models with a hidden Markov model (HMM) for regime identification is capable of capturing complex dynamic patterns and non-stationarity. Interestingly, both HMM and LSTM can be used for modeling an observation sequence from a set of latent or, hidden state variables. In LSTM, the latent variable is computed in a deterministic manner from the current observation and the previous latent variable, while, in HMM, the set of latent variables is a Markov chain. Inspired by research in natural language processing, a hybrid hidden Markov-LSTM model that is capable of learning complementary features in traffic data is proposed for traffic flow prediction. Results indicate significant performance gains in using hybrid architecture compared to conventional methods such as Markov switching ARIMA and LSTM.
    Measuring and Mitigating Interference in Reinforcement Learning. (arXiv:2307.04887v1 [cs.LG])
    Catastrophic interference is common in many network-based learning systems, and many proposals exist for mitigating it. Before overcoming interference we must understand it better. In this work, we provide a definition and novel measure of interference for value-based reinforcement learning methods such as Fitted Q-Iteration and DQN. We systematically evaluate our measure of interference, showing that it correlates with instability in control performance, across a variety of network architectures. Our new interference measure allows us to ask novel scientific questions about commonly used deep learning architectures and study learning algorithms which mitigate interference. Lastly, we outline a class of algorithms which we call online-aware that are designed to mitigate interference, and show they do reduce interference according to our measure and that they improve stability and performance in several classic control environments.
    Predicting small molecules solubilities on endpoint devices using deep ensemble neural networks. (arXiv:2307.05318v1 [physics.chem-ph])
    Aqueous solubility is a valuable yet challenging property to predict. Computing solubility using first-principles methods requires accounting for the competing effects of entropy and enthalpy, resulting in long computations for relatively poor accuracy. Data-driven approaches, such as deep learning, offer improved accuracy and computational efficiency but typically lack uncertainty quantification. Additionally, ease of use remains a concern for any computational technique, resulting in the sustained popularity of group-based contribution methods. In this work, we addressed these problems with a deep learning model with predictive uncertainty that runs on a static website (without a server). This approach moves computing needs onto the website visitor without requiring installation, removing the need to pay for and maintain servers. Our model achieves satisfactory results in solubility prediction. Furthermore, we demonstrate how to create molecular property prediction models that balance uncertainty and ease of use. The code is available at \url{https://github.com/ur-whitelab/mol.dev}, and the model is usable at \url{https://mol.dev}.
    Compact Twice Fusion Network for Edge Detection. (arXiv:2307.04952v1 [cs.CV])
    The significance of multi-scale features has been gradually recognized by the edge detection community. However, the fusion of multi-scale features increases the complexity of the model, which is not friendly to practical application. In this work, we propose a Compact Twice Fusion Network (CTFN) to fully integrate multi-scale features while maintaining the compactness of the model. CTFN includes two lightweight multi-scale feature fusion modules: a Semantic Enhancement Module (SEM) that can utilize the semantic information contained in coarse-scale features to guide the learning of fine-scale features, and a Pseudo Pixel-level Weighting (PPW) module that aggregate the complementary merits of multi-scale features by assigning weights to all features. Notwithstanding all this, the interference of texture noise makes the correct classification of some pixels still a challenge. For these hard samples, we propose a novel loss function, coined Dynamic Focal Loss, which reshapes the standard cross-entropy loss and dynamically adjusts the weights to correct the distribution of hard samples. We evaluate our method on three datasets, i.e., BSDS500, NYUDv2, and BIPEDv2. Compared with state-of-the-art methods, CTFN achieves competitive accuracy with less parameters and computational cost. Apart from the backbone, CTFN requires only 0.1M additional parameters, which reduces its computation cost to just 60% of other state-of-the-art methods. The codes are available at https://github.com/Li-yachuan/CTFN-pytorch-master.
    Intrinsically motivated graph exploration using network theories of human curiosity. (arXiv:2307.04962v1 [cs.LG])
    Intrinsically motivated exploration has proven useful for reinforcement learning, even without additional extrinsic rewards. When the environment is naturally represented as a graph, how to guide exploration best remains an open question. In this work, we propose a novel approach for exploring graph-structured data motivated by two theories of human curiosity: the information gap theory and the compression progress theory. The theories view curiosity as an intrinsic motivation to optimize for topological features of subgraphs induced by the visited nodes in the environment. We use these proposed features as rewards for graph neural-network-based reinforcement learning. On multiple classes of synthetically generated graphs, we find that trained agents generalize to larger environments and to longer exploratory walks than are seen during training. Our method computes more efficiently than the greedy evaluation of the relevant topological properties. The proposed intrinsic motivations bear particular relevance for recommender systems. We demonstrate that curiosity-based recommendations are more predictive of human behavior than PageRank centrality for several real-world graph datasets, including MovieLens, Amazon Books, and Wikispeedia.
    Empowering recommender systems using automatically generated Knowledge Graphs and Reinforcement Learning. (arXiv:2307.04996v1 [cs.IR])
    Personalized recommendations have a growing importance in direct marketing, which motivates research to enhance customer experiences by knowledge graph (KG) applications. For example, in financial services, companies may benefit from providing relevant financial articles to their customers to cultivate relationships, foster client engagement and promote informed financial decisions. While several approaches center on KG-based recommender systems for improved content, in this study we focus on interpretable KG-based recommender systems for decision making.To this end, we present two knowledge graph-based approaches for personalized article recommendations for a set of customers of a large multinational financial services company. The first approach employs Reinforcement Learning and the second approach uses the XGBoost algorithm for recommending articles to the customers. Both approaches make use of a KG generated from both structured (tabular data) and unstructured data (a large body of text data).Using the Reinforcement Learning-based recommender system we could leverage the graph traversal path leading to the recommendation as a way to generate interpretations (Path Directed Reasoning (PDR)). In the XGBoost-based approach, one can also provide explainable results using post-hoc methods such as SHAP (SHapley Additive exPlanations) and ELI5 (Explain Like I am Five).Importantly, our approach offers explainable results, promoting better decision-making. This study underscores the potential of combining advanced machine learning techniques with KG-driven insights to bolster experience in customer relationship management.
    BayesFlow: Amortized Bayesian Workflows With Neural Networks. (arXiv:2306.16015v2 [cs.LG] UPDATED)
    Modern Bayesian inference involves a mixture of computational techniques for estimating, validating, and drawing conclusions from probabilistic models as part of principled workflows for data analysis. Typical problems in Bayesian workflows are the approximation of intractable posterior distributions for diverse model types and the comparison of competing models of the same process in terms of their complexity and predictive performance. This manuscript introduces the Python library BayesFlow for simulation-based training of established neural network architectures for amortized data compression and inference. Amortized Bayesian inference, as implemented in BayesFlow, enables users to train custom neural networks on model simulations and re-use these networks for any subsequent application of the models. Since the trained networks can perform inference almost instantaneously, the upfront neural network training is quickly amortized.
    Self Expanding Neural Networks. (arXiv:2307.04526v2 [cs.LG] UPDATED)
    The results of training a neural network are heavily dependent on the architecture chosen; and even a modification of only the size of the network, however small, typically involves restarting the training process. In contrast to this, we begin training with a small architecture, only increase its capacity as necessary for the problem, and avoid interfering with previous optimization while doing so. We thereby introduce a natural gradient based approach which intuitively expands both the width and depth of a neural network when this is likely to substantially reduce the hypothetical converged training loss. We prove an upper bound on the "rate" at which neurons are added, and a computationally cheap lower bound on the expansion score. We illustrate the benefits of such Self-Expanding Neural Networks in both classification and regression problems, including those where the appropriate architecture size is substantially uncertain a priori.
    TOAST: Transfer Learning via Attention Steering. (arXiv:2305.15542v2 [cs.CV] UPDATED)
    Transfer learning involves adapting a pre-trained model to novel downstream tasks. However, we observe that current transfer learning methods often fail to focus on task-relevant features. In this work, we explore refocusing model attention for transfer learning. We introduce Top-Down Attention Steering (TOAST), a novel transfer learning algorithm that keeps the pre-trained backbone frozen, selects task-relevant features in the output, and feeds those features back to the model to steer the attention to the task-specific features. By refocusing the attention only, TOAST achieves state-of-the-art results on a number of transfer learning benchmarks, while having a small number of tunable parameters. Compared to fully fine-tuning, LoRA, and prompt tuning, TOAST substantially improves performance across a range of fine-grained visual classification datasets (e.g., 81.1% -> 86.2% on FGVC). TOAST also outperforms the fully fine-tuned Alpaca and Vicuna models on instruction-following language generation. Code is available at https://github.com/bfshi/TOAST.
    Empowering Cross-lingual Behavioral Testing of NLP Models with Typological Features. (arXiv:2307.05454v1 [cs.CL])
    A challenge towards developing NLP systems for the world's languages is understanding how they generalize to typological differences relevant for real-world applications. To this end, we propose M2C, a morphologically-aware framework for behavioral testing of NLP models. We use M2C to generate tests that probe models' behavior in light of specific linguistic features in 12 typologically diverse languages. We evaluate state-of-the-art language models on the generated tests. While models excel at most tests in English, we highlight generalization failures to specific typological characteristics such as temporal expressions in Swahili and compounding possessives in Finish. Our findings motivate the development of models that address these blind spots.
    Smooth Monotonic Networks. (arXiv:2306.01147v2 [cs.LG] UPDATED)
    Monotonicity constraints are powerful regularizers in statistical modelling. They can support fairness in computer supported decision making and increase plausibility in data-driven scientific models. The seminal min-max (MM) neural network architecture ensures monotonicity, but often gets stuck in undesired local optima during training because of vanishing gradients. We propose a simple modification of the MM network using strictly-increasing smooth non-linearities that alleviates this problem. The resulting smooth min-max (SMM) network module inherits the asymptotic approximation properties from the MM architecture. It can be used within larger deep learning systems trained end-to-end. The SMM module is considerably simpler and less computationally demanding than state-of-the-art neural networks for monotonic modelling. Still, in our experiments, it compared favorably to alternative neural and non-neural approaches in terms of generalization performance.
    Comparison of High-Dimensional Bayesian Optimization Algorithms on BBOB. (arXiv:2303.00890v2 [cs.LG] UPDATED)
    Bayesian Optimization (BO) is a class of black-box, surrogate-based heuristics that can efficiently optimize problems that are expensive to evaluate, and hence admit only small evaluation budgets. BO is particularly popular for solving numerical optimization problems in industry, where the evaluation of objective functions often relies on time-consuming simulations or physical experiments. However, many industrial problems depend on a large number of parameters. This poses a challenge for BO algorithms, whose performance is often reported to suffer when the dimension grows beyond 15 variables. Although many new algorithms have been proposed to address this problem, it is not well understood which one is the best for which optimization scenario. In this work, we compare five state-of-the-art high-dimensional BO algorithms, with vanilla BO and CMA-ES on the 24 BBOB functions of the COCO environment at increasing dimensionality, ranging from 10 to 60 variables. Our results confirm the superiority of BO over CMA-ES for limited evaluation budgets and suggest that the most promising approach to improve BO is the use of trust regions. However, we also observe significant performance differences for different function landscapes and budget exploitation phases, indicating improvement potential, e.g., through hybridization of algorithmic components.
    Capafoldable: self-tracking foldable smart textiles with capacitive sensing. (arXiv:2307.05370v1 [cs.HC])
    Folding is an unique structural technique to enable planer materials with motion or 3D mechanical properties. Textile-based capacitive sensing has shown to be sensitive to the geometry deformation and relative motion of conductive textiles. In this work, we propose a novel self-tracking foldable smart textile by combining folded fabric structures and capacitive sensing to detect the structural motions using state-of-the-art sensing circuits and deep learning technologies. We created two folding patterns, Accordion and Chevron, each with two layouts of capacitive sensors in the form of thermobonded conductive textile patches. In an experiment of manually moving patches of the folding patterns, we developed deep neural network to learn and reconstruct the vision-tracked shape of the patches. Through our approach, the geometry primitives defining the patch shape can be reconstructed from the capacitive signals with R-squared value of up to 95\% and tracking error of 1cm for 22.5cm long patches. With mechanical, electrical and sensing properties, Capafoldable could enable a new range of smart textile applications.
    Differential Analysis of Triggers and Benign Features for Black-Box DNN Backdoor Detection. (arXiv:2307.05422v1 [cs.CR])
    This paper proposes a data-efficient detection method for deep neural networks against backdoor attacks under a black-box scenario. The proposed approach is motivated by the intuition that features corresponding to triggers have a higher influence in determining the backdoored network output than any other benign features. To quantitatively measure the effects of triggers and benign features on determining the backdoored network output, we introduce five metrics. To calculate the five-metric values for a given input, we first generate several synthetic samples by injecting the input's partial contents into clean validation samples. Then, the five metrics are computed by using the output labels of the corresponding synthetic samples. One contribution of this work is the use of a tiny clean validation dataset. Having the computed five metrics, five novelty detectors are trained from the validation dataset. A meta novelty detector fuses the output of the five trained novelty detectors to generate a meta confidence score. During online testing, our method determines if online samples are poisoned or not via assessing their meta confidence scores output by the meta novelty detector. We show the efficacy of our methodology through a broad range of backdoor attacks, including ablation studies and comparison to existing approaches. Our methodology is promising since the proposed five metrics quantify the inherent differences between clean and poisoned samples. Additionally, our detection method can be incrementally improved by appending more metrics that may be proposed to address future advanced attacks.
    Selective Sampling and Imitation Learning via Online Regression. (arXiv:2307.04998v1 [cs.LG])
    We consider the problem of Imitation Learning (IL) by actively querying noisy expert for feedback. While imitation learning has been empirically successful, much of prior work assumes access to noiseless expert feedback which is not practical in many applications. In fact, when one only has access to noisy expert feedback, algorithms that rely on purely offline data (non-interactive IL) can be shown to need a prohibitively large number of samples to be successful. In contrast, in this work, we provide an interactive algorithm for IL that uses selective sampling to actively query the noisy expert for feedback. Our contributions are twofold: First, we provide a new selective sampling algorithm that works with general function classes and multiple actions, and obtains the best-known bounds for the regret and the number of queries. Next, we extend this analysis to the problem of IL with noisy expert feedback and provide a new IL algorithm that makes limited queries. Our algorithm for selective sampling leverages function approximation, and relies on an online regression oracle w.r.t.~the given model class to predict actions, and to decide whether to query the expert for its label. On the theoretical side, the regret bound of our algorithm is upper bounded by the regret of the online regression oracle, while the query complexity additionally depends on the eluder dimension of the model class. We complement this with a lower bound that demonstrates that our results are tight. We extend our selective sampling algorithm for IL with general function approximation and provide bounds on both the regret and the number of queries made to the noisy expert. A key novelty here is that our regret and query complexity bounds only depend on the number of times the optimal policy (and not the noisy expert, or the learner) go to states that have a small margin.
    Optimal Algorithms for Latent Bandits with Cluster Structure. (arXiv:2301.07040v3 [cs.LG] UPDATED)
    We consider the problem of latent bandits with cluster structure where there are multiple users, each with an associated multi-armed bandit problem. These users are grouped into \emph{latent} clusters such that the mean reward vectors of users within the same cluster are identical. At each round, a user, selected uniformly at random, pulls an arm and observes a corresponding noisy reward. The goal of the users is to maximize their cumulative rewards. This problem is central to practical recommendation systems and has received wide attention of late \cite{gentile2014online, maillard2014latent}. Now, if each user acts independently, then they would have to explore each arm independently and a regret of $\Omega(\sqrt{\mathsf{MNT}})$ is unavoidable, where $\mathsf{M}, \mathsf{N}$ are the number of arms and users, respectively. Instead, we propose LATTICE (Latent bAndiTs via maTrIx ComplEtion) which allows exploitation of the latent cluster structure to provide the minimax optimal regret of $\widetilde{O}(\sqrt{(\mathsf{M}+\mathsf{N})\mathsf{T}})$, when the number of clusters is $\widetilde{O}(1)$. This is the first algorithm to guarantee such strong regret bound. LATTICE is based on a careful exploitation of arm information within a cluster while simultaneously clustering users. Furthermore, it is computationally efficient and requires only $O(\log{\mathsf{T}})$ calls to an offline matrix completion oracle across all $\mathsf{T}$ rounds.
    CrysMMNet: Multimodal Representation for Crystal Property Prediction. (arXiv:2307.05390v1 [cond-mat.mtrl-sci])
    Machine Learning models have emerged as a powerful tool for fast and accurate prediction of different crystalline properties. Exiting state-of-the-art models rely on a single modality of crystal data i.e. crystal graph structure, where they construct multi-graph by establishing edges between nearby atoms in 3D space and apply GNN to learn materials representation. Thereby, they encode local chemical semantics around the atoms successfully but fail to capture important global periodic structural information like space group number, crystal symmetry, rotational information, etc, which influence different crystal properties. In this work, we leverage textual descriptions of materials to model global structural information into graph structure and learn a more robust and enriched representation of crystalline materials. To this effect, we first curate a textual dataset for crystalline material databases containing descriptions of each material. Further, we propose CrysMMNet, a simple multi-modal framework, which fuses both structural and textual representation together to generate a joint multimodal representation of crystalline materials. We conduct extensive experiments on two benchmark datasets across ten different properties to show that CrysMMNet outperforms existing state-of-the-art baseline methods with a good margin. We also observe that fusing the textual representation with crystal graph structure provides consistent improvement for all the SOTA GNN models compared to their own vanilla versions. We have shared the textual dataset, that we have curated for both the benchmark material databases, with the community for future use.
    To Raise or Not To Raise: The Autonomous Learning Rate Question. (arXiv:2106.08767v3 [cs.LG] UPDATED)
    There is a parameter ubiquitous throughout the deep learning world: learning rate. There is likewise a ubiquitous question: what should that learning rate be? The true answer to this question is often tedious and time consuming to obtain, and a great deal of arcane knowledge has accumulated in recent years over how to pick and modify learning rates to achieve optimal training performance. Moreover, the long hours spent carefully crafting the perfect learning rate can come to nothing the moment your network architecture, optimizer, dataset, or initial conditions change ever so slightly. But it need not be this way. We propose a new answer to the great learning rate question: the Autonomous Learning Rate Controller. Find it at https://github.com/fastestimator/ARC/tree/v2.0
    Secrets of RLHF in Large Language Models Part I: PPO. (arXiv:2307.04964v1 [cs.CL])
    Large language models (LLMs) have formulated a blueprint for the advancement of artificial general intelligence. Its primary objective is to function as a human-centric (helpful, honest, and harmless) assistant. Alignment with humans assumes paramount significance, and reinforcement learning with human feedback (RLHF) emerges as the pivotal technological paradigm underpinning this pursuit. Current technical routes usually include \textbf{reward models} to measure human preferences, \textbf{Proximal Policy Optimization} (PPO) to optimize policy model outputs, and \textbf{process supervision} to improve step-by-step reasoning capabilities. However, due to the challenges of reward design, environment interaction, and agent training, coupled with huge trial and error cost of large language models, there is a significant barrier for AI researchers to motivate the development of technical alignment and safe landing of LLMs. The stable training of RLHF has still been a puzzle. In the first report, we dissect the framework of RLHF, re-evaluate the inner workings of PPO, and explore how the parts comprising PPO algorithms impact policy agent training. We identify policy constraints being the key factor for the effective implementation of the PPO algorithm. Therefore, we explore the PPO-max, an advanced version of PPO algorithm, to efficiently improve the training stability of the policy model. Based on our main results, we perform a comprehensive analysis of RLHF abilities compared with SFT models and ChatGPT. The absence of open-source implementations has posed significant challenges to the investigation of LLMs alignment. Therefore, we are eager to release technical reports, reward models and PPO codes
    Automated Detection of Double Nuclei Galaxies using GOTHIC and the Discovery of a Large Sample of Dual AGN. (arXiv:2011.12177v4 [astro-ph.GA] UPDATED)
    We present a novel algorithm to detect double nuclei galaxies (DNG) called GOTHIC (Graph BOosted iterated HIll Climbing) - that detects whether a given image of a galaxy has two or more closely separated nuclei. Our aim is to detect samples of dual or multiple active galactic nuclei (AGN) in galaxies. Although galaxy mergers are common, the detection of dual AGN is rare. Their detection is very important as they help us understand the formation of supermassive black hole (SMBH) binaries, SMBH growth and AGN feedback effects in multiple nuclei systems. There is thus a need for an algorithm to do a systematic survey of existing imaging data for the discovery of DNGs and dual AGN. We have tested GOTHIC on a known sample of DNGs and subsequently applied it to a sample of a million SDSS DR16 galaxies lying in the redshift range of 0 to 0.75 approximately, and have available spectroscopic data. We have detected 159 dual AGN in this sample, of which 2 are triple AGN systems. Our results show that dual AGN are not common, and triple AGN even rarer. The color (u-r) magnitude plots of the DNGs indicate that star formation is quenched as the nuclei come closer and as the AGN fraction increases. The quenching is especially prominent for dual/triple AGN galaxies that lie in the extreme end of the red sequence.
    Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks. (arXiv:2209.10200v3 [cs.LG] UPDATED)
    This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which aggregates them into a quantized global model and synchronizes the devices. The goal is to jointly determine the bitwidths employed for local FL model quantization and the set of devices participating in FL training at each iteration. We pose this as an optimization problem that aims to minimize the training loss of quantized FL under a per-iteration device sampling budget and delay requirement. However, the formulated problem is difficult to solve without (i) a concrete understanding of how quantization impacts global ML performance and (ii) the ability of the server to construct estimates of this process efficiently. To address the first challenge, we analytically characterize how limited wireless resources and induced quantization errors affect the performance of the proposed FL method. Our results quantify how the improvement of FL training loss between two consecutive iterations depends on the device selection and quantization scheme as well as on several parameters inherent to the model being learned. Then, we show that the FL training process can be described as a Markov decision process and propose a model-based reinforcement learning (RL) method to optimize action selection over iterations. Compared to model-free RL, this model-based RL approach leverages the derived mathematical characterization of the FL training process to discover an effective device selection and quantization scheme without imposing additional device communication overhead. Simulation results show that the proposed FL algorithm can reduce the convergence time.
    Improving RNN-Transducers with Acoustic LookAhead. (arXiv:2307.05006v1 [cs.CL])
    RNN-Transducers (RNN-Ts) have gained widespread acceptance as an end-to-end model for speech to text conversion because of their high accuracy and streaming capabilities. A typical RNN-T independently encodes the input audio and the text context, and combines the two encodings by a thin joint network. While this architecture provides SOTA streaming accuracy, it also makes the model vulnerable to strong LM biasing which manifests as multi-step hallucination of text without acoustic evidence. In this paper we propose LookAhead that makes text representations more acoustically grounded by looking ahead into the future within the audio input. This technique yields a significant 5%-20% relative reduction in word error rate on both in-domain and out-of-domain evaluation sets.
    Number Systems for Deep Neural Network Architectures: A Survey. (arXiv:2307.05035v1 [cs.NE])
    Deep neural networks (DNNs) have become an enabling component for a myriad of artificial intelligence applications. DNNs have shown sometimes superior performance, even compared to humans, in cases such as self-driving, health applications, etc. Because of their computational complexity, deploying DNNs in resource-constrained devices still faces many challenges related to computing complexity, energy efficiency, latency, and cost. To this end, several research directions are being pursued by both academia and industry to accelerate and efficiently implement DNNs. One important direction is determining the appropriate data representation for the massive amount of data involved in DNN processing. Using conventional number systems has been found to be sub-optimal for DNNs. Alternatively, a great body of research focuses on exploring suitable number systems. This article aims to provide a comprehensive survey and discussion about alternative number systems for more efficient representations of DNN data. Various number systems (conventional/unconventional) exploited for DNNs are discussed. The impact of these number systems on the performance and hardware design of DNNs is considered. In addition, this paper highlights the challenges associated with each number system and various solutions that are proposed for addressing them. The reader will be able to understand the importance of an efficient number system for DNN, learn about the widely used number systems for DNN, understand the trade-offs between various number systems, and consider various design aspects that affect the impact of number systems on DNN performance. In addition, the recent trends and related research opportunities will be highlighted
    Learned Kernels for Interpretable and Efficient PPG Signal Quality Assessment and Artifact Segmentation. (arXiv:2307.05385v1 [eess.SP])
    Photoplethysmography (PPG) provides a low-cost, non-invasive method to continuously monitor various cardiovascular parameters. PPG signals are generated by wearable devices and frequently contain large artifacts caused by external factors, such as motion of the human subject. In order to ensure robust and accurate extraction of physiological parameters, corrupted areas of the signal need to be identified and handled appropriately. Previous methodology relied either on handcrafted feature detectors or signal metrics which yield sub-optimal performance, or relied on machine learning techniques such as deep neural networks (DNN) which lack interpretability and are computationally and memory intensive. In this work, we present a novel method to learn a small set of interpretable convolutional kernels that has performance similar to -- and often better than -- the state-of-the-art DNN approach with several orders of magnitude fewer parameters. This work allows for efficient, robust, and interpretable signal quality assessment and artifact segmentation on low-power devices.
    Fisher-Weighted Merge of Contrastive Learning Models in Sequential Recommendation. (arXiv:2307.05476v1 [cs.IR])
    Along with the exponential growth of online platforms and services, recommendation systems have become essential for identifying relevant items based on user preferences. The domain of sequential recommendation aims to capture evolving user preferences over time. To address dynamic preference, various contrastive learning methods have been proposed to target data sparsity, a challenge in recommendation systems due to the limited user-item interactions. In this paper, we are the first to apply the Fisher-Merging method to Sequential Recommendation, addressing and resolving practical challenges associated with it. This approach ensures robust fine-tuning by merging the parameters of multiple models, resulting in improved overall performance. Through extensive experiments, we demonstrate the effectiveness of our proposed methods, highlighting their potential to advance the state-of-the-art in sequential learning and recommendation systems.
    Neural network analysis of neutron and X-ray reflectivity data: Incorporating prior knowledge for tackling the phase problem. (arXiv:2307.05364v1 [eess.SP])
    Due to the lack of phase information, determining the physical parameters of multilayer thin films from measured neutron and X-ray reflectivity curves is, on a fundamental level, an underdetermined inverse problem. This so-called phase problem poses limitations on standard neural networks, constraining the range and number of considered parameters in previous machine learning solutions. To overcome this, we present an approach that utilizes prior knowledge to regularize the training process over larger parameter spaces. We demonstrate the effectiveness of our method in various scenarios, including multilayer structures with box model parameterization and a physics-inspired special parameterization of the scattering length density profile for a multilayer structure. By leveraging the input of prior knowledge, we can improve the training dynamics and address the underdetermined ("ill-posed") nature of the problem. In contrast to previous methods, our approach scales favorably when increasing the complexity of the inverse problem, working properly even for a 5-layer multilayer model and an N-layer periodic multilayer model with up to 17 open parameters.
    Uncertainty Quantification of the Virial Black Hole Mass with Conformal Prediction. (arXiv:2307.04993v1 [astro-ph.CO])
    Precise measurements of the black hole mass are essential to gain insight on the black hole and host galaxy co-evolution. A direct measure of the black hole mass is often restricted to nearest galaxies and instead, an indirect method using the single-epoch virial black hole mass estimation is used for objects at high redshifts. However, this method is subjected to biases and uncertainties as it is reliant on the scaling relation from a small sample of local active galactic nuclei. In this study, we propose the application of conformalised quantile regression (CQR) to quantify the uncertainties of the black hole predictions in a machine learning setting. We compare CQR with various prediction interval techniques and demonstrated that CQR can provide a more useful prediction interval indicator. In contrast to baseline approaches for prediction interval estimation, we show that the CQR method provides prediction intervals that adjust to the black hole mass and its related properties. That is it yields a tighter constraint on the prediction interval (hence more certain) for a larger black hole mass, and accordingly, bright and broad spectral line width source. Using a combination of neural network model and CQR framework, the recovered virial black hole mass predictions and uncertainties are comparable to those measured from the Sloan Digital Sky Survey. The code is publicly available at https://github.com/yongsukyee/uncertain_blackholemass.
    CareFall: Automatic Fall Detection through Wearable Devices and AI Methods. (arXiv:2307.05275v1 [cs.LG])
    The aging population has led to a growing number of falls in our society, affecting global public health worldwide. This paper presents CareFall, an automatic Fall Detection System (FDS) based on wearable devices and Artificial Intelligence (AI) methods. CareFall considers the accelerometer and gyroscope time signals extracted from a smartwatch. Two different approaches are used for feature extraction and classification: i) threshold-based, and ii) machine learning-based. Experimental results on two public databases show that the machine learning-based approach, which combines accelerometer and gyroscope information, outperforms the threshold-based approach in terms of accuracy, sensitivity, and specificity. This research contributes to the design of smart and user-friendly solutions to mitigate the negative consequences of falls among older people.
    Fast dynamic time warping and clustering in C++. (arXiv:2307.04904v1 [eess.SP])
    We present an approach for computationally efficient dynamic time warping (DTW) and clustering of time-series data. The method frames the dynamic warping of time series datasets as an optimisation problem solved using dynamic programming, and then clusters time series data by solving a second optimisation problem using mixed-integer programming (MIP). There is also an option to use k-medoids clustering for increased speed, when a certificate for global optimality is not essential. The improved efficiency of our approach is due to task-level parallelisation of the clustering alongside DTW. Our approach was tested using the UCR Time Series Archive, and was found to be, on average, 33% faster than the next fastest option when using the same clustering method. This increases to 64% faster when considering only larger datasets (with more than 1000 time series). The MIP clustering is most effective on small numbers of longer time series, because the DTW computation is faster than other approaches, but the clustering problem becomes increasingly computationally expensive as the number of time series to be clustered increases.
    Robust Inference of Manifold Density and Geometry by Doubly Stochastic Scaling. (arXiv:2209.08004v2 [math.ST] UPDATED)
    The Gaussian kernel and its traditional normalizations (e.g., row-stochastic) are popular approaches for assessing similarities between data points. Yet, they can be inaccurate under high-dimensional noise, especially if the noise magnitude varies considerably across the data, e.g., under heteroskedasticity or outliers. In this work, we investigate a more robust alternative -- the doubly stochastic normalization of the Gaussian kernel. We consider a setting where points are sampled from an unknown density on a low-dimensional manifold embedded in high-dimensional space and corrupted by possibly strong, non-identically distributed, sub-Gaussian noise. We establish that the doubly stochastic affinity matrix and its scaling factors concentrate around certain population forms, and provide corresponding finite-sample probabilistic error bounds. We then utilize these results to develop several tools for robust inference under general high-dimensional noise. First, we derive a robust density estimator that reliably infers the underlying sampling density and can substantially outperform the standard kernel density estimator under heteroskedasticity and outliers. Second, we obtain estimators for the pointwise noise magnitudes, the pointwise signal magnitudes, and the pairwise Euclidean distances between clean data points. Lastly, we derive robust graph Laplacian normalizations that accurately approximate various manifold Laplacians, including the Laplace Beltrami operator, improving over traditional normalizations in noisy settings. We exemplify our results in simulations and on real single-cell RNA-sequencing data. For the latter, we show that in contrast to traditional methods, our approach is robust to variability in technical noise levels across cell types.
    Score Function Gradient Estimation to Widen the Applicability of Decision-Focused Learning. (arXiv:2307.05213v1 [cs.LG])
    Many real-world optimization problems contain unknown parameters that must be predicted prior to solving. To train the predictive machine learning (ML) models involved, the commonly adopted approach focuses on maximizing predictive accuracy. However, this approach does not always lead to the minimization of the downstream task loss. Decision-focused learning (DFL) is a recently proposed paradigm whose goal is to train the ML model by directly minimizing the task loss. However, state-of-the-art DFL methods are limited by the assumptions they make about the structure of the optimization problem (e.g., that the problem is linear) and by the fact that can only predict parameters that appear in the objective function. In this work, we address these limitations by instead predicting \textit{distributions} over parameters and adopting score function gradient estimation (SFGE) to compute decision-focused updates to the predictive model, thereby widening the applicability of DFL. Our experiments show that by using SFGE we can: (1) deal with predictions that occur both in the objective function and in the constraints; and (2) effectively tackle two-stage stochastic optimization problems.
    FairLay-ML: Intuitive Remedies for Unfairness in Data-Driven Social-Critical Algorithms. (arXiv:2307.05029v1 [cs.LG])
    This thesis explores open-sourced machine learning (ML) model explanation tools to understand whether these tools can allow a layman to visualize, understand, and suggest intuitive remedies to unfairness in ML-based decision-support systems. Machine learning models trained on datasets biased against minority groups are increasingly used to guide life-altering social decisions, prompting the urgent need to study their logic for unfairness. Due to this problem's impact on vast populations of the general public, it is critical for the layperson -- not just subject matter experts in social justice or machine learning experts -- to understand the nature of unfairness within these algorithms and the potential trade-offs. Existing research on fairness in machine learning focuses mostly on the mathematical definitions and tools to understand and remedy unfair models, with some directly citing user-interactive tools as necessary for future work. This thesis presents FairLay-ML, a proof-of-concept GUI integrating some of the most promising tools to provide intuitive explanations for unfair logic in ML models by integrating existing research tools (e.g. Local Interpretable Model-Agnostic Explanations) with existing ML-focused GUI (e.g. Python Streamlit). We test FairLay-ML using models of various accuracy and fairness generated by an unfairness detector tool, Parfait-ML, and validate our results using Themis. Our study finds that the technology stack used for FairLay-ML makes it easy to install and provides real-time black-box explanations of pre-trained models to users. Furthermore, the explanations provided translate to actionable remedies.
    M$^2$Hub: Unlocking the Potential of Machine Learning for Materials Discovery. (arXiv:2307.05378v1 [cond-mat.mtrl-sci])
    We introduce M$^2$Hub, a toolkit for advancing machine learning in materials discovery. Machine learning has achieved remarkable progress in modeling molecular structures, especially biomolecules for drug discovery. However, the development of machine learning approaches for modeling materials structures lag behind, which is partly due to the lack of an integrated platform that enables access to diverse tasks for materials discovery. To bridge this gap, M$^2$Hub will enable easy access to materials discovery tasks, datasets, machine learning methods, evaluations, and benchmark results that cover the entire workflow. Specifically, the first release of M$^2$Hub focuses on three key stages in materials discovery: virtual screening, inverse design, and molecular simulation, including 9 datasets that covers 6 types of materials with 56 tasks across 8 types of material properties. We further provide 2 synthetic datasets for the purpose of generative tasks on materials. In addition to random data splits, we also provide 3 additional data partitions to reflect the real-world materials discovery scenarios. State-of-the-art machine learning methods (including those are suitable for materials structures but never compared in the literature) are benchmarked on representative tasks. Our codes and library are publicly available at https://github.com/yuanqidu/M2Hub.
    Conformalization of Sparse Generalized Linear Models. (arXiv:2307.05109v1 [cs.LG])
    Given a sequence of observable variables $\{(x_1, y_1), \ldots, (x_n, y_n)\}$, the conformal prediction method estimates a confidence set for $y_{n+1}$ given $x_{n+1}$ that is valid for any finite sample size by merely assuming that the joint distribution of the data is permutation invariant. Although attractive, computing such a set is computationally infeasible in most regression problems. Indeed, in these cases, the unknown variable $y_{n+1}$ can take an infinite number of possible candidate values, and generating conformal sets requires retraining a predictive model for each candidate. In this paper, we focus on a sparse linear model with only a subset of variables for prediction and use numerical continuation techniques to approximate the solution path efficiently. The critical property we exploit is that the set of selected variables is invariant under a small perturbation of the input data. Therefore, it is sufficient to enumerate and refit the model only at the change points of the set of active features and smoothly interpolate the rest of the solution via a Predictor-Corrector mechanism. We show how our path-following algorithm accurately approximates conformal prediction sets and illustrate its performance using synthetic and real data examples.
    SuryaKiran at MEDIQA-Sum 2023: Leveraging LoRA for Clinical Dialogue Summarization. (arXiv:2307.05162v1 [cs.CL])
    Finetuning Large Language Models helps improve the results for domain-specific use cases. End-to-end finetuning of large language models is time and resource intensive and has high storage requirements to store the finetuned version of the large language model. Parameter Efficient Fine Tuning (PEFT) methods address the time and resource challenges by keeping the large language model as a fixed base and add additional layers, which the PEFT methods finetune. This paper demonstrates the evaluation results for one such PEFT method Low Rank Adaptation (LoRA), for Clinical Dialogue Summarization. The evaluation results show that LoRA works at par with end-to-end finetuning for a large language model. The paper presents the evaluations done for solving both the Subtask A and B from ImageCLEFmedical {https://www.imageclef.org/2023/medical}
    Portfolio Optimization: A Comparative Study. (arXiv:2307.05048v1 [q-fin.PM])
    Portfolio optimization has been an area that has attracted considerable attention from the financial research community. Designing a profitable portfolio is a challenging task involving precise forecasting of future stock returns and risks. This chapter presents a comparative study of three portfolio design approaches, the mean-variance portfolio (MVP), hierarchical risk parity (HRP)-based portfolio, and autoencoder-based portfolio. These three approaches to portfolio design are applied to the historical prices of stocks chosen from ten thematic sectors listed on the National Stock Exchange (NSE) of India. The portfolios are designed using the stock price data from January 1, 2018, to December 31, 2021, and their performances are tested on the out-of-sample data from January 1, 2022, to December 31, 2022. Extensive results are analyzed on the performance of the portfolios. It is observed that the performance of the MVP portfolio is the best on the out-of-sample data for the risk-adjusted returns. However, the autoencoder portfolios outperformed their counterparts on annual returns.
    Differentially Private Statistical Inference through $\beta$-Divergence One Posterior Sampling. (arXiv:2307.05194v1 [stat.ML])
    Differential privacy guarantees allow the results of a statistical analysis involving sensitive data to be released without compromising the privacy of any individual taking part. Achieving such guarantees generally requires the injection of noise, either directly into parameter estimates or into the estimation process. Instead of artificially introducing perturbations, sampling from Bayesian posterior distributions has been shown to be a special case of the exponential mechanism, producing consistent, and efficient private estimates without altering the data generative process. The application of current approaches has, however, been limited by their strong bounding assumptions which do not hold for basic models, such as simple linear regressors. To ameliorate this, we propose $\beta$D-Bayes, a posterior sampling scheme from a generalised posterior targeting the minimisation of the $\beta$-divergence between the model and the data generating process. This provides private estimation that is generally applicable without requiring changes to the underlying model and consistently learns the data generating parameter. We show that $\beta$D-Bayes produces more precise inference estimation for the same privacy guarantees, and further facilitates differentially private estimation via posterior sampling for complex classifiers and continuous regression models such as neural networks for the first time.
    A Mapping Study of Machine Learning Methods for Remaining Useful Life Estimation of Lead-Acid Batteries. (arXiv:2307.05163v1 [cs.LG])
    Energy storage solutions play an increasingly important role in modern infrastructure and lead-acid batteries are among the most commonly used in the rechargeable category. Due to normal degradation over time, correctly determining the battery's State of Health (SoH) and Remaining Useful Life (RUL) contributes to enhancing predictive maintenance, reliability, and longevity of battery systems. Besides improving the cost savings, correct estimation of the SoH can lead to reduced pollution though reuse of retired batteries. This paper presents a mapping study of the state-of-the-art in machine learning methods for estimating the SoH and RUL of lead-acid batteries. These two indicators are critical in the battery management systems of electric vehicles, renewable energy systems, and other applications that rely heavily on this battery technology. In this study, we analyzed the types of machine learning algorithms employed for estimating SoH and RUL, and evaluated their performance in terms of accuracy and inference time. Additionally, this mapping identifies and analyzes the most commonly used combinations of sensors in specific applications, such as vehicular batteries. The mapping concludes by highlighting potential gaps and opportunities for future research, which lays the foundation for further advancements in the field.
    Tracking Most Significant Shifts in Nonparametric Contextual Bandits. (arXiv:2307.05341v1 [stat.ML])
    We study nonparametric contextual bandits where Lipschitz mean reward functions may change over time. We first establish the minimax dynamic regret rate in this less understood setting in terms of number of changes $L$ and total-variation $V$, both capturing all changes in distribution over context space, and argue that state-of-the-art procedures are suboptimal in this setting. Next, we tend to the question of an adaptivity for this setting, i.e. achieving the minimax rate without knowledge of $L$ or $V$. Quite importantly, we posit that the bandit problem, viewed locally at a given context $X_t$, should not be affected by reward changes in other parts of context space $\cal X$. We therefore propose a notion of change, which we term experienced significant shifts, that better accounts for locality, and thus counts considerably less changes than $L$ and $V$. Furthermore, similar to recent work on non-stationary MAB (Suk & Kpotufe, 2022), experienced significant shifts only count the most significant changes in mean rewards, e.g., severe best-arm changes relevant to observed contexts. Our main result is to show that this more tolerant notion of change can in fact be adapted to.
    Distributed Pruning Towards Tiny Neural Networks in Federated Learning. (arXiv:2212.01977v2 [cs.LG] UPDATED)
    Neural network pruning is an essential technique for reducing the size and complexity of deep neural networks, enabling large-scale models on devices with limited resources. However, existing pruning approaches heavily rely on training data for guiding the pruning strategies, making them ineffective for federated learning over distributed and confidential datasets. Additionally, the memory- and computation-intensive pruning process becomes infeasible for recourse-constrained devices in federated learning. To address these challenges, we propose FedTiny, a distributed pruning framework for federated learning that generates specialized tiny models for memory- and computing-constrained devices. We introduce two key modules in FedTiny to adaptively search coarse- and finer-pruned specialized models to fit deployment scenarios with sparse and cheap local computation. First, an adaptive batch normalization selection module is designed to mitigate biases in pruning caused by the heterogeneity of local data. Second, a lightweight progressive pruning module aims to finer prune the models under strict memory and computational budgets, allowing the pruning policy for each layer to be gradually determined rather than evaluating the overall model structure. The experimental results demonstrate the effectiveness of FedTiny, which outperforms state-of-the-art approaches, particularly when compressing deep models to extremely sparse tiny models. FedTiny achieves an accuracy improvement of 2.61% while significantly reducing the computational cost by 95.91% and the memory footprint by 94.01% compared to state-of-the-art methods.
    Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Models. (arXiv:2305.03829v2 [cs.LG] UPDATED)
    Image-based precision medicine aims to personalize treatment decisions based on an individual's unique imaging features so as to improve their clinical outcome. Machine learning frameworks that integrate uncertainty estimation as part of their treatment recommendations would be safer and more reliable. However, little work has been done in adapting uncertainty estimation techniques and validation metrics for precision medicine. In this paper, we use Bayesian deep learning for estimating the posterior distribution over factual and counterfactual outcomes on several treatments. This allows for estimating the uncertainty for each treatment option and for the individual treatment effects (ITE) between any two treatments. We train and evaluate this model to predict future new and enlarging T2 lesion counts on a large, multi-center dataset of MR brain images of patients with multiple sclerosis, exposed to several treatments during randomized controlled trials. We evaluate the correlation of the uncertainty estimate with the factual error, and, given the lack of ground truth counterfactual outcomes, demonstrate how uncertainty for the ITE prediction relates to bounds on the ITE error. Lastly, we demonstrate how knowledge of uncertainty could modify clinical decision-making to improve individual patient and clinical trial outcomes.
    Deep Probabilistic Movement Primitives with a Bayesian Aggregator. (arXiv:2307.05141v1 [cs.RO])
    Movement primitives are trainable parametric models that reproduce robotic movements starting from a limited set of demonstrations. Previous works proposed simple linear models that exhibited high sample efficiency and generalization power by allowing temporal modulation of movements (reproducing movements faster or slower), blending (merging two movements into one), via-point conditioning (constraining a movement to meet some particular via-points) and context conditioning (generation of movements based on an observed variable, e.g., position of an object). Previous works have proposed neural network-based motor primitive models, having demonstrated their capacity to perform tasks with some forms of input conditioning or time-modulation representations. However, there has not been a single unified deep motor primitive's model proposed that is capable of all previous operations, limiting neural motor primitive's potential applications. This paper proposes a deep movement primitive architecture that encodes all the operations above and uses a Bayesian context aggregator that allows a more sound context conditioning and blending. Our results demonstrate our approach can scale to reproduce complex motions on a larger variety of input choices compared to baselines while maintaining operations of linear movement primitives provide.
    Probabilistic Counterexample Guidance for Safer Reinforcement Learning. (arXiv:2307.04927v1 [cs.LG])
    Safe exploration aims at addressing the limitations of Reinforcement Learning (RL) in safety-critical scenarios, where failures during trial-and-error learning may incur high costs. Several methods exist to incorporate external knowledge or to use proximal sensor data to limit the exploration of unsafe states. However, reducing exploration risks in unknown environments, where an agent must discover safety threats during exploration, remains challenging. In this paper, we target the problem of safe exploration by guiding the training with counterexamples of the safety requirement. Our method abstracts both continuous and discrete state-space systems into compact abstract models representing the safety-relevant knowledge acquired by the agent during exploration. We then exploit probabilistic counterexample generation to construct minimal simulation submodels eliciting safety requirement violations, where the agent can efficiently train offline to refine its policy towards minimising the risk of safety violations during the subsequent online exploration. We demonstrate our method's effectiveness in reducing safety violations during online exploration in preliminary experiments by an average of 40.3% compared with QL and DQN standard algorithms and 29.1% compared with previous related work, while achieving comparable cumulative rewards with respect to unrestricted exploration and alternative approaches.
    One-Versus-Others Attention: Scalable Multimodal Integration. (arXiv:2307.05435v1 [cs.LG])
    Multimodal learning models have become increasingly important as they surpass single-modality approaches on diverse tasks ranging from question-answering to autonomous driving. Despite the importance of multimodal learning, existing efforts focus on NLP applications, where the number of modalities is typically less than four (audio, video, text, images). However, data inputs in other domains, such as the medical field, may include X-rays, PET scans, MRIs, genetic screening, clinical notes, and more, creating a need for both efficient and accurate information fusion. Many state-of-the-art models rely on pairwise cross-modal attention, which does not scale well for applications with more than three modalities. For $n$ modalities, computing attention will result in $n \choose 2$ operations, potentially requiring considerable amounts of computational resources. To address this, we propose a new domain-neutral attention mechanism, One-Versus-Others (OvO) attention, that scales linearly with the number of modalities and requires only $n$ attention operations, thus offering a significant reduction in computational complexity compared to existing cross-modal attention algorithms. Using three diverse real-world datasets as well as an additional simulation experiment, we show that our method improves performance compared to popular fusion techniques while decreasing computation costs.
    Restoring the saturation response of a PMT using pulse-shape and artificial-neural-networks. (arXiv:2302.06170v3 [physics.ins-det] UPDATED)
    The linear response of a photomultiplier tube (PMT) is a required property for photon counting and reconstruction of the neutrino energy. The linearity valid region and the saturation response of PMT were investigated using a linear-alkyl-benzene (LAB)-based liquid scintillator. A correlation was observed between the two different saturation responses, with pulse-shape distortion and pulse-area decrease. The observed pulse-shape provides useful information for the estimation of the linearity region relative to the pulse-area. This correlation-based diagnosis allows an ${in}$-${situ}$ estimation of the linearity range, which was previously challenging. The measured correlation between the two saturation responses was employed to train an artificial-neural-network (ANN) to predict the decrease in pulse-area from the observed pulse-shape. The ANN-predicted pulse-area decrease enables the prediction of the ideal number of photoelectrons irrelevant to the saturation behavior. This pulse-shape-based machine learning technique offers a novel method for restoring the saturation response of PMTs.
    Hierarchical Classification of Research Fields in the "Web of Science" Using Deep Learning. (arXiv:2302.00390v2 [cs.DL] UPDATED)
    This paper presents a hierarchical classification system that automatically categorizes a scholarly publication using its abstract into a three-tier hierarchical label set (discipline, field, subfield) in a multi-class setting. This system enables a holistic categorization of research activities in the mentioned hierarchy in terms of knowledge production through articles and impact through citations, permitting those activities to fall into multiple categories. The classification system distinguishes 44 disciplines, 718 fields and 1,485 subfields among 160 million abstract snippets in Microsoft Academic Graph (version 2018-05-17). We used batch training in a modularized and distributed fashion to address and allow for interdisciplinary and interfield classifications in single-label and multi-label settings. In total, we have conducted 3,140 experiments in all considered models (Convolutional Neural Networks, Recurrent Neural Networks, Transformers). The classification accuracy is > 90% in 77.13% and 78.19% of the single-label and multi-label classifications, respectively. We examine the advantages of our classification by its ability to better align research texts and output with disciplines, to adequately classify them in an automated way, and to capture the degree of interdisciplinarity. The proposed system (a set of pre-trained models) can serve as a backbone to an interactive system for indexing scientific publications in the future.
    Simplicial Message Passing for Chemical Property Prediction. (arXiv:2307.05392v1 [cond-mat.mtrl-sci])
    Recently, message-passing Neural networks (MPNN) provide a promising tool for dealing with molecular graphs and have achieved remarkable success in facilitating the discovery and materials design with desired properties. However, the classical MPNN methods also suffer from a limitation in capturing the strong topological information hidden in molecular structures, such as nonisomorphic graphs. To address this problem, this work proposes a Simplicial Message Passing (SMP) framework to better capture the topological information from molecules, which can break through the limitation within the vanilla message-passing paradigm. In SMP, a generalized message-passing framework is established for aggregating the information from arbitrary-order simplicial complex, and a hierarchical structure is elaborated to allow information exchange between different order simplices. We apply the SMP framework within deep learning architectures for quantum-chemical properties prediction and achieve state-of-the-art results. The results show that compared to traditional MPNN, involving higher-order simplex can better capture the complex structure of molecules and substantially enhance the performance of tasks. The SMP-based model can provide a generalized framework for GNNs and aid in the discovery and design of materials with tailored properties for various applications.
    SleepEGAN: A GAN-enhanced Ensemble Deep Learning Model for Imbalanced Classification of Sleep Stages. (arXiv:2307.05362v1 [eess.SP])
    Deep neural networks have played an important role in automatic sleep stage classification because of their strong representation and in-model feature transformation abilities. However, class imbalance and individual heterogeneity which typically exist in raw EEG signals of sleep data can significantly affect the classification performance of any machine learning algorithms. To solve these two problems, this paper develops a generative adversarial network (GAN)-powered ensemble deep learning model, named SleepEGAN, for the imbalanced classification of sleep stages. To alleviate class imbalance, we propose a new GAN (called EGAN) architecture adapted to the features of EEG signals for data augmentation. The generated samples for the minority classes are used in the training process. In addition, we design a cost-free ensemble learning strategy to reduce the model estimation variance caused by the heterogeneity between the validation and test sets, so as to enhance the accuracy and robustness of prediction performance. We show that the proposed method can improve classification accuracy compared to several existing state-of-the-art methods using three public sleep datasets.
    Stochastic Nested Compositional Bi-level Optimization for Robust Feature Learning. (arXiv:2307.05384v1 [math.OC])
    We develop and analyze stochastic approximation algorithms for solving nested compositional bi-level optimization problems. These problems involve a nested composition of $T$ potentially non-convex smooth functions in the upper-level, and a smooth and strongly convex function in the lower-level. Our proposed algorithm does not rely on matrix inversions or mini-batches and can achieve an $\epsilon$-stationary solution with an oracle complexity of approximately $\tilde{O}_T(1/\epsilon^{2})$, assuming the availability of stochastic first-order oracles for the individual functions in the composition and the lower-level, which are unbiased and have bounded moments. Here, $\tilde{O}_T$ hides polylog factors and constants that depend on $T$. The key challenge we address in establishing this result relates to handling three distinct sources of bias in the stochastic gradients. The first source arises from the compositional nature of the upper-level, the second stems from the bi-level structure, and the third emerges due to the utilization of Neumann series approximations to avoid matrix inversion. To demonstrate the effectiveness of our approach, we apply it to the problem of robust feature learning for deep neural networks under covariate shift, showcasing the benefits and advantages of our methodology in that context.
    Choosing Well Your Opponents: How to Guide the Synthesis of Programmatic Strategies. (arXiv:2307.04893v1 [cs.LG])
    This paper introduces Local Learner (2L), an algorithm for providing a set of reference strategies to guide the search for programmatic strategies in two-player zero-sum games. Previous learning algorithms, such as Iterated Best Response (IBR), Fictitious Play (FP), and Double-Oracle (DO), can be computationally expensive or miss important information for guiding search algorithms. 2L actively selects a set of reference strategies to improve the search signal. We empirically demonstrate the advantages of our approach while guiding a local search algorithm for synthesizing strategies in three games, including MicroRTS, a challenging real-time strategy game. Results show that 2L learns reference strategies that provide a stronger search signal than IBR, FP, and DO. We also simulate a tournament of MicroRTS, where a synthesizer using 2L outperformed the winners of the two latest MicroRTS competitions, which were programmatic strategies written by human programmers.  ( 2 min )
    Optimized Crystallographic Graph Generation for Material Science. (arXiv:2307.05380v1 [cond-mat.mtrl-sci])
    Graph neural networks are widely used in machine learning applied to chemistry, and in particular for material science discovery. For crystalline materials, however, generating graph-based representation from geometrical information for neural networks is not a trivial task. The periodicity of crystalline needs efficient implementations to be processed in real-time under a massively parallel environment. With the aim of training graph-based generative models of new material discovery, we propose an efficient tool to generate cutoff graphs and k-nearest-neighbours graphs of periodic structures within GPU optimization. We provide pyMatGraph a Pytorch-compatible framework to generate graphs in real-time during the training of neural network architecture. Our tool can update a graph of a structure, making generative models able to update the geometry and process the updated graph during the forward propagation on the GPU side. Our code is publicly available at https://github.com/aklipf/mat-graph.  ( 2 min )
    Discovering Symbolic Laws Directly from Trajectories with Hamiltonian Graph Neural Networks. (arXiv:2307.05299v1 [cs.LG])
    The time evolution of physical systems is described by differential equations, which depend on abstract quantities like energy and force. Traditionally, these quantities are derived as functionals based on observables such as positions and velocities. Discovering these governing symbolic laws is the key to comprehending the interactions in nature. Here, we present a Hamiltonian graph neural network (HGNN), a physics-enforced GNN that learns the dynamics of systems directly from their trajectory. We demonstrate the performance of HGNN on n-springs, n-pendulums, gravitational systems, and binary Lennard Jones systems; HGNN learns the dynamics in excellent agreement with the ground truth from small amounts of data. We also evaluate the ability of HGNN to generalize to larger system sizes, and to hybrid spring-pendulum system that is a combination of two original systems (spring and pendulum) on which the models are trained independently. Finally, employing symbolic regression on the learned HGNN, we infer the underlying equations relating the energy functionals, even for complex systems such as the binary Lennard-Jones liquid. Our framework facilitates the interpretable discovery of interaction laws directly from physical system trajectories. Furthermore, this approach can be extended to other systems with topology-dependent dynamics, such as cells, polydisperse gels, or deformable bodies.  ( 2 min )
    Automated Detection of Gait Events and Travel Distance Using Waist-worn Accelerometers Across a Typical Range of Walking and Running Speeds. (arXiv:2307.04866v1 [eess.SP])
    Background: Estimation of temporospatial clinical features of gait (CFs), such as step count and length, step duration, step frequency, gait speed and distance traveled is an important component of community-based mobility evaluation using wearable accelerometers. However, challenges arising from device complexity and availability, cost and analytical methodology have limited widespread application of such tools. Research Question: Can accelerometer data from commercially-available smartphones be used to extract gait CFs across a broad range of attainable gait velocities in children with Duchenne muscular dystrophy (DMD) and typically developing controls (TDs) using machine learning (ML)-based methods Methods: Fifteen children with DMD and 15 TDs underwent supervised clinical testing across a range of gait speeds using 10 or 25m run/walk (10MRW, 25MRW), 100m run/walk (100MRW), 6-minute walk (6MWT) and free-walk (FW) evaluations while wearing a mobile phone-based accelerometer at the waist near the body's center of mass. Gait CFs were extracted from the accelerometer data using a multi-step machine learning-based process and results were compared to ground-truth observation data. Results: Model predictions vs. observed values for step counts, distance traveled, and step length showed a strong correlation (Pearson's r = -0.9929 to 0.9986, p<0.0001). The estimates demonstrated a mean (SD) percentage error of 1.49% (7.04%) for step counts, 1.18% (9.91%) for distance traveled, and 0.37% (7.52%) for step length compared to ground truth observations for the combined 6MWT, 100MRW, and FW tasks. Significance: The study findings indicate that a single accelerometer placed near the body's center of mass can accurately measure CFs across different gait speeds in both TD and DMD peers, suggesting that there is potential for accurately measuring CFs in the community with consumer-level smartphones.  ( 3 min )
    Articulated 3D Head Avatar Generation using Text-to-Image Diffusion Models. (arXiv:2307.04859v1 [cs.CV])
    The ability to generate diverse 3D articulated head avatars is vital to a plethora of applications, including augmented reality, cinematography, and education. Recent work on text-guided 3D object generation has shown great promise in addressing these needs. These methods directly leverage pre-trained 2D text-to-image diffusion models to generate 3D-multi-view-consistent radiance fields of generic objects. However, due to the lack of geometry and texture priors, these methods have limited control over the generated 3D objects, making it difficult to operate inside a specific domain, e.g., human heads. In this work, we develop a new approach to text-guided 3D head avatar generation to address this limitation. Our framework directly operates on the geometry and texture of an articulable 3D morphable model (3DMM) of a head, and introduces novel optimization procedures to update the geometry and texture while keeping the 2D and 3D facial features aligned. The result is a 3D head avatar that is consistent with the text description and can be readily articulated using the deformation model of the 3DMM. We show that our diffusion-based articulated head avatars outperform state-of-the-art approaches for this task. The latter are typically based on CLIP, which is known to provide limited diversity of generation and accuracy for 3D object generation.  ( 2 min )
    Realising Synthetic Active Inference Agents, Part II: Variational Message Updates. (arXiv:2306.02733v2 [stat.ML] UPDATED)
    The Free Energy Principle (FEP) describes (biological) agents as minimising a variational Free Energy (FE) with respect to a generative model of their environment. Active Inference (AIF) is a corollary of the FEP that describes how agents explore and exploit their environment by minimising an expected FE objective. In two related papers, we describe a scalable, epistemic approach to synthetic AIF agents, by message passing on free-form Forney-style Factor Graphs (FFGs). A companion paper (part I) introduces a Constrained FFG (CFFG) notation that visually represents (generalised) FE objectives for AIF. The current paper (part II) derives message passing algorithms that minimise (generalised) FE objectives on a CFFG by variational calculus. A comparison between simulated Bethe and generalised FE agents illustrates how synthetic AIF induces epistemic behaviour on a T-maze navigation task. With a full message passing account of synthetic AIF agents, it becomes possible to derive and reuse message updates across models and move closer to industrial applications of synthetic AIF.  ( 2 min )
    DyCL: Dynamic Neural Network Compilation Via Program Rewriting and Graph Optimization. (arXiv:2307.04963v1 [cs.CL])
    DL compiler's primary function is to translate DNN programs written in high-level DL frameworks such as PyTorch and TensorFlow into portable executables. These executables can then be flexibly executed by the deployed host programs. However, existing DL compilers rely on a tracing mechanism, which involves feeding a runtime input to a neural network program and tracing the program execution paths to generate the computational graph necessary for compilation. Unfortunately, this mechanism falls short when dealing with modern dynamic neural networks (DyNNs) that possess varying computational graphs depending on the inputs. Consequently, conventional DL compilers struggle to accurately compile DyNNs into executable code. To address this limitation, we propose \tool, a general approach that enables any existing DL compiler to successfully compile DyNNs. \tool tackles the dynamic nature of DyNNs by introducing a compilation mechanism that redistributes the control and data flow of the original DNN programs during the compilation process. Specifically, \tool develops program analysis and program transformation techniques to convert a dynamic neural network into multiple sub-neural networks. Each sub-neural network is devoid of conditional statements and is compiled independently. Furthermore, \tool synthesizes a host module that models the control flow of the DyNNs and facilitates the invocation of the sub-neural networks. Our evaluation demonstrates the effectiveness of \tool, achieving a 100\% success rate in compiling all dynamic neural networks. Moreover, the compiled executables generated by \tool exhibit significantly improved performance, running between $1.12\times$ and $20.21\times$ faster than the original DyNNs executed on general-purpose DL frameworks.  ( 3 min )
    Perturbed-History Exploration in Stochastic Linear Bandits. (arXiv:1903.09132v2 [cs.LG] UPDATED)
    We propose a new online algorithm for cumulative regret minimization in a stochastic linear bandit. The algorithm pulls the arm with the highest estimated reward in a linear model trained on its perturbed history. Therefore, we call it perturbed-history exploration in a linear bandit (LinPHE). The perturbed history is a mixture of observed rewards and randomly generated i.i.d. pseudo-rewards. We derive a $\tilde{O}(d \sqrt{n})$ gap-free bound on the $n$-round regret of LinPHE, where $d$ is the number of features. The key steps in our analysis are new concentration and anti-concentration bounds on the weighted sum of Bernoulli random variables. To show the generality of our design, we generalize LinPHE to a logistic model. We evaluate our algorithms empirically and show that they are practical.  ( 2 min )
    Test-Time Training on Video Streams. (arXiv:2307.05014v1 [cs.CV])
    Prior work has established test-time training (TTT) as a general framework to further improve a trained model at test time. Before making a prediction on each test instance, the model is trained on the same instance using a self-supervised task, such as image reconstruction with masked autoencoders. We extend TTT to the streaming setting, where multiple test instances - video frames in our case - arrive in temporal order. Our extension is online TTT: The current model is initialized from the previous model, then trained on the current frame and a small window of frames immediately before. Online TTT significantly outperforms the fixed-model baseline for four tasks, on three real-world datasets. The relative improvement is 45% and 66% for instance and panoptic segmentation. Surprisingly, online TTT also outperforms its offline variant that accesses more information, training on all frames from the entire test video regardless of temporal order. This differs from previous findings using synthetic videos. We conceptualize locality as the advantage of online over offline TTT. We analyze the role of locality with ablations and a theory based on bias-variance trade-off.  ( 2 min )
    Accelerated Discovery of Machine-Learned Symmetries: Deriving the Exceptional Lie Groups G2, F4 and E6. (arXiv:2307.04891v1 [hep-th])
    Recent work has applied supervised deep learning to derive continuous symmetry transformations that preserve the data labels and to obtain the corresponding algebras of symmetry generators. This letter introduces two improved algorithms that significantly speed up the discovery of these symmetry transformations. The new methods are demonstrated by deriving the complete set of generators for the unitary groups U(n) and the exceptional Lie groups $G_2$, $F_4$, and $E_6$. A third post-processing algorithm renders the found generators in sparse form. We benchmark the performance improvement of the new algorithms relative to the standard approach. Given the significant complexity of the exceptional Lie groups, our results demonstrate that this machine-learning method for discovering symmetries is completely general and can be applied to a wide variety of labeled datasets.  ( 2 min )
    Monotone deep Boltzmann machines. (arXiv:2307.04990v1 [cs.LG])
    Deep Boltzmann machines (DBMs), one of the first ``deep'' learning methods ever studied, are multi-layered probabilistic models governed by a pairwise energy function that describes the likelihood of all variables/nodes in the network. In practice, DBMs are often constrained, i.e., via the \emph{restricted} Boltzmann machine (RBM) architecture (which does not permit intra-layer connections), in order to allow for more efficient inference. In this work, we revisit the generic DBM approach, and ask the question: are there other possible restrictions to their design that would enable efficient (approximate) inference? In particular, we develop a new class of restricted model, the monotone DBM, which allows for arbitrary self-connection in each layer, but restricts the \emph{weights} in a manner that guarantees the existence and global uniqueness of a mean-field fixed point. To do this, we leverage tools from the recently-proposed monotone Deep Equilibrium model and show that a particular choice of activation results in a fixed-point iteration that gives a variational mean-field solution. While this approach is still largely conceptual, it is the first architecture that allows for efficient approximate inference in fully-general weight structures for DBMs. We apply this approach to simple deep convolutional Boltzmann architectures and demonstrate that it allows for tasks such as the joint completion and classification of images, within a single deep probabilistic setting, while avoiding the pitfalls of mean-field inference in traditional RBMs.  ( 2 min )
    Human Emotion Recognition Based On Galvanic Skin Response signal Feature Selection and SVM. (arXiv:2307.05383v1 [eess.SP])
    A novel human emotion recognition method based on automatically selected Galvanic Skin Response (GSR) signal features and SVM is proposed in this paper. GSR signals were acquired by e-Health Sensor Platform V2.0. Then, the data is de-noised by wavelet function and normalized to get rid of the individual difference. 30 features are extracted from the normalized data, however, directly using of these features will lead to a low recognition rate. In order to gain the optimized features, a covariance based feature selection is employed in our method. Finally, a SVM with input of the optimized features is utilized to achieve the human emotion recognition. The experimental results indicate that the proposed method leads to good human emotion recognition, and the recognition accuracy is more than 66.67%.  ( 2 min )
    A physics-constrained machine learning method for mapping gapless land surface temperature. (arXiv:2307.04817v1 [physics.ao-ph])
    More accurate, spatio-temporally, and physically consistent LST estimation has been a main interest in Earth system research. Developing physics-driven mechanism models and data-driven machine learning (ML) models are two major paradigms for gapless LST estimation, which have their respective advantages and disadvantages. In this paper, a physics-constrained ML model, which combines the strengths in the mechanism model and ML model, is proposed to generate gapless LST with physical meanings and high accuracy. The hybrid model employs ML as the primary architecture, under which the input variable physical constraints are incorporated to enhance the interpretability and extrapolation ability of the model. Specifically, the light gradient-boosting machine (LGBM) model, which uses only remote sensing data as input, serves as the pure ML model. Physical constraints (PCs) are coupled by further incorporating key Community Land Model (CLM) forcing data (cause) and CLM simulation data (effect) as inputs into the LGBM model. This integration forms the PC-LGBM model, which incorporates surface energy balance (SEB) constraints underlying the data in CLM-LST modeling within a biophysical framework. Compared with a pure physical method and pure ML methods, the PC-LGBM model improves the prediction accuracy and physical interpretability of LST. It also demonstrates a good extrapolation ability for the responses to extreme weather cases, suggesting that the PC-LGBM model enables not only empirical learning from data but also rationally derived from theory. The proposed method represents an innovative way to map accurate and physically interpretable gapless LST, and could provide insights to accelerate knowledge discovery in land surface processes and data mining in geographical parameter estimation.  ( 3 min )
    Predicting Outcomes in Long COVID Patients with Spatiotemporal Attention. (arXiv:2307.04770v1 [cs.LG])
    Long COVID is a general term of post-acute sequelae of COVID-19. Patients with long COVID can endure long-lasting symptoms including fatigue, headache, dyspnea and anosmia, etc. Identifying the cohorts with severe long-term complications in COVID-19 could benefit the treatment planning and resource arrangement. However, due to the heterogeneous phenotype presented in long COVID patients, it is difficult to predict their outcomes from their longitudinal data. In this study, we proposed a spatiotemporal attention mechanism to weigh feature importance jointly from the temporal dimension and feature space. Considering that medical examinations can have interchangeable orders in adjacent time points, we restricted the learning of short-term dependency with a Local-LSTM and the learning of long-term dependency with the joint spatiotemporal attention. We also compared the proposed method with several state-of-the-art methods and a method in clinical practice. The methods are evaluated on a hard-to-acquire clinical dataset of patients with long COVID. Experimental results show the Local-LSTM with joint spatiotemporal attention outperformed related methods in outcome prediction. The proposed method provides a clinical tool for the severity assessment of long COVID.  ( 2 min )
    Collaborative Score Distillation for Consistent Visual Synthesis. (arXiv:2307.04787v1 [cs.CV])
    Generative priors of large-scale text-to-image diffusion models enable a wide range of new generation and editing applications on diverse visual modalities. However, when adapting these priors to complex visual modalities, often represented as multiple images (e.g., video), achieving consistency across a set of images is challenging. In this paper, we address this challenge with a novel method, Collaborative Score Distillation (CSD). CSD is based on the Stein Variational Gradient Descent (SVGD). Specifically, we propose to consider multiple samples as "particles" in the SVGD update and combine their score functions to distill generative priors over a set of images synchronously. Thus, CSD facilitates seamless integration of information across 2D images, leading to a consistent visual synthesis across multiple samples. We show the effectiveness of CSD in a variety of tasks, encompassing the visual editing of panorama images, videos, and 3D scenes. Our results underline the competency of CSD as a versatile method for enhancing inter-sample consistency, thereby broadening the applicability of text-to-image diffusion models.  ( 2 min )
    On Detecting Some Defective Items in Group Testing. (arXiv:2307.04822v1 [cs.DS])
    Group testing is an approach aimed at identifying up to $d$ defective items among a total of $n$ elements. This is accomplished by examining subsets to determine if at least one defective item is present. In our study, we focus on the problem of identifying a subset of $\ell\leq d$ defective items. We develop upper and lower bounds on the number of tests required to detect $\ell$ defective items in both the adaptive and non-adaptive settings while considering scenarios where no prior knowledge of $d$ is available, and situations where an estimate of $d$ or at least some non-trivial upper bound on $d$ is available. When no prior knowledge on $d$ is available, we prove a lower bound of $ \Omega(\frac{\ell \log^2n}{\log \ell +\log\log n})$ tests in the randomized non-adaptive settings and an upper bound of $O(\ell \log^2 n)$ for the same settings. Furthermore, we demonstrate that any non-adaptive deterministic algorithm must ask $\Theta(n)$ tests, signifying a fundamental limitation in this scenario. For adaptive algorithms, we establish tight bounds in different scenarios. In the deterministic case, we prove a tight bound of $\Theta(\ell\log{(n/\ell)})$. Moreover, in the randomized settings, we derive a tight bound of $\Theta(\ell\log{(n/d)})$. When $d$, or at least some non-trivial estimate of $d$, is known, we prove a tight bound of $\Theta(d\log (n/d))$ for the deterministic non-adaptive settings, and $\Theta(\ell\log(n/d))$ for the randomized non-adaptive settings. In the adaptive case, we present an upper bound of $O(\ell \log (n/\ell))$ for the deterministic settings, and a lower bound of $\Omega(\ell\log(n/d)+\log n)$. Additionally, we establish a tight bound of $\Theta(\ell \log(n/d))$ for the randomized adaptive settings.  ( 3 min )
    Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation. (arXiv:2307.04780v1 [cs.LG])
    Score based generative models are a new class of generative models that have been shown to accurately generate high dimensional calorimeter datasets. Recent advances in generative models have used images with 3D voxels to represent and model complex calorimeter showers. Point clouds, however, are likely a more natural representation of calorimeter showers, particularly in calorimeters with high granularity. Point clouds preserve all of the information of the original simulation, more naturally deal with sparse datasets, and can be implemented with more compact models and data files. In this work, two state-of-the-art score based models are trained on the same set of calorimeter simulation and directly compared.  ( 2 min )
    Formulating A Strategic Plan Based On Statistical Analyses And Applications For Financial Companies Through A Real-World Use Case. (arXiv:2307.04778v1 [cs.LG])
    Business statistics play a crucial role in implementing a data-driven strategic plan at the enterprise level to employ various analytics where the outcomes of such a plan enable an enterprise to enhance the decision-making process or to mitigate risks to the organization. In this work, a strategic plan informed by the statistical analysis is introduced for a financial company called LendingClub, where the plan is comprised of exploring the possibility of onboarding a big data platform along with advanced feature selection capacities. The main objectives of such a plan are to increase the company's revenue while reducing the risks of granting loans to borrowers who cannot return their loans. In this study, different hypotheses formulated to address the company's concerns are studied, where the results reveal that the amount of loans profoundly impacts the number of borrowers charging off their loans. Also, the proposed strategic plan includes onboarding advanced analytics such as machine learning technologies that allow the company to build better generalized data-driven predictive models.  ( 2 min )
    MentalHealthAI: Utilizing Personal Health Device Data to Optimize Psychiatry Treatment. (arXiv:2307.04777v1 [cs.LG])
    Mental health disorders remain a significant challenge in modern healthcare, with diagnosis and treatment often relying on subjective patient descriptions and past medical history. To address this issue, we propose a personalized mental health tracking and mood prediction system that utilizes patient physiological data collected through personal health devices. Our system leverages a decentralized learning mechanism that combines transfer and federated machine learning concepts using smart contracts, allowing data to remain on users' devices and enabling effective tracking of mental health conditions for psychiatric treatment and management in a privacy-aware and accountable manner. We evaluate our model using a popular mental health dataset that demonstrates promising results. By utilizing connected health systems and machine learning models, our approach offers a novel solution to the challenge of providing psychiatrists with further insight into their patients' mental health outside of traditional office visits.  ( 2 min )
    Digital Twins for Patient Care via Knowledge Graphs and Closed-Form Continuous-Time Liquid Neural Networks. (arXiv:2307.04772v1 [cs.LG])
    Digital twin technology has is anticipated to transform healthcare, enabling personalized medicines and support, earlier diagnoses, simulated treatment outcomes, and optimized surgical plans. Digital twins are readily gaining traction in industries like manufacturing, supply chain logistics, and civil infrastructure. Not in patient care, however. The challenge of modeling complex diseases with multimodal patient data and the computational complexities of analyzing it have stifled digital twin adoption in the biomedical vertical. Yet, these major obstacles can potentially be handled by approaching these models in a different way. This paper proposes a novel framework for addressing the barriers to clinical twin modeling created by computational costs and modeling complexities. We propose structuring patient health data as a knowledge graph and using closed-form continuous-time liquid neural networks, for real-time analytics. By synthesizing multimodal patient data and leveraging the flexibility and efficiency of closed form continuous time networks and knowledge graph ontologies, our approach enables real time insights, personalized medicine, early diagnosis and intervention, and optimal surgical planning. This novel approach provides a comprehensive and adaptable view of patient health along with real-time analytics, paving the way for digital twin simulations and other anticipated benefits in healthcare.  ( 2 min )
  • Open

    Optimal Algorithms for Latent Bandits with Cluster Structure. (arXiv:2301.07040v3 [cs.LG] UPDATED)
    We consider the problem of latent bandits with cluster structure where there are multiple users, each with an associated multi-armed bandit problem. These users are grouped into \emph{latent} clusters such that the mean reward vectors of users within the same cluster are identical. At each round, a user, selected uniformly at random, pulls an arm and observes a corresponding noisy reward. The goal of the users is to maximize their cumulative rewards. This problem is central to practical recommendation systems and has received wide attention of late \cite{gentile2014online, maillard2014latent}. Now, if each user acts independently, then they would have to explore each arm independently and a regret of $\Omega(\sqrt{\mathsf{MNT}})$ is unavoidable, where $\mathsf{M}, \mathsf{N}$ are the number of arms and users, respectively. Instead, we propose LATTICE (Latent bAndiTs via maTrIx ComplEtion) which allows exploitation of the latent cluster structure to provide the minimax optimal regret of $\widetilde{O}(\sqrt{(\mathsf{M}+\mathsf{N})\mathsf{T}})$, when the number of clusters is $\widetilde{O}(1)$. This is the first algorithm to guarantee such strong regret bound. LATTICE is based on a careful exploitation of arm information within a cluster while simultaneously clustering users. Furthermore, it is computationally efficient and requires only $O(\log{\mathsf{T}})$ calls to an offline matrix completion oracle across all $\mathsf{T}$ rounds.
    Conformalization of Sparse Generalized Linear Models. (arXiv:2307.05109v1 [cs.LG])
    Given a sequence of observable variables $\{(x_1, y_1), \ldots, (x_n, y_n)\}$, the conformal prediction method estimates a confidence set for $y_{n+1}$ given $x_{n+1}$ that is valid for any finite sample size by merely assuming that the joint distribution of the data is permutation invariant. Although attractive, computing such a set is computationally infeasible in most regression problems. Indeed, in these cases, the unknown variable $y_{n+1}$ can take an infinite number of possible candidate values, and generating conformal sets requires retraining a predictive model for each candidate. In this paper, we focus on a sparse linear model with only a subset of variables for prediction and use numerical continuation techniques to approximate the solution path efficiently. The critical property we exploit is that the set of selected variables is invariant under a small perturbation of the input data. Therefore, it is sufficient to enumerate and refit the model only at the change points of the set of active features and smoothly interpolate the rest of the solution via a Predictor-Corrector mechanism. We show how our path-following algorithm accurately approximates conformal prediction sets and illustrate its performance using synthetic and real data examples.
    Differentially Private Statistical Inference through $\beta$-Divergence One Posterior Sampling. (arXiv:2307.05194v1 [stat.ML])
    Differential privacy guarantees allow the results of a statistical analysis involving sensitive data to be released without compromising the privacy of any individual taking part. Achieving such guarantees generally requires the injection of noise, either directly into parameter estimates or into the estimation process. Instead of artificially introducing perturbations, sampling from Bayesian posterior distributions has been shown to be a special case of the exponential mechanism, producing consistent, and efficient private estimates without altering the data generative process. The application of current approaches has, however, been limited by their strong bounding assumptions which do not hold for basic models, such as simple linear regressors. To ameliorate this, we propose $\beta$D-Bayes, a posterior sampling scheme from a generalised posterior targeting the minimisation of the $\beta$-divergence between the model and the data generating process. This provides private estimation that is generally applicable without requiring changes to the underlying model and consistently learns the data generating parameter. We show that $\beta$D-Bayes produces more precise inference estimation for the same privacy guarantees, and further facilitates differentially private estimation via posterior sampling for complex classifiers and continuous regression models such as neural networks for the first time.
    Hybrid hidden Markov LSTM for short-term traffic flow prediction. (arXiv:2307.04954v1 [cs.LG])
    Deep learning (DL) methods have outperformed parametric models such as historical average, ARIMA and variants in predicting traffic variables into short and near-short future, that are critical for traffic management. Specifically, recurrent neural network (RNN) and its variants (e.g. long short-term memory) are designed to retain long-term temporal correlations and therefore are suitable for modeling sequences. However, multi-regime models assume the traffic system to evolve through multiple states (say, free-flow, congestion in traffic) with distinct characteristics, and hence, separate models are trained to characterize the traffic dynamics within each regime. For instance, Markov-switching models with a hidden Markov model (HMM) for regime identification is capable of capturing complex dynamic patterns and non-stationarity. Interestingly, both HMM and LSTM can be used for modeling an observation sequence from a set of latent or, hidden state variables. In LSTM, the latent variable is computed in a deterministic manner from the current observation and the previous latent variable, while, in HMM, the set of latent variables is a Markov chain. Inspired by research in natural language processing, a hybrid hidden Markov-LSTM model that is capable of learning complementary features in traffic data is proposed for traffic flow prediction. Results indicate significant performance gains in using hybrid architecture compared to conventional methods such as Markov switching ARIMA and LSTM.
    Diagnosing Model Performance Under Distribution Shift. (arXiv:2303.02011v4 [stat.ML] UPDATED)
    Prediction models can perform poorly when deployed to target distributions different from the training distribution. To understand these operational failure modes, we develop a method, called DIstribution Shift DEcomposition (DISDE), to attribute a drop in performance to different types of distribution shifts. Our approach decomposes the performance drop into terms for 1) an increase in harder but frequently seen examples from training, 2) changes in the relationship between features and outcomes, and 3) poor performance on examples infrequent or unseen during training. These terms are defined by fixing a distribution on $X$ while varying the conditional distribution of $Y \mid X$ between training and target, or by fixing the conditional distribution of $Y \mid X$ while varying the distribution on $X$. In order to do this, we define a hypothetical distribution on $X$ consisting of values common in both training and target, over which it is easy to compare $Y \mid X$ and thus predictive performance. We estimate performance on this hypothetical distribution via reweighting methods. Empirically, we show how our method can 1) inform potential modeling improvements across distribution shifts for employment prediction on tabular census data, and 2) help to explain why certain domain adaptation methods fail to improve model performance for satellite image classification.
    MAP- and MLE-Based Teaching. (arXiv:2307.05252v1 [cs.LG])
    Imagine a learner L who tries to infer a hidden concept from a collection of observations. Building on the work [4] of Ferri et al., we assume the learner to be parameterized by priors P(c) and by c-conditional likelihoods P(z|c) where c ranges over all concepts in a given class C and z ranges over all observations in an observation set Z. L is called a MAP-learner (resp. an MLE-learner) if it thinks of a collection S of observations as a random sample and returns the concept with the maximum a-posteriori probability (resp. the concept which maximizes the c-conditional likelihood of S). Depending on whether L assumes that S is obtained from ordered or unordered sampling resp. from sampling with or without replacement, we can distinguish four different sampling modes. Given a target concept c in C, a teacher for a MAP-learner L aims at finding a smallest collection of observations that causes L to return c. This approach leads in a natural manner to various notions of a MAP- or MLE-teaching dimension of a concept class C. Our main results are: We show that this teaching model has some desirable monotonicity properties. We clarify how the four sampling modes are related to each other. As for the (important!) special case, where concepts are subsets of a domain and observations are 0,1-labeled examples, we obtain some additional results. First of all, we characterize the MAP- and MLE-teaching dimension associated with an optimally parameterized MAP-learner graph-theoretically. From this central result, some other ones are easy to derive. It is shown, for instance, that the MLE-teaching dimension is either equal to the MAP-teaching dimension or exceeds the latter by 1. It is shown furthermore that these dimensions can be bounded from above by the so-called antichain number, the VC-dimension and related combinatorial parameters. Moreover they can be computed in polynomial time.
    Normalized mutual information is a biased measure for classification and community detection. (arXiv:2307.01282v1 [cs.SI] CROSS LISTED)
    Normalized mutual information is widely used as a similarity measure for evaluating the performance of clustering and classification algorithms. In this paper, we show that results returned by the normalized mutual information are biased for two reasons: first, because they ignore the information content of the contingency table and, second, because their symmetric normalization introduces spurious dependence on algorithm output. We introduce a modified version of the mutual information that remedies both of these shortcomings. As a practical demonstration of the importance of using an unbiased measure, we perform extensive numerical tests on a basket of popular algorithms for network community detection and show that one's conclusions about which algorithm is best are significantly affected by the biases in the traditional mutual information.
    Robust Inference of Manifold Density and Geometry by Doubly Stochastic Scaling. (arXiv:2209.08004v2 [math.ST] UPDATED)
    The Gaussian kernel and its traditional normalizations (e.g., row-stochastic) are popular approaches for assessing similarities between data points. Yet, they can be inaccurate under high-dimensional noise, especially if the noise magnitude varies considerably across the data, e.g., under heteroskedasticity or outliers. In this work, we investigate a more robust alternative -- the doubly stochastic normalization of the Gaussian kernel. We consider a setting where points are sampled from an unknown density on a low-dimensional manifold embedded in high-dimensional space and corrupted by possibly strong, non-identically distributed, sub-Gaussian noise. We establish that the doubly stochastic affinity matrix and its scaling factors concentrate around certain population forms, and provide corresponding finite-sample probabilistic error bounds. We then utilize these results to develop several tools for robust inference under general high-dimensional noise. First, we derive a robust density estimator that reliably infers the underlying sampling density and can substantially outperform the standard kernel density estimator under heteroskedasticity and outliers. Second, we obtain estimators for the pointwise noise magnitudes, the pointwise signal magnitudes, and the pairwise Euclidean distances between clean data points. Lastly, we derive robust graph Laplacian normalizations that accurately approximate various manifold Laplacians, including the Laplace Beltrami operator, improving over traditional normalizations in noisy settings. We exemplify our results in simulations and on real single-cell RNA-sequencing data. For the latter, we show that in contrast to traditional methods, our approach is robust to variability in technical noise levels across cell types.
    Selective Sampling and Imitation Learning via Online Regression. (arXiv:2307.04998v1 [cs.LG])
    We consider the problem of Imitation Learning (IL) by actively querying noisy expert for feedback. While imitation learning has been empirically successful, much of prior work assumes access to noiseless expert feedback which is not practical in many applications. In fact, when one only has access to noisy expert feedback, algorithms that rely on purely offline data (non-interactive IL) can be shown to need a prohibitively large number of samples to be successful. In contrast, in this work, we provide an interactive algorithm for IL that uses selective sampling to actively query the noisy expert for feedback. Our contributions are twofold: First, we provide a new selective sampling algorithm that works with general function classes and multiple actions, and obtains the best-known bounds for the regret and the number of queries. Next, we extend this analysis to the problem of IL with noisy expert feedback and provide a new IL algorithm that makes limited queries. Our algorithm for selective sampling leverages function approximation, and relies on an online regression oracle w.r.t.~the given model class to predict actions, and to decide whether to query the expert for its label. On the theoretical side, the regret bound of our algorithm is upper bounded by the regret of the online regression oracle, while the query complexity additionally depends on the eluder dimension of the model class. We complement this with a lower bound that demonstrates that our results are tight. We extend our selective sampling algorithm for IL with general function approximation and provide bounds on both the regret and the number of queries made to the noisy expert. A key novelty here is that our regret and query complexity bounds only depend on the number of times the optimal policy (and not the noisy expert, or the learner) go to states that have a small margin.
    The Statistical Complexity of Interactive Decision Making. (arXiv:2112.13487v3 [cs.LG] UPDATED)
    A fundamental challenge in interactive learning and decision making, ranging from bandit problems to reinforcement learning, is to provide sample-efficient, adaptive learning algorithms that achieve near-optimal regret. This question is analogous to the classical problem of optimal (supervised) statistical learning, where there are well-known complexity measures (e.g., VC dimension and Rademacher complexity) that govern the statistical complexity of learning. However, characterizing the statistical complexity of interactive learning is substantially more challenging due to the adaptive nature of the problem. The main result of this work provides a complexity measure, the Decision-Estimation Coefficient, that is proven to be both necessary and sufficient for sample-efficient interactive learning. In particular, we provide: 1. a lower bound on the optimal regret for any interactive decision making problem, establishing the Decision-Estimation Coefficient as a fundamental limit. 2. a unified algorithm design principle, Estimation-to-Decisions (E2D), which transforms any algorithm for supervised estimation into an online algorithm for decision making. E2D attains a regret bound that matches our lower bound up to dependence on a notion of estimation performance, thereby achieving optimal sample-efficient learning as characterized by the Decision-Estimation Coefficient. Taken together, these results constitute a theory of learnability for interactive decision making. When applied to reinforcement learning settings, the Decision-Estimation Coefficient recovers essentially all existing hardness results and lower bounds. More broadly, the approach can be viewed as a decision-theoretic analogue of the classical Le Cam theory of statistical estimation; it also unifies a number of existing approaches -- both Bayesian and frequentist.
    Realising Synthetic Active Inference Agents, Part II: Variational Message Updates. (arXiv:2306.02733v2 [stat.ML] UPDATED)
    The Free Energy Principle (FEP) describes (biological) agents as minimising a variational Free Energy (FE) with respect to a generative model of their environment. Active Inference (AIF) is a corollary of the FEP that describes how agents explore and exploit their environment by minimising an expected FE objective. In two related papers, we describe a scalable, epistemic approach to synthetic AIF agents, by message passing on free-form Forney-style Factor Graphs (FFGs). A companion paper (part I) introduces a Constrained FFG (CFFG) notation that visually represents (generalised) FE objectives for AIF. The current paper (part II) derives message passing algorithms that minimise (generalised) FE objectives on a CFFG by variational calculus. A comparison between simulated Bethe and generalised FE agents illustrates how synthetic AIF induces epistemic behaviour on a T-maze navigation task. With a full message passing account of synthetic AIF agents, it becomes possible to derive and reuse message updates across models and move closer to industrial applications of synthetic AIF.
    Randomized Exploration in Generalized Linear Bandits. (arXiv:1906.08947v3 [cs.LG] UPDATED)
    We study two randomized algorithms for generalized linear bandits. The first, GLM-TSL, samples a generalized linear model (GLM) from the Laplace approximation to the posterior distribution. The second, GLM-FPL, fits a GLM to a randomly perturbed history of past rewards. We analyze both algorithms and derive $\tilde{O}(d \sqrt{n \log K})$ upper bounds on their $n$-round regret, where $d$ is the number of features and $K$ is the number of arms. The former improves on prior work while the latter is the first for Gaussian noise perturbations in non-linear models. We empirically evaluate both GLM-TSL and GLM-FPL in logistic bandits, and apply GLM-FPL to neural network bandits. Our work showcases the role of randomization, beyond posterior sampling, in exploration.
    Tracking Most Significant Shifts in Nonparametric Contextual Bandits. (arXiv:2307.05341v1 [stat.ML])
    We study nonparametric contextual bandits where Lipschitz mean reward functions may change over time. We first establish the minimax dynamic regret rate in this less understood setting in terms of number of changes $L$ and total-variation $V$, both capturing all changes in distribution over context space, and argue that state-of-the-art procedures are suboptimal in this setting. Next, we tend to the question of an adaptivity for this setting, i.e. achieving the minimax rate without knowledge of $L$ or $V$. Quite importantly, we posit that the bandit problem, viewed locally at a given context $X_t$, should not be affected by reward changes in other parts of context space $\cal X$. We therefore propose a notion of change, which we term experienced significant shifts, that better accounts for locality, and thus counts considerably less changes than $L$ and $V$. Furthermore, similar to recent work on non-stationary MAB (Suk & Kpotufe, 2022), experienced significant shifts only count the most significant changes in mean rewards, e.g., severe best-arm changes relevant to observed contexts. Our main result is to show that this more tolerant notion of change can in fact be adapted to.
    Prediction intervals for neural network models using weighted asymmetric loss functions. (arXiv:2210.04318v4 [stat.ML] UPDATED)
    We propose a simple and efficient approach to generate a prediction intervals (PI) for approximated and forecasted trends. Our method leverages a weighted asymmetric loss function to estimate the lower and upper bounds of the PI, with the weights determined by its coverage probability. We provide a concise mathematical proof of the method, show how it can be extended to derive PIs for parametrised functions and argue why the method works for predicting PIs of dependent variables. The presented tests of the method on a real-world forecasting task using a neural network-based model show that it can produce reliable PIs in complex machine learning scenarios.
    Stochastic Nested Compositional Bi-level Optimization for Robust Feature Learning. (arXiv:2307.05384v1 [math.OC])
    We develop and analyze stochastic approximation algorithms for solving nested compositional bi-level optimization problems. These problems involve a nested composition of $T$ potentially non-convex smooth functions in the upper-level, and a smooth and strongly convex function in the lower-level. Our proposed algorithm does not rely on matrix inversions or mini-batches and can achieve an $\epsilon$-stationary solution with an oracle complexity of approximately $\tilde{O}_T(1/\epsilon^{2})$, assuming the availability of stochastic first-order oracles for the individual functions in the composition and the lower-level, which are unbiased and have bounded moments. Here, $\tilde{O}_T$ hides polylog factors and constants that depend on $T$. The key challenge we address in establishing this result relates to handling three distinct sources of bias in the stochastic gradients. The first source arises from the compositional nature of the upper-level, the second stems from the bi-level structure, and the third emerges due to the utilization of Neumann series approximations to avoid matrix inversion. To demonstrate the effectiveness of our approach, we apply it to the problem of robust feature learning for deep neural networks under covariate shift, showcasing the benefits and advantages of our methodology in that context.
    The Implicit Bias of Batch Normalization in Linear Models and Two-layer Linear Convolutional Neural Networks. (arXiv:2306.11680v2 [cs.LG] UPDATED)
    We study the implicit bias of batch normalization trained by gradient descent. We show that when learning a linear model with batch normalization for binary classification, gradient descent converges to a uniform margin classifier on the training data with an $\exp(-\Omega(\log^2 t))$ convergence rate. This distinguishes linear models with batch normalization from those without batch normalization in terms of both the type of implicit bias and the convergence rate. We further extend our result to a class of two-layer, single-filter linear convolutional neural networks, and show that batch normalization has an implicit bias towards a patch-wise uniform margin. Based on two examples, we demonstrate that patch-wise uniform margin classifiers can outperform the maximum margin classifiers in certain learning problems. Our results contribute to a better theoretical understanding of batch normalization.
    Comparison of High-Dimensional Bayesian Optimization Algorithms on BBOB. (arXiv:2303.00890v2 [cs.LG] UPDATED)
    Bayesian Optimization (BO) is a class of black-box, surrogate-based heuristics that can efficiently optimize problems that are expensive to evaluate, and hence admit only small evaluation budgets. BO is particularly popular for solving numerical optimization problems in industry, where the evaluation of objective functions often relies on time-consuming simulations or physical experiments. However, many industrial problems depend on a large number of parameters. This poses a challenge for BO algorithms, whose performance is often reported to suffer when the dimension grows beyond 15 variables. Although many new algorithms have been proposed to address this problem, it is not well understood which one is the best for which optimization scenario. In this work, we compare five state-of-the-art high-dimensional BO algorithms, with vanilla BO and CMA-ES on the 24 BBOB functions of the COCO environment at increasing dimensionality, ranging from 10 to 60 variables. Our results confirm the superiority of BO over CMA-ES for limited evaluation budgets and suggest that the most promising approach to improve BO is the use of trust regions. However, we also observe significant performance differences for different function landscapes and budget exploitation phases, indicating improvement potential, e.g., through hybridization of algorithmic components.
    A stochastic optimization approach to minimize robust density power-based divergences for general parametric density models. (arXiv:2307.05251v1 [stat.ME])
    Density power divergence (DPD) [Basu et al. (1998), Biometrika], designed to estimate the underlying distribution of the observations robustly, comprises an integral term of the power of the parametric density models to be estimated. While the explicit form of the integral term can be obtained for some specific densities (such as normal density and exponential density), its computational intractability has prohibited the application of DPD-based estimation to more general parametric densities, over a quarter of a century since the proposal of DPD. This study proposes a stochastic optimization approach to minimize DPD for general parametric density models and explains its adequacy by referring to conventional theories on stochastic optimization. The proposed approach also can be applied to the minimization of another density power-based $\gamma$-divergence with the aid of unnormalized models [Kanamori and Fujisawa (2015), Biometrika].
    BayesFlow: Amortized Bayesian Workflows With Neural Networks. (arXiv:2306.16015v2 [cs.LG] UPDATED)
    Modern Bayesian inference involves a mixture of computational techniques for estimating, validating, and drawing conclusions from probabilistic models as part of principled workflows for data analysis. Typical problems in Bayesian workflows are the approximation of intractable posterior distributions for diverse model types and the comparison of competing models of the same process in terms of their complexity and predictive performance. This manuscript introduces the Python library BayesFlow for simulation-based training of established neural network architectures for amortized data compression and inference. Amortized Bayesian inference, as implemented in BayesFlow, enables users to train custom neural networks on model simulations and re-use these networks for any subsequent application of the models. Since the trained networks can perform inference almost instantaneously, the upfront neural network training is quickly amortized.
    Geometric Neural Diffusion Processes. (arXiv:2307.05431v1 [stat.ML])
    Denoising diffusion models have proven to be a flexible and effective paradigm for generative modelling. Their recent extension to infinite dimensional Euclidean spaces has allowed for the modelling of stochastic processes. However, many problems in the natural sciences incorporate symmetries and involve data living in non-Euclidean spaces. In this work, we extend the framework of diffusion models to incorporate a series of geometric priors in infinite-dimension modelling. We do so by a) constructing a noising process which admits, as limiting distribution, a geometric Gaussian process that transforms under the symmetry group of interest, and b) approximating the score with a neural network that is equivariant w.r.t. this group. We show that with these conditions, the generative functional model admits the same symmetry. We demonstrate scalability and capacity of the model, using a novel Langevin-based conditional sampler, to fit complex scalar and vector fields, with Euclidean and spherical codomain, on synthetic and real-world weather data.
    Leveraging Variational Autoencoders for Parameterized MMSE Channel Estimation. (arXiv:2307.05352v1 [eess.SP])
    In this manuscript, we propose to utilize the generative neural network-based variational autoencoder for channel estimation. The variational autoencoder models the underlying true but unknown channel distribution as a conditional Gaussian distribution in a novel way. The derived channel estimator exploits the internal structure of the variational autoencoder to parameterize an approximation of the mean squared error optimal estimator resulting from the conditional Gaussian channel models. We provide a rigorous analysis under which conditions a variational autoencoder-based estimator is mean squared error optimal. We then present considerations that make the variational autoencoder-based estimator practical and propose three different estimator variants that differ in their access to channel knowledge during the training and evaluation phase. In particular, the proposed estimator variant trained solely on noisy pilot observations is particularly noteworthy as it does not require access to noise-free, ground-truth channel data during training or evaluation. Extensive numerical simulations first analyze the internal behavior of the variational autoencoder-based estimators and then demonstrate excellent channel estimation performance compared to related classical and machine learning-based state-of-the-art channel estimators.
    Reinforcement Learning with Non-Cumulative Objective. (arXiv:2307.04957v1 [cs.LG])
    In reinforcement learning, the objective is almost always defined as a \emph{cumulative} function over the rewards along the process. However, there are many optimal control and reinforcement learning problems in various application fields, especially in communications and networking, where the objectives are not naturally expressed as summations of the rewards. In this paper, we recognize the prevalence of non-cumulative objectives in various problems, and propose a modification to existing algorithms for optimizing such objectives. Specifically, we dive into the fundamental building block for many optimal control and reinforcement learning algorithms: the Bellman optimality equation. To optimize a non-cumulative objective, we replace the original summation operation in the Bellman update rule with a generalized operation corresponding to the objective. Furthermore, we provide sufficient conditions on the form of the generalized operation as well as assumptions on the Markov decision process under which the globally optimal convergence of the generalized Bellman updates can be guaranteed. We demonstrate the idea experimentally with the bottleneck objective, i.e., the objectives determined by the minimum reward along the process, on classical optimal control and reinforcement learning tasks, as well as on two network routing problems on maximizing the flow rates.
    Law of Large Numbers for Bayesian two-layer Neural Network trained with Variational Inference. (arXiv:2307.04779v1 [stat.ML])
    We provide a rigorous analysis of training by variational inference (VI) of Bayesian neural networks in the two-layer and infinite-width case. We consider a regression problem with a regularized evidence lower bound (ELBO) which is decomposed into the expected log-likelihood of the data and the Kullback-Leibler (KL) divergence between the a priori distribution and the variational posterior. With an appropriate weighting of the KL, we prove a law of large numbers for three different training schemes: (i) the idealized case with exact estimation of a multiple Gaussian integral from the reparametrization trick, (ii) a minibatch scheme using Monte Carlo sampling, commonly known as Bayes by Backprop, and (iii) a new and computationally cheaper algorithm which we introduce as Minimal VI. An important result is that all methods converge to the same mean-field limit. Finally, we illustrate our results numerically and discuss the need for the derivation of a central limit theorem.
    Perturbed-History Exploration in Stochastic Linear Bandits. (arXiv:1903.09132v2 [cs.LG] UPDATED)
    We propose a new online algorithm for cumulative regret minimization in a stochastic linear bandit. The algorithm pulls the arm with the highest estimated reward in a linear model trained on its perturbed history. Therefore, we call it perturbed-history exploration in a linear bandit (LinPHE). The perturbed history is a mixture of observed rewards and randomly generated i.i.d. pseudo-rewards. We derive a $\tilde{O}(d \sqrt{n})$ gap-free bound on the $n$-round regret of LinPHE, where $d$ is the number of features. The key steps in our analysis are new concentration and anti-concentration bounds on the weighted sum of Bernoulli random variables. To show the generality of our design, we generalize LinPHE to a logistic model. We evaluate our algorithms empirically and show that they are practical.
    Dynamics of Temporal Difference Reinforcement Learning. (arXiv:2307.04841v1 [stat.ML])
    Reinforcement learning has been successful across several applications in which agents have to learn to act in environments with sparse feedback. However, despite this empirical success there is still a lack of theoretical understanding of how the parameters of reinforcement learning models and the features used to represent states interact to control the dynamics of learning. In this work, we use concepts from statistical physics, to study the typical case learning curves for temporal difference learning of a value function with linear function approximators. Our theory is derived under a Gaussian equivalence hypothesis where averages over the random trajectories are replaced with temporally correlated Gaussian feature averages and we validate our assumptions on small scale Markov Decision Processes. We find that the stochastic semi-gradient noise due to subsampling the space of possible episodes leads to significant plateaus in the value error, unlike in traditional gradient descent dynamics. We study how learning dynamics and plateaus depend on feature structure, learning rate, discount factor, and reward function. We then analyze how strategies like learning rate annealing and reward shaping can favorably alter learning dynamics and plateaus. To conclude, our work introduces new tools to open a new direction towards developing a theory of learning dynamics in reinforcement learning.

  • Open

    MAMAY AI-powered algorithm digitises taste for food and beverage makers
    submitted by /u/trueslicky [link] [comments]  ( 8 min )
    Jesus in 2023 lmao
    submitted by /u/reinkrestfoxy [link] [comments]  ( 8 min )
    Can AI replicate a character's voice cracks and raspy voice?
    I used RVC V2 to make an AI model of a character whose voice is raspy, has a lot of voice cracks and has a very "shouty" voice. I used a dataset which was 70% of voice clips of him yelling and/or talking loudly and the rest were of him talking normally. The dataset was decent quality, I did have to use background music remover software on some parts but it's overall decent. The thing is, the model doesn't sound ANYTHING like the character. For some reason it's way too soft spoken, and even when it's supposed to be yelling or screaming it sounds kinda like he's whispering. The AI's neutral voice does sound like him but it's missing his voice cracks and voice raspiness. Is there any way I can mimic it? submitted by /u/donutpancito [link] [comments]  ( 8 min )
    Inside the AI Factory (about the underclass work force)
    submitted by /u/facinabush [link] [comments]  ( 8 min )
    Behind the secretive work of the many, many humans helping to train AI
    submitted by /u/facinabush [link] [comments]  ( 8 min )
    if you stare into the abyss, the abyss stares back at you
    I'm scared of AI but this is one of my favourite quotes and the result is magnificent submitted by /u/peditte [link] [comments]  ( 8 min )
    Anthropic releases Claude 2 with 100K token limit
    submitted by /u/wyem [link] [comments]  ( 8 min )
    AI Can Accurately Predict Potentially Fatal Cardiac Events in Firefighters
    submitted by /u/nist [link] [comments]  ( 8 min )
    Report: China to tighten rules around releasing generative AI tools
    submitted by /u/PleasantLiberation [link] [comments]  ( 8 min )
    AI Robots Admit They'd Run Earth Better Than 'Clouded' Humans
    submitted by /u/ChubbyBrunch [link] [comments]  ( 8 min )
    One-Minute Daily AI News 7/10/2023
    Just like other large chip designers, AMD has already started to use AI for designing chips. In fact, Lisa Su, chief executive of AMD, believes that eventually, AI-enabled tools will dominate chip design as the complexity of modern processors is increasing exponentially.[1] Comedian Sarah Silverman and two authors are suing Meta and ChatGPT-maker OpenAI, alleging the companies’ AI language models were trained on copyrighted materials from their books without their knowledge or consent.[2] Several hospitals, including the Mayo Clinic, have begun test-driving Google’s Med-PaLM 2, an AI chatbot that is widely expected to shake up the healthcare industry. Med-PaLM 2 is an updated model of PaLM2, which the tech giant announced at Google I/O earlier this year. PaLM 2 is the language model underpinning Google’s AI tool, Bard.[3] Japanese police will begin testing security cameras equipped with AI-based technology to protect high-profile public figures, Nikkei has learned, as the country mourns the anniversary of the fatal shooting of former Prime Minister Shinzo Abe on Saturday. The technology could lead to the detection of suspicious activity, supplementing existing security measures.[4] Sources: [1] https://www.tomshardware.com/news/lisa-su-ai-will-dominate-chip-design [2] https://www.cnn.com/2023/07/10/tech/sarah-silverman-openai-meta-lawsuit/index.html [3] https://www.foxbusiness.com/technology/hospitals-begin-test-driving-googles-medical-ai-chatbot-report [4] https://asia.nikkei.com/Politics/Japan-police-to-test-AI-equipped-cameras-in-protecting-VIPs submitted by /u/Excellent-Target-847 [link] [comments]  ( 9 min )
    would disruptive denial of service AI be a viable option in internet malpractice since its progressed so much?
    title submitted by /u/Icy_Independence_125 [link] [comments]  ( 8 min )
  • Open

    [D] Regression for Everyone
    Check my article on Regression. I'd love to hear your thoughts ☺️ https://medium.com/@sandundayananda/regression-fef89ad7c68f submitted by /u/sandun-dayananda [link] [comments]  ( 8 min )
    [D] Masters of AI/Machine Learning
    I have a Bachelor's in Electrical Engineering, valedictorian, 4.00 out of 4.00 CGPA. IELTS 7.5 and have been working in the AI center in a research company for 3 months now. I have 2 papers published, one of them is Falcon B40. My employer asked me to pursue a master's degree in AI or machine learning. The tuition fees don't matter (Fully sponsored) The degree has to be certified as (Msc of Artificial intelligence) OR (Msc of Machine Learning) Not (Msc of Computer science) . University should be QS World Rank 100 to 200 I applied to: 1- Imperial College London 2- Monash Univerisity 3- Univerity Technology Sydney I need a fourth option? I prefer a university that provides a rigid background of computer science since I have an electrical engineering bachelors. Also, I must start maximum by spring 2024.. Thank you ❤️ submitted by /u/Maithah_x [link] [comments]  ( 9 min )
    [P]Benchmarking NVIDIA RAPIDS vs. Pandas: Join our Experiment with Thousands of GPUs!
    We are excited to announce a benchmarking experiment comparing the performance of NVIDIA RAPIDS and Pandas, two powerful data manipulation libraries, on a large Kubernetes-based cluster. Our cluster consists of thousands of A4000 GPUs, providing an excellent opportunity to evaluate these libraries for various workloads that involve data scrubbing. If you're interested in participating, we are offering the necessary compute resources, and you can even define your own datasets! What is NVIDIA RAPIDS? NVIDIA RAPIDS is a suite of open-source software libraries and APIs that accelerate data science and analytics workflows on GPUs. It aims to provide GPU-accelerated alternatives to popular data science tools, including Pandas. RAPIDS leverages the power of GPUs to speed up data processing, maki…  ( 9 min )
    [R] Semantic-SAM: Segment and Recognize Anything at Any Granularity
    We introduce Semantic-SAM, a universal image segmentation model to enable segment and recognize anything at any desired granularity. 🔥code & demo link: https://github.com/UX-Decoder/Semantic-SAM 🔥paper link: https://arxiv.org/pdf/2307.04767.pdf 🔥Our model offers the following attributes from instance to part level: Granularity Abundance. Our model can produce all possible segmentation granularities for a user click with high quality, which enables more **controllable** and **user-friendly** interactive segmentation. Semantic Awareness. We jointly train SA-1B with semantically labeled datasets to learn the semantics in both object-level and part-level. High Quality. We base on the DETR-based model to implement both generic and interactive segmentation, and validate that SA-1B hel…  ( 9 min )
    [D] Confusion over AMD GPU Ai benchmarking
    I'm using an ai benchmarker from this website, https://ai-benchmark.com/ranking_deeplearning.html. It was the only ai benchmarker I could find that I could understand. For some reason the results are in AMD's favor. I always thought ai wasn't good enough on AMD, but it's beating out the 4090! Am I using this benchmark wrong or has AMD really gotten good recently? Also if anyone knows another benchmark I could use, that would be wonderful. ​ ​ https://preview.redd.it/c779j1pb4ebb1.png?width=1565&format=png&auto=webp&s=d62f003587c73443cc83acf98f14a69c4ee14d2c submitted by /u/SociallyApparent [link] [comments]  ( 8 min )
    [P] Reward Prioritization
    Hi all! I will preface by saying I am very new to machine learning. I understand the fundamentals but have little practice in implementation. I am trying to create an AI to play a game that I have re-created in Python by using TensorFlow/Keras. The game is similar to Tetris, and the goal is for the user to survive for as long as possible and the user can score more points by making more complex moves. In my current situation, the game has been entirely coded and converted to a useable format for the AI (I used OpenAI’s Gymnasium (the open-source fork)). Over the last two days I have created the Neural Net model (5 Dense layers with 6 output options) and have been tweaking variables with little noticeable difference in the AI’s abilities. While I certainly need help in several areas and am open to any and all recommendations, I currently feel that my issues lie in the rewards. Currently the AI plays 5000 rounds, then keeps the best 10% of all games based on the reward score. Currently the longer it survives, the higher the reward. However, I also want the model to prioritize score similarly to survival time. To compare this to Tetris, the player could survive for 1 hour only scoring 1-liners, or 5 minutes scoring 90% Tetris’s and end up with a higher ‘score’ than the one that survives longer. I am looking for a compromise between these two extremes that will allow me to select the best 10% of all the games played. I know that I may not have explained this problem very well, but I am open to all suggestions, and all comments are appreciated. Thanks! submitted by /u/TCPisJustFancyUDP [link] [comments]  ( 9 min )
    "[D]" Problems with Lazy Config detectron2 (MViTv2)
    Hey. So, I want to use a config file from a detectron2 project which is undere MViTv2 in my custom dataset which is "my_dataset_train" (datacatalog format) I want to use this config file https://github.com/facebookresearch/detectron2/blob/main/projects/MViTv2/configs/mask_rcnn_mvitv2_t_3x.py like the beneath typical way I use a yaml config file. But giving so many errors one after another that, I even failed to count at this point. ​ I have to use this config file with the dataloader which is in https://github.com/facebookresearch/detectron2/blob/main/projects/MViTv2/configs/common/coco_loader.py. I figured that i can use cfg.dataloader.train.dataset.names = "my_dataset_train" for this. ​ cfg = LazyConfig.load("detectron2/projects/MViTv2/configs/mask_rcnn_mvitv2_t_3x.py") cfg.dataloader.train.dataset.names = "my_dataset_train" train = model_zoo.get_config("common/train.py").train cfg.train.amp.enabled = True cfg.train.ddp.fp16_compression = True cfg.lr_multiplier = L(WarmupParamScheduler)( scheduler=L(MultiStepParamScheduler)( values=[1.0, 0.1, 0.01], milestones=[52500, 62500, 67500], ), warmup_length=250 / train.max_iter, warmup_factor=0.001, ) # cfg.train.init_checkpoint = "detectron2://ImageNetPretrained/mvitv2/MViTv2_T_in1k.pyth" ​ cfg.dataloader.train.total_batch_size = 1 cfg.train.max_iter = 200 cfg.optimizer = model_zoo.get_config("common/optim.py").AdamW cfg.optimizer.lr = 1.6e-4 dataset = instantiate(cfg.dataloader.train) # optimizer = instantiate(cfg.optimizer) # optimizer = model_zoo.get_config("common/optim.py").A ​ # trainer = AMPTrainer(model, dataset, optimizer) trainer = DefaultTrainer(cfg) trainer.train() # FileLink(str(OUTPUT_MODEL)) ​ But typical trainer.train(start_iter,cfg.max_iter) is not working here? How can run this without shell command? Thanks everyone submitted by /u/Ok-Reflection-4049 [link] [comments]  ( 9 min )
    [D] Keras 3.0 Announcement: Keras for TensorFlow, JAX, and PyTorch
    Keras just announced a preview version of Keras 3.0. It's a full rewrite of the Keras codebase that rebases it on top of a modular backend architecture. It makes it possible to run Keras workflows on top of arbitrary frameworks — starting with TensorFlow, JAX, and PyTorch. https://keras.io/keras_core/announcement/ submitted by /u/codemaker1 [link] [comments]  ( 8 min )
    Backpropaagtion with Cross-Entropy and Softmax, HOW? [D]
    Let Zs be the input of the output layer (for example, Z1 is the input of the first neuron in the output layer), Os be the output of the output layer (which are actually the results of applying the softmax activation function to Zs, for example, O1 = softmax(Z1)), and Ys be the target values (which are 0 or 1 because in this example we are dealing with classification problems and using one-hot encoding). E is the sum of the neuron's loss using the CrossEntropy loss function. Let's say our neural network has 2 neurons, and Y1 = 1 (so Y2 = 0). What is the derivative of E with respect to Z1 and the derivative of E with respect to Z2? After calculations, I came to the conclusion that the value of all derivative of E with respects to Zs(Z1 and Z2) should be equal, becasue they are all equal to O1-1 ( since Y1 = 1 as i said), so am i right or wrong?(and why) submitted by /u/qaz_zaqi [link] [comments]  ( 9 min )
    Generating a step by step painting [D]
    I am thinking about this from a long time. Is there anyway that we can generate each step of painting from an image input. If I give an image as input, it should be able to give me step by step instructions on how to paint. I like painting but I got bored of You submitted by /u/dosa-palli-chutney [link] [comments]  ( 8 min )
    [D] Compared to paying a monthly subscription to ChatGPT, or per token for long-form content on the OpenAI platform, Is Poe.com's annual payment worth the money?
    Pretty much as the title says. I'm wondering whether it would be cheaper paying for PdfGPT, Claude2 and GPT-4 access separately, or Poe.com as a bundle? submitted by /u/RTSBasebuilder [link] [comments]  ( 8 min )
    [R] Why was Tacotron trained on <1000h of data?
    Tacotron TTS models (e.g. Tacotron 2 and Parallel Tacotron 2) were trained on 25h and 405h of speech data, respectively. By comparison, more recent TTS systems are trained on >50,000h of speech data. Why were Tacotron models trained on such a relatively small volume of data? Was it simply a matter of compute resource, or is there something in the Tacotron architecture/training set-up that explains the relatively small training size? submitted by /u/Upstairs_Buy_6243 [link] [comments]  ( 8 min )
    [Discussion] AWS Sagemaker Issue
    Hi everyone, I have data that included these columns: customer_id,product_id,product_title,product_category,star_rating Then I used these functions, codes with descriptions below, and AWS Sagemaker config for creating model training I tried to write a function for predicting 5 best products for 1 customer using this model using the deployed endpoint. https://preview.redd.it/i3b2pm099cbb1.jpg?width=966&format=pjpg&auto=webp&s=1aa12ca864e500c1d0bc0e823e8c9e88c0a36316 https://preview.redd.it/nolhkl299cbb1.jpg?width=1002&format=pjpg&auto=webp&s=8d49a232620361076d802fd20ee124fa8a7c2feb https://preview.redd.it/ge5ggs299cbb1.jpg?width=895&format=pjpg&auto=webp&s=c58b80ff6a2af85546563d0a257da4cc3540da9f https://preview.redd.it/slbqok299cbb1.jpg?width=1028&format=pjpg&auto=webp&s=5ee8c5733dde42f1eb7d113c5eb0e0a6d3ba739c https://preview.redd.it/zu3nun099cbb1.jpg?width=1018&format=pjpg&auto=webp&s=86a29a222ab05ef4d4ed5c4eaf6fc321e1cfe194 https://preview.redd.it/bk9qif299cbb1.jpg?width=826&format=pjpg&auto=webp&s=80169c891de0ff3dd2e98fb5be3aa9eaa29b35c6 https://preview.redd.it/n1yigg299cbb1.jpg?width=898&format=pjpg&auto=webp&s=5a3f09983f9cddda1e0772ce473271a84790055d Nevertheless, I got a strange issue and cannot resolve it( SSLError: SSL validation failed for https:// runtime.sagemaker.us-east-1.amazonaws.com/endpoints/ factorization-machines-2023-07-10-12-13-54-256/invocations EOF occurred in violation of protocol (_ssl.c:2396)). Could someone please help me explain this, give me a solution, or help me solve it? submitted by /u/Hung_98 [link] [comments]  ( 8 min )
    [D] Seeking Comprehensive Project Tutorial for Industry Popular ML Tech Stack
    Hi all, I have a MS that focused in AI/ML, and took a role for the last several years that was supposed to focus on ML but never did. I'm now looking for jobs in this area and want to be able to list understanding/proficiency in industry popular technologies on my resume. Specifically, I'm looking to incorporate the following: Data Preprocessing: Numpy, pandas, SQL for data extraction Machine Learning Frameworks: TensorFlow, PyTorch (perform GPU training), Keras, XGBoost, LightGBM Model Deployment and Scalability: Docker and Kubernetes Big Data Processing: Apache Spark, Hadoop Version Control: Git Cloud Services: AWS, Microsoft Azure, or Google Cloud Platform System: Unix-Based I know these individual technologies have their own tutorials and documentation online. Does anyone have a more comprehensive project/tutorial that integrates these technologies? I'd like to create my own project that I can put on my resume to demonstrate proficiency, but I'm feeling a bit overwhelmed learning the entire stack and how to integrate it. Thanks! submitted by /u/ruseriousrightnow [link] [comments]  ( 9 min )
    [D] Scaling Neuroscience Research Using Federated Learning
    https://ieeexplore.ieee.org/document/9433925 Research on Federated Learning and its applications moved a step forward! https://github.com/NevronAI/metisfl ​ ​ submitted by /u/No-Literature-1930 [link] [comments]  ( 8 min )
    [D] Weird loss behaviour with difusion models.
    Has anyone had this happen when training a diffusion model? : The loss decreases to a very low value (close to 0) quite early (around half the first epoch), and keeps oscillating there. Image quality is improving throughout training but the loss isn't really decreasing, just fluctuating around the same values. I had this happen when training pixel-space diffusion models (with latent diffusion the loss seems to decrease gradually), and when fine-tuning Stable Diffusion with textual inversion (loss isn't really decreasing whereas image quality is increasing). submitted by /u/theotherfellah [link] [comments]  ( 8 min )
    [R] Large Language Models as General Pattern Machines. In context, LLMs are capable of completing a wide variety of complex non linguistic patterns.
    Blog/Paper - https://general-pattern-machines.github.io/ submitted by /u/MysteryInc152 [link] [comments]  ( 8 min )
    [P] [R] Machine Unlearning Summary
    Hey, fellow data enthusiasts! We made a Kaggle notebook that delves into an intriguing concept called "Machine Unlearning: The Right to Be Forgotten." If you're interested in exploring the uncharted territory of reversing machine learning models and allowing data to be forgotten, this notebook is an absolute must-read! 📚 Notebook: https://www.kaggle.com/code/tamlhp/machine-unlearning-the-right-to-be-forgotten/ Overview: We all know how machine learning algorithms have transformed our lives by uncovering patterns and making predictions. However, what if we want to reverse this process and erase the knowledge acquired by these models? That's where machine unlearning comes into play, and this notebook provides an in-depth exploration of this cutting-edge concept. Key Highlights: Unde…  ( 9 min )
    [D] - Representation Learning MSc course: Videos + PyTorch exercises
    [🎥📚 Exciting NEWS, everyone! 🌟 ] - I'm ultra thrilled to announce that the first ever online #RepresentationLearniing MSc course is now publicly available. Ready to learn how to learn informative representations from images, proteins, and natural language? 🎓📹 1️⃣ Visit https://www.youtube.com/playlist?list=PL3mKiGE4zNJJ83K4c3IBka6eYfe6v71dS to access the full playlist of recorded lectures. 📖💡 2️⃣ Feel free to tag anyone who might be interested. 💬🤝 3️⃣ Spread the word and help us out! 🌍🗣️ 4️⃣ Github link with all the material: https://github.com/HHU-MMBS/RepresentationLearning_SS2023 It's our (together with Felix Michels, and Tim Kaiser) first attempt, thus be prepared for some mistakes along the way. Let us know how you like it! Have a great day, N.A. submitted by /u/black0017 [link] [comments]  ( 8 min )
    [P]Using lime for this ??
    hello peeps, so i implemented this - https://github.com/karndeepsingh/Extract_key_information_Document_understanding/blob/main/Fine_tuning_LayoutLMForTokenClassification_on_FUNSD.ipynb so the model predicticts the tokens and the bounding boxes (whether its a question or answer or other), i am trying to use lime here to validate the results but idk which lime explainer to use the text or the image ? this notebook uses OCR to get the words in the document. i dont get how to use lime here like what to look for ? submitted by /u/Affectionate_Win2460 [link] [comments]  ( 8 min )
    [R] A Survey of Machine Unlearning
    submitted by /u/KingsmanVince [link] [comments]  ( 8 min )
    [D] [R] Use cases for Generative AI in Robotics
    Hi everyone, Are there any use cases for Generative AI in Robotics apart from Autonomous Decision making? Am looking for other use cases within the Gen AI for our Cobots. I couldn't find any other use cases. Could you guys share your ideas and use cases? submitted by /u/Meeloveall [link] [comments]  ( 8 min )
  • Open

    DSC Weekly 11 July 2023
    Announcements Top Stories In-Depth The post DSC Weekly 11 July 2023 appeared first on Data Science Central.  ( 20 min )
  • Open

    An open-source gymnasium for machine learning assisted computer architecture design
    Posted by Amir Yazdanbakhsh, Research Scientist, and Vijay Janapa Reddi, Visiting Researcher, Google Research Computer Architecture research has a long history of developing simulators and tools to evaluate and shape the design of computer systems. For example, the SimpleScalar simulator was introduced in the late 1990s and allowed researchers to explore various microarchitectural ideas. Computer architecture simulators and tools, such as gem5, DRAMSys, and many more have played a significant role in advancing computer architecture research. Since then, these shared resources and infrastructure have benefited industry and academia and have enabled researchers to systematically build on each other's work, leading to significant advances in the field. Nonetheless, computer architectu…  ( 93 min )
  • Open

    Access private repos using the @remote decorator for Amazon SageMaker training workloads
    As more and more customers are looking to put machine learning (ML) workloads in production, there is a large push in organizations to shorten the development lifecycle of ML code. Many organizations prefer writing their ML code in a production-ready style in the form of Python methods and classes as opposed to an exploratory style […]  ( 8 min )
  • Open

    Russian transliteration hack
    I mentioned in the previous post that I had been poking around in HTML entities and noticed symbols for Fourier transforms and such. I also noticed HTML entities for Cyrillic letters. These entities have the form & + transliteration + cy;. For example, the Cyrillic letter П is based on the Greek letter Π and […] Russian transliteration hack first appeared on John D. Cook.  ( 5 min )
    Symbols for transforms
    I was looking through HTML entities and ran across ℱ. I searched for all entities ending in trf; and also found ℳ, ℒ, and ℨ. Apparently “trf” stands “transform” and these symbols are intended to be used to represent the Fourier transform, Mellin transform, Laplace transform, and z-transform. You would not know from the Unicode […] Symbols for transforms first appeared on John D. Cook.  ( 5 min )
  • Open

    Microsoft at ICALP 2023: Deploying cloud capacity robustly against power failures
    Efficiency is vital in the face of escalating demand for cloud resources. And efficient power management strategies address the bottleneck of power availability in datacenters. Learn how we optimize power allocation to support sustainable resource usage. The post Microsoft at ICALP 2023: Deploying cloud capacity robustly against power failures appeared first on Microsoft Research.  ( 11 min )
  • Open

    Sierra Division Studios Presents Three Epic Projects Built With NVIDIA Omniverse
    Jacob Norris is a 3D artist and the president, co-founder and creative director of Sierra Division Studios — an outsource studio specializing in digital 3D content creation.  ( 9 min )
  • Open

    GPT-4's details are leaked
    submitted by /u/nickb [link] [comments]  ( 8 min )
    Intel reports high-rendering graphics with low-power GPUs
    submitted by /u/keghn [link] [comments]  ( 8 min )

  • Open

    Afro Pink
    submitted by /u/Akumetsu_971 [link] [comments]  ( 8 min )
    How could zero point energy help ai go to the next level ?
    I am curious about this mainly because many people are talking about uap ! The technology that powers these uap most likely are not oil or gas or batteries more likely zero point energy . My main question like i said how could it make ai even better to heights to where its never been before ! submitted by /u/jdgementdragonotk [link] [comments]  ( 8 min )
    What are some GitHub security best practices?
    It seems like about 90% of the stuff happening in AI is only accessible via GitHub. I'm probably just being overly cautious, but downloading something from such a public place is just not something I am currently comfortable with. What are your thought on this? Are there precautions you take that I should be aware of before venturing into this territory? Or is it just generally considered pretty safe, and nothing to worry about much? submitted by /u/gcubed [link] [comments]  ( 8 min )
    Offline music AI generator
    Hi all, i'm searching AI for music for my own game, so I want to find AI without copyright. But every model that i found were online with copyright rules. Is there any offline, better - opensource AI model? submitted by /u/krakotay1 [link] [comments]  ( 8 min )
    An open Source AI on a european server, readings PDFe (Texts), answering questions in different languages - pls help
    Hey All - i am looking for an open-source chatbot. He will get a bench of PDF documents (training-Data). this data should be searchable and the bot should be able to answer questions in different languages. so far, so ez: we´re in germany - which means we do have hard regulations for servers, which are NOT hosted in the EU (god i really hate to write that). so we need a model, which can be installed here, train it hre and finally it should his work here. If you have any advice - i would be really thankful! submitted by /u/myreddit333 [link] [comments]  ( 8 min )
    How is it possible that there were no LLM AIs, then there was ChatGPT, now there are dozens of similar products?
    Like, didn’t ChatGPT need a whole company in stealth mode for years, with hundreds of millions of investment? How is it that they release their product and then overnight there are competitors – and not just from the massive tech companies? submitted by /u/Aquillyne [link] [comments]  ( 8 min )
    The Rockstars of the AI World
    AGI: Brainiac robots will be like the ultimate multitaskers on steroids. They will possess mind-blowing cognitive abilities, making them the geniuses who can solve complex math problems in their sleep and then casually drop fashion advice that would make Coco Chanel blush. You might catch them pondering the mysteries of the universe or composing symphonies that give Beethoven a run for his money. Are they secretly working as undercover geniuses on the weekends? All we know is that AGI will be among us to challenge our notion of human intelligence and make us wonder if we should start polishing our robot-dance moves to keep up and maintain them entertained. submitted by /u/Powerful-Pumpkin-938 [link] [comments]  ( 8 min )
    Using google account data for AI personalization
    Brand new to AI and trying to understand possible functionalities, so bear with me if i sound uneducated. i know that you can download all of your google account data, (which i think is probably the largest amount of personalized information that most of us have easy access to. includes youtube history, google pay transactions, browsing history, tons of other info.) but my question is; after downloading this data, how could you implement this into some localized ai program to improve your life? it knows what restaurants you frequent, items you buy, travel patterns, vacations, email history, ect i would love to hear peoples ideas on this, thanks. submitted by /u/G-Boogie42 [link] [comments]  ( 8 min )
    Any methods for achieving close to elevenlabs quality and inference speed with a local model?
    My upcoming master thesis (on use of AI models for believable NPCs in video games) will involve the use of text to speech. Currently elevenlabs has been my go to for TTS, but the pricing model is quite inconvenient since its a monthly subscription instead of pay for use. I'm sure some of you here are knowledgeable with TTS, it would be great if anyone could point me in a good direction. I want use use TTS to generate natural human sounding conversational voices. Ideally I could finetune the model on different voice profiles to get a wide range of voices. I have only started to look I to options outside elevenlabs, but I figured I should ask here before I start diving deep, so I can avoid unnecessary waste of time! If this is not achievable locally, it would be great to know if there are any methods for hosting a model on a cloud compute platform, so I can at least pay for use, instead of deal with the monthly subscription that comes with eleven labs. Thank you! submitted by /u/timidavid350 [link] [comments]  ( 9 min )
    ai generated video compilations
    was wondering if there's an AI video tool where you put in keywords and it creates a compilation of gifs/clips based on your text. for example, if you typed in "dog", "car" and "clouds" it would create a video compiled of short clips of dogs, cars and clouds. sorry if this is a dumb question i was just curious if this exists haha thanks! submitted by /u/bonlom [link] [comments]  ( 8 min )
  • Open

    How would one normalize observations in off-policy online reinforcement learning?
    Can someone please help with this question . Please let me know if you'd like me to paste the whole question in the description here. Thank you! submitted by /u/Academic-Rent7800 [link] [comments]  ( 8 min )
    Tuning Rewards/Adding More Rewards in RL is a headache
    I am working on RL and it is really exciting. But in a lot of cases I don't get the desired behaviours i want. So i either tune the weights of existing rewards or adding more reward terms trying to fix that, but later I realized that adding more rewards just make the problem more complex and I even get worse performance than before. I also tried hyperparameter tuning, but they are really slow and I don't see too much difference compared with my heuristic choice. I read some RL papers and I never figure out where these magic weights come from. Anyone can provide some advice will be very helpful :) submitted by /u/Alchemist1990 [link] [comments]  ( 8 min )
    Causes of RL agent reaching a more optimal policy but not continuing to improve
    Hey, I'm developing RL agent for optimal setpoint control but I'm experiencing some weird behaviour where the agent would reach a policy where it will perform better than all other previous policies, but doesn't continue improving and goes back to get stuck at a local minimum. I'm assuming this behaviour comes from wrong hyperparameter tuning, but I was wondering if there could be different sources for this behaviour. ​ Training (lower score is more optimal) This is the picture from training. I'm training with PPO and had non-zero value for entropy-coefficient. Edit: When I stop training at the dips, and test the agent, it will quite consistently score better. submitted by /u/LeSUTHU [link] [comments]  ( 8 min )
    "Solving math word problems with process- and outcome-based feedback", Uesato et al 2022 {DM}
    submitted by /u/gwern [link] [comments]  ( 8 min )
    Extensions for SAC
    I am a starter in Reinforcement learning and stumbeled across SAC. While all other off-policy algorithm seem to have extensions (DQN,DDQN/DDPG,TD3) I am wondering what are extensions for SAC that are worth having a look at? I already found 2 papers (DR3 and TQC) but im not experienced enough to evaluate them. So i thought about building them and comparing them to others. Would be nice to hear someones opinion:) submitted by /u/MChiefMC [link] [comments]  ( 8 min )
    PPO agent for "2048": help requested
    Hi folks, I have started to dip my toes in reinforcement learning recently, reproducing some basic algorithms (DQN, REINFORCE, DDPG) with the easy gymnasium examples with success. I have now started with a new project with a custom environment: 2048. It seems to be an interesting target for a deep reinforcement learning agent: the action space is small (at most four directions) but with a large observation space to generalize over using (a) neural network(s). Here's where the problem starts: after implementing a custom environment that follows the typical gymnasium interface, and use a slightly adjusted PPO implementation from CleanRL, I cannot get the agent to learn anything at all, even though this specific implementation seems to work just fine on basic gymnasium examples. I am hoping…  ( 10 min )
    How to work with a large action space?
    I want to train a single agent for a game using reinforcement learning. I am totally new in this field. In the game, 4 characters can be moved around on a 10x10 board. After the character is moved to a new position, it can build something on one of the grid cells in a certain range from its new position. Each character has a different range of tiles, where they can move and where they can build a house. So, in general, the action space is 4x100x100 = 40000 including all the illegal moves, which is huge. Can you advise any way to shrink the action space? I am using Gymnasium to define the environment. Previously, I thought about using MultiDiscrete or Tuple spaces for action space, e.g. one dimension for characters, one dimension for 100 tiles to move, and one dimension for 100 tiles to build the house. However, how can I then mask the illegal moves? submitted by /u/naeson [link] [comments]  ( 9 min )
  • Open

    [D] NVIDIA Rapid Vs Pandas
    Hello ML Team, I am reaching out on behalf of Data Care LLC. We developed a large Kubernetes-based cluster with thousands of A4000 GPUs and are currently conducting a benchmarking experiment to compare NVIDIA RAPIDS against Pandas for various size workloads that needs a bit of scrubbing. Here are the details: Infra1: Pandas with 6 CPU cores, 16 GB RAM Infra2: A4000 with 16 GB VRAM, 16 GM RAM and 6 CPU Cores. Synthetic dataset of .8 GB, 1.6Gb, 3.2 GB, 6.4 GB sizes with errors with data that need to be scrubbed. You are welcome to define your own dataset. If any of you are interested in participating in this experiment, we would be happy to provide you with the necessary compute resources. To sign up for this experiment, please visit www.thedatacare.com/experiment-rapid We greatly appreciate your participation and encourage you to publish your results to contribute to the community. Thank you, Arun Data Care LLC submitted by /u/arunpalepu1981 [link] [comments]  ( 9 min )
    [DISCUSSION] How much can we trust OpenAI (and other large AI companies) will keep our fine tuned models and data sets private?
    tldr: Do you trust OpenAI or other large AI companies with your data? Do you reckon it's just a matter of time before they find all of the data, so might as well contribute to their research project and benefit from it while you can? Or do you prefer to go the open sourced route instead for this reason? Here's is my concern: Some of my team members are very high on Open AI's models, their ease of use, and how smart they are out the box. Now, Open AI (relatively recently) published a statement saying that they will not use your fine tuned models or data sets internally to improve their products, but given their history and the value of these fine tuned models and their corresponding datasets, I'm uncertain to what extent we are able to trust that they will keep our data private. I like t…  ( 9 min )
    Just was Offered a $130k Machine Learning Engineer Position - Is this Salary too Low? [D]
    I am about to graduate with my master's and I was offered a Machine Learning Engineer role with a salary of $130k. I have data science experience, but not much programming experience. I pushed back on the salary and said I was expecting a little more (I was paid more as a Data Scientist), and the recruiter said the team was taking a chance on me and I need to prove myself before they would raise my salary. I also want to mention I am switching into work that is more coding heavy than what I was doing as a Data Scientist. The job is exactly what I want to do though and the team was really great to talk to. The company has great reviews online and wonderful benefits. I am leaning towards just taking the job but this is my first job interview and I don't want to sell myself short. FYI I live in California, not the Bay Area. submitted by /u/datatastic08200 [link] [comments]  ( 9 min )
    [D] What have been your use cases for LLM autonomous agents?
    I've been using GPT for completions on a daily basis for a while now - code completion and search-like chatting, basically. I've recently been playing around with both ChatGPT plugins and LangChain for autonomous-agent-like behavior, and although the idea of the LLM interacting with the environment through API calls or code interpretation seems promising, in practice I haven't found such a useful and usable case for it like completions yet. LangChain's OpenAPI toolkit with its planner/controller agent duo seems to get lost 90% of the time, making it unusable. This happens even with an /api endpoint telling it exactly how to interact with the API and prompt templates suggesting that this endpoint be used to get the API specs. Maybe I'm just not getting it right... As for ChatGPT plugins, other than web search for more updated results I haven't really found a use case where I could not do the same thing with completions. Code Interpreter shaves off a few seconds vs completions and running whatever script it produces locally, but it's not very useful in face of compliance or privacy requirements of not uploading stuff into OpenAI. For example I wanted to speed up a work related video and add a separate audio track to it. I couldn't upload the video to OpenAI as it contained internal work stuff, so I just used completions for an ffmpeg script to do the job and ran it locally. Same thing with transforming or plotting CSV data - can't really update customer data to OpenAI, so just get the script and run it locally. Anyway, I can think of a lot of cool use cases for autonomous agents and the like, but I haven't been able to actually use it in my daily routine, unlike text completion. Have you been using autonomous agents successfully and regularly? submitted by /u/osantacruz [link] [comments]  ( 9 min )
    [D] When will the LLM winter come?
    Everyone is jumping on the bandwagon of LLMs, and it seems like we should follow suit. But why? LLMs are impressive; they can play chess, emulate a Linux terminal, perform mathematical calculations, convert code, and more. However, in most cases, they perform these tasks poorly, which means they cannot be used as is, at least not for now. While we come across numerous cool demos, the results are often cherry-picked and not applicable to real business use cases. I can only identify a few limited use cases where LLMs truly shine: QA, summarization, and acting as a co-pilot for tasks such as coding, writing, and education etc. In essence, they excel in seq2seq tasks. There are some notable drawbacks that should be acknowledged: Cost: The cost of using LLMs will never be cheaper than specialized solutions. Latency: Building real-time applications with LLMs is extremely challenging. Quality and Accuracy: For now, LLMs lack strong reasoning capabilities. Reliability: Hallucinations are still an issue. Occasionally, the model fails to follow instructions. Quotas: Currently, the quotas imposed on LLMs are too low for large-scale production applications. It is possible that most of these problems will be resolved in the future. However, the question remains: how long will it take? Will it be 1 year, 5 years, or even 10 years? Unfortunately, we need to see a ROI this year. Anyway, just want to remind you that there is no free lunch! submitted by /u/___mlm___ [link] [comments]  ( 9 min )
    [D] Regression Problem
    I’m trying to develop a ML model to be able to predict “time series” data. It’s not actually time series, it’s temperature series. But regardless, the order matters. I have 20 sets of tabular data in separate csv’s that each contain 3 columns. Column 1 is temperature from 800 to 1360, column 2 are values I’d like to be able to predict, and column 3 contains values that I’d like to use as input to predict column 2 values at each respective temperature. Because the temperature range is between 800 and 1360, each csv has 560 rows of data (1360 - 800 = 560). So in total, there’s 11,200 rows of data (20 sets * 560 = 11,200 total rows). The data in each column, excluding the temperature column, are second order polynomials. So I’m essentially trying to get from one polynomial (column 3) to another (column 2). What would be the best ML method to achieve this given my problem? I know there’s an underlying pattern but I need ML to help me determine it. Appreciate any help here, thanks!! submitted by /u/cgifted7 [link] [comments]  ( 9 min )
    Seeking Participants for AI-related Survey[R]
    I am currently working on my IB Extended Essay, and I would greatly appreciate your help in gathering valuable insights from individuals knowledgeable in the field of AI. The purpose of my survey is to understand the perspectives of AI enthusiasts and professionals like you. If you have a few minutes to spare, I kindly request you to participate in my survey. Your input will contribute significantly to my research and help me gain a deeper understanding of the topic. The survey covers various aspects of AI, and your expertise will be invaluable in shaping the results. Survey Link: https://forms.gle/PVGrRbPLTpZRbbpL9 Rest assured that all responses will be kept confidential and only used for academic purposes. Additionally, feel free to share this survey with others who might be interested or knowledgeable in the field. Thank you in advance for your time and contributions! Your participation will greatly aid in the successful completion of my IB Extended Essay. submitted by /u/KVNG_Winston [link] [comments]  ( 9 min )
    [R] All about evaluating Large language models
    I explored my curiosity on how to best evaluate LLMs and LLM application and consolidated my thoughts in this article https://explodinggradients.com/all-about-evaluating-large-language-models https://preview.redd.it/btfz3loxb6bb1.png?width=1920&format=png&auto=webp&s=94d1789c0e0f8c1dac50864996d908497a45a241 submitted by /u/iamikka [link] [comments]  ( 8 min )
    [D] Hacking LangChain for Fun and Profit
    https://blog.kevinhu.me/2023/07/10/hacking-langchain-for-fun-and-profit/ I'm starting a series of blogs to delve into LangChain. Hope this helps anyone who's interested in LLM and building with LangChain. submitted by /u/OLDGUN [link] [comments]  ( 8 min )
    [D] Forcing diversification in similarity search
    I’m using a vector database for storing image embeddings and using it for similarity search. If I pick top ten most similar vectors I can sometimes end up inside of an echo chamber with almost “duplicates” or too similar images. I would like to diversify the results so that all the results are close to the input vector but different between themselves. Are there common patterns/algorithms for this type of diversification? The idea that I have: I want to pick 10 images. I would query the database for a 100 of the most similar embeddings and use a clustering algorithm to cluster them into 10 clusters. Finally, I would pick one image from each cluster. submitted by /u/alkibijad [link] [comments]  ( 8 min )
    [D] Website to get historical price for agriculture commodities?
    Hey guys, i want to make price prediction for agriculture commodities such as grain, corn and coffee, I need Historical price data like open, low, high, close, and volume. You guys know which website that provides those data for free??? I tried https://markets.businessinsider.com/ but it was no help submitted by /u/Classic-Fee6889 [link] [comments]  ( 8 min )
    [P] We built a paper sharing platform. Looking for alpha testers!
    https://www.entepond.com We are a group of industry practitioners who got tired with sharing papers via social media platforms. They are not designed for that. It’s hard enough to keep track of the trending/latest papers nowadays. So, we built Pond, a paper sharing platform that makes sense. submitted by /u/dockerun [link] [comments]  ( 8 min )
    [D][R] Help with bachelor thesis
    Hi all, I am finishing my 3rd year UG AI program, and I’ve already been admitted to a MSc in AI. However, I need some ideas for my thesis which should be around 30 pages long. I’ve been doing some research on what I would like to study, but I still havent found anything I like. I am open to both an applied ML topic and a more theoretical one, so feel free to suggest something you think is appropriate 😄 submitted by /u/AskAmbitious5697 [link] [comments]  ( 8 min )
    [D] MPT-30b or Falcon 40b: which one is the better option
    In the open-source community, two models that have gained a lot of popularity is MPT and Falcon. However, I wonder which one is the best LLM. What are your considerations about both models? submitted by /u/paulo_zip [link] [comments]  ( 8 min )
    [Project] Brute force for popular models, Feature Reduction and Scaling Techniques for Classification [Python]
    This is my first article to try something new, it is brute forcing on classifications problems Does anybody have suggestions to improve the code, add more features, or suggest another idea https://baroodz.medium.com/brute-force-for-popular-models-feature-reduction-and-scaling-techniques-for-classification-69be9c0426b9 https://github.com/Barood-cmd/BruteforceML/ All opinions appreciated submitted by /u/Barood_D [link] [comments]  ( 8 min )
    [D] How does RTX 3060 12GB fare against the RTX 4060 Ti 16GB for
    Ok, now that the RTX 4060 Ti 16GB has been launched this question needs to be asked :) RTX 4060 Ti 16GB is faster has more/latest tensor cores but has less memory bandwidth 128bit 288gbps while the RTX 3060 12GB is slower (also cheaper) but has more memory bandwidth 192 bit 360gbps. less memory though. Does anyone has experience with the new card for machine learning purpose? ​ Specs: RTX 4060 Ti 16GB - https://www.techpowerup.com/gpu-specs/geforce-rtx-4060-ti-16-gb.c4155 RTX 3060 12GB - https://www.techpowerup.com/gpu-specs/geforce-rtx-3060-12-gb-ga104.c3832 ​ submitted by /u/PhotonAttack [link] [comments]  ( 8 min )
    [D] - Issues with Model Collapse in VAE for Reconstruction Task
    I am currently experiencing a suspected model collapse in a Variational Autoencoder (VAE) model I am working with. Below are details on the project setup and the issue at hand: Project Goal: Exploring if a the same VAE architecture can independently handle two tasks - reconstruction and regression - using real-world production line data. Data: Dataset: Approximately 80,000 samples Features: Sensor readings such as flow, temperature, force, etc. (both binary and continuous data) Target for regression: The measurement of a product property at the end of the line (can be divided in approximately 50 classes) Preprocessing: Data normalized between 0 and 1 before training Model Architecture: Encoder: Input layer of 800 and a hidden layer of 600 Latent Space: Size of 400 Decoder: …  ( 10 min )
    [P] Image based financial statements ratio analysis like quick ratio, debt to equity ratio?
    I have image balance sheets and using aws ocr we extract text then format and segment them to find ratios like quick ratio, current ratio, debt to equity ratio for analysis using fuzzy string matching. However, sometimes the values for calculation of these ratio are present but with different keywords, values are there but the total is not present. Is there any literature or code base utilizing ML approach to do ratio analysis on financial statements submitted by /u/Priyanshu1_ [link] [comments]  ( 8 min )
    [D] Data Management for Machine Learning Projects
    Hey, r/MachineLearning, it’s Nir from DagsHub 🐶 Data management may seem straightforward, but beneath the surface, it poses complex hurdles that can impact project success. We've been thinking a lot about it lately, particularly within machine learning projects. So we decided to conduct research, talking with companies and individual practitioners about the challenges they face. We wanted to shed light on the often-overlooked challenges that come with this crucial aspect. If you're interested in understanding the intricacies of data management and how it influences ML projects, I invite you to check out our latest blog post where we share our initial results. It's an informative read that aims to foster knowledge-sharing within our community. This is one area we can all learn a lot from each other. Feel free to share your own experiences and insights related to data management challenges and how you overcome them. Here's the link to the blog post: https://dagshub.com/blog/challenges-in-data-management-for-machine-learning/ As always, looking forward to hearing your thoughts! 🤗 submitted by /u/RepresentativeCod613 [link] [comments]  ( 9 min )
    [D] Choosing the right embeddings model
    I'm trying to pick the right model to take short stories and pick similar short stories based on the story. I was looking through https://huggingface.co/spaces/mteb/leaderboard and trying to figure out what model I should use. Should I be looking at the top clustering models? I was going to use chatgpt ada002 but would rather use a better model if possible submitted by /u/juniorskimbrough [link] [comments]  ( 8 min )
    [R] Adversarial Robust Deep Reinforcement Learning Requires Redefining Robustness
    https://ojs.aaai.org/index.php/AAAI/article/view/26009/25781 submitted by /u/ml_dnn [link] [comments]  ( 8 min )
    For deep learning practitioners in industry, is the workflow always this annoying? [D]
    Right now I’m working on a multivariate time series classification problem for a lab I’m working at. The training set is a multivariate time series with data collected over minutely level. Each time series is a type of positional tracking measurement. The goal is to classify each of the time series and see if they can discriminate between 3 different types of outcomes of interest. The labels are 0,1,2. I’m doing this in PyTorch, and I read roughly 8-9 papers. They all seem to point to convolutional neural networks as a starting point. After building the model and setting up the training loops, this whole process feels like maybe 1% model building and 99% debugging, tuning, architecture changing, and tensor shape checking. My day to day has just been stepping through my architecture with …  ( 9 min )
    [D] LLM Multi-Rounds Fine-Tuning
    I have 2 datasets that I want to use for fine-tuning multiple LLMs. Dataset #1 is large and has "raw" text data (many scraped webpages revolving around different topics that an LLM may not have been pre-trained on). Dataset #2 is very small and has selected examples depicting how a topic from Dataset #1 should be presented to the user during chat interactions. I am trying to determine a good strategy for fine-tuning. I could not find any examples of "two-round" fine tuning in a scenario like this. I am thinking about using quantization and training just the last few layers of selected LLM on Dataset #1, and using the LoRA method for Dataset #2. However, I do not have much compute resources. Any tips/suggestions/advice would be greatly appreciated! submitted by /u/ilovejoi36912 [link] [comments]  ( 8 min )
  • Open

    Visualizing a determinant identity
    The previous post discussed an algorithm developed by Charles Dodgson (better known as Lewis Carroll) for computing determinants. The key identity for proving that Dodgson’s algorithm is correct involves the Desnanot-Jacobi identity from 1841. The identity is intimidating in its symbolic form and yet easy to visualize. In algebraic form the identity says Here a […] Visualizing a determinant identity first appeared on John D. Cook.  ( 5 min )
    How Lewis Carroll computed determinants
    Charles Dodgson, better known by his pen name Lewis Carroll, discovered a method of calculating determinants now known variously as the method of contractants, Dodgson condensation, or simply condensation. The method was devised for ease of computation by hand, but it has features that make it a practical method for computation by machine. Overview The […] How Lewis Carroll computed determinants first appeared on John D. Cook.  ( 6 min )
    Gram matrix
    An elegant algebraic identity says If x is the vector [a b] and y is the vector [c d] then this identity can be written where the dot indicates the usual dot product. I posted this on Twitter the other day. Gram matrix Now suppose that x and y are vectors of any length n. […] Gram matrix first appeared on John D. Cook.  ( 5 min )
  • Open

    3 Questions: Honing robot perception and mapping
    Luca Carlone and Jonathan How of MIT LIDS discuss how future robots might perceive and interact with their environment.  ( 8 min )
  • Open

    Design Speed Takes the Lead: Trek Bicycle Competes in Tour de France With Bikes Developed Using NVIDIA GPUs
    NVIDIA RTX is spinning new cycles for designs. Trek Bicycle is using GPUs to bring design concepts to life. The Wisconsin-based company, one of the largest bicycle manufacturers in the world, aims to create bikes with the highest-quality craftsmanship. With its new partner Lidl, an international retailer chain, Trek Bicycle also owns a cycling team, Read article >  ( 7 min )
  • Open

    Renovating computer systems securely and progressively with APRON
    This research paper was accepted by 2023 USENIX Annual Technical Conference (ATC), which is dedicated to advancing the field of systems research. Whether they’re personal computers or cloud instances, it’s crucial to ensure that the computer systems people use every day are reliable and secure. The validity of these systems is critical because if storage […] The post Renovating computer systems securely and progressively with APRON appeared first on Microsoft Research.  ( 10 min )
  • Open

    AIOps above the radar – Using AI to monitor your AI infrastructure
    When an enterprise project is low-profile (“below the radar”), then it is not likely to be the target of bad actors. Similarly, if some part of that project’s infrastructure fails or falters, then the consequences of the problem and/or the urgency of providing a solution are usually manageable. But when a high-profile (“above the radar”)… Read More »AIOps above the radar – Using AI to monitor your AI infrastructure The post AIOps above the radar – Using AI to monitor your AI infrastructure appeared first on Data Science Central.  ( 22 min )
    Security data lakes and the future of organizational security
    Evolving technological advancements have created a far more data-centric world. This has dramatically changed the enterprise landscape, while also creating more data silos. The explosion of cybersecurity tools and mounds of data in modern enterprises have made it difficult to combine data to create a unified view. This has resulted in siloed data that’s also… Read More »Security data lakes and the future of organizational security The post Security data lakes and the future of organizational security appeared first on Data Science Central.  ( 21 min )
    Sentience: AI has demystified human consciousness, intelligence
    There is a recent article, Unraveling the Mystery of Human Consciousness, where it was stated that, “Consciousness makes us capable of experiencing the scent of a rose, the touch of a breeze, the taste of food, the sound of music, and the sight of a sunrise. We also have a unique ability to be aware… Read More »Sentience: AI has demystified human consciousness, intelligence The post Sentience: AI has demystified human consciousness, intelligence appeared first on Data Science Central.  ( 19 min )
    Exploring intelligent search solutions: A comparative analysis of Amazon Kendra integration and large language model crawlers
    Amazon Kendra and LLamaIndex can help with knowledge integration but fall short in connecting diverse knowledge sources, to enable efficient intelligent search. In this article, we compare the existing solutions and explain how to overcome their limitations using a Google Drive crawler. Companies often face difficulties in consolidating their knowledge base when their data is… Read More »Exploring intelligent search solutions: A comparative analysis of Amazon Kendra integration and large language model crawlers The post Exploring intelligent search solutions: A comparative analysis of Amazon Kendra integration and large language model crawlers appeared first on Data Science Central.  ( 27 min )
    What’s missing from ChatGPT and other LLMs?
    Recent developments in artificial intelligence remind me of the automotive industry in the late 19th and early 20th century. In that case, it took the industry several decades to commit to internal combustion engines. And while that picture was still unclear, there were over 250 different car manufacturers, some of whom were producing steam-powered cars.… Read More »What’s missing from ChatGPT and other LLMs? The post What’s missing from ChatGPT and other LLMs? appeared first on Data Science Central.  ( 21 min )
  • Open

    Google at ACL 2023
    Posted by Malaya Jules, Program Manager, Google This week, the 61st annual meeting of the Association for Computational Linguistics (ACL), a premier conference covering a broad spectrum of research areas that are concerned with computational approaches to natural language, is taking place online. As a leader in natural language processing and understanding, and a Diamond Level sponsor of ACL 2023, Google will showcase the latest research in the field with over 50 publications, and active involvement in a variety of workshops and tutorials. bold). Board and Organizing Committee Dan Garrette Workshop chairs include: Annie Louis Publication chairs include: Lei Shu Program Committee includes: Vinodkumar Prabhakaran, Najoung Kim, Markus Freitag Spotlight papers NusaCrowd: O…  ( 93 min )
  • Open

    A lighting-fast and developer-friendly Federated Learning SDK
    Hello Reddit 👋 I am contacting you because I am one of the community relations coordinators of MetisFL, a federated learning framework. https://github.com/NevronAI/metisfl#readme I would really love and appreciate it if you checked out and star our repo and considered using the framework or contributing to it 🤗 The code is the result of several years of research on federated learning and was just released a few weeks ago. So you would be one of the first outside users/contributors! We are currently preparing for the first stable release of our pip package and any community input would be much appreciated! Please do not hesitate to reach out to us anytime, we are always available on our Slack channel to answer questions about MetisFL, solve technical issues or chat about the future of tech and AI! Slack is pretty new as well, you'd be one of the first outside members! Hope to see you in our online community! submitted by /u/No-Literature-1930 [link] [comments]  ( 8 min )
    Transformers
    Is anyone know where can I get more detailed Explanation for better Understanding of RNN,LSTM, Transformers,BERT and Practical Implementation of it too submitted by /u/Potential_Plant_160 [link] [comments]  ( 8 min )
    Fast SAM (Segment Anything Model) review
    ​ https://preview.redd.it/gvfxnbpfx2bb1.jpg?width=871&format=pjpg&auto=webp&s=63778cd329dd8ba56dc77e9b67947b334f4eeb81 You can find it interesting. A new post from the OpenCV.ai team about Fast SAM (Segment Anything Model) Short description: Fast SAM, an innovative approach to the Segment Anything Task, drastically increases SAM model speed by 50 times. It introduces prompts to facilitate a broad range of problem-solving, breaking barriers of traditional supervised learning. This blog post will briefly explain the segment anything task and the Fast SAM approach. More details are here submitted by /u/No-Independence5880 [link] [comments]  ( 8 min )
  • Open

    On the Stepwise Nature of Self-Supervised Learning
    Figure 1: stepwise behavior in self-supervised learning. When training common SSL algorithms, we find that the loss descends in a stepwise fashion (top left) and the learned embeddings iteratively increase in dimensionality (bottom left). Direct visualization of embeddings (right; top three PCA directions shown) confirms that embeddings are initially collapsed to a point, which then expands to a 1D manifold, a 2D manifold, and beyond concurrently with steps in the loss. It is widely believed that deep learning’s stunning success is due in part to its ability to discover and extract useful representations of complex data. Self-supervised learning (SSL) has emerged as a leading framework for learning these representations for images directly from unlabeled data, similar to how LLMs learn re…  ( 5 min )
  • Open

    GEANN: Scalable Graph Augmentations for Multi-Horizon Time Series Forecasting. (arXiv:2307.03595v1 [cs.LG])
    Encoder-decoder deep neural networks have been increasingly studied for multi-horizon time series forecasting, especially in real-world applications. However, to forecast accurately, these sophisticated models typically rely on a large number of time series examples with substantial history. A rapidly growing topic of interest is forecasting time series which lack sufficient historical data -- often referred to as the ``cold start'' problem. In this paper, we introduce a novel yet simple method to address this problem by leveraging graph neural networks (GNNs) as a data augmentation for enhancing the encoder used by such forecasters. These GNN-based features can capture complex inter-series relationships, and their generation process can be optimized end-to-end with the forecasting task. We show that our architecture can use either data-driven or domain knowledge-defined graphs, scaling to incorporate information from multiple very large graphs with millions of nodes. In our target application of demand forecasting for a large e-commerce retailer, we demonstrate on both a small dataset of 100K products and a large dataset with over 2 million products that our method improves overall performance over competitive baseline models. More importantly, we show that it brings substantially more gains to ``cold start'' products such as those newly launched or recently out-of-stock.  ( 2 min )
    ContextLabeler Dataset: physical and virtual sensors data collected from smartphone usage in-the-wild. (arXiv:2307.03586v1 [cs.HC])
    This paper describes a data collection campaign and the resulting dataset derived from smartphone sensors characterizing the daily life activities of 3 volunteers in a period of two weeks. The dataset is released as a collection of CSV files containing more than 45K data samples, where each sample is composed by 1332 features related to a heterogeneous set of physical and virtual sensors, including motion sensors, running applications, devices in proximity, and weather conditions. Moreover, each data sample is associated with a ground truth label that describes the user activity and the situation in which she was involved during the sensing experiment (e.g., working, at restaurant, and doing sport activity). To avoid introducing any bias during the data collection, we performed the sensing experiment in-the-wild, that is, by using the volunteers' devices, and without defining any constraint related to the user's behavior. For this reason, the collected dataset represents a useful source of real data to both define and evaluate a broad set of novel context-aware solutions (both algorithms and protocols) that aim to adapt their behavior according to the changes in the user's situation in a mobile environment.  ( 2 min )
    Equivariant Single View Pose Prediction Via Induced and Restricted Representations. (arXiv:2307.03704v1 [cs.CV])
    Learning about the three-dimensional world from two-dimensional images is a fundamental problem in computer vision. An ideal neural network architecture for such tasks would leverage the fact that objects can be rotated and translated in three dimensions to make predictions about novel images. However, imposing SO(3)-equivariance on two-dimensional inputs is difficult because the group of three-dimensional rotations does not have a natural action on the two-dimensional plane. Specifically, it is possible that an element of SO(3) will rotate an image out of plane. We show that an algorithm that learns a three-dimensional representation of the world from two dimensional images must satisfy certain geometric consistency properties which we formulate as SO(2)-equivariance constraints. We use the induced and restricted representations of SO(2) on SO(3) to construct and classify architectures which satisfy these geometric consistency constraints. We prove that any architecture which respects said consistency constraints can be realized as an instance of our construction. We show that three previously proposed neural architectures for 3D pose prediction are special cases of our construction. We propose a new algorithm that is a learnable generalization of previously considered methods. We test our architecture on three pose predictions task and achieve SOTA results on both the PASCAL3D+ and SYMSOL pose estimation tasks.
    Distilling Universal and Joint Knowledge for Cross-Domain Model Compression on Time Series Data. (arXiv:2307.03347v1 [cs.LG])
    For many real-world time series tasks, the computational complexity of prevalent deep leaning models often hinders the deployment on resource-limited environments (e.g., smartphones). Moreover, due to the inevitable domain shift between model training (source) and deploying (target) stages, compressing those deep models under cross-domain scenarios becomes more challenging. Although some of existing works have already explored cross-domain knowledge distillation for model compression, they are either biased to source data or heavily tangled between source and target data. To this end, we design a novel end-to-end framework called Universal and joint knowledge distillation (UNI-KD) for cross-domain model compression. In particular, we propose to transfer both the universal feature-level knowledge across source and target domains and the joint logit-level knowledge shared by both domains from the teacher to the student model via an adversarial learning scheme. More specifically, a feature-domain discriminator is employed to align teacher's and student's representations for universal knowledge transfer. A data-domain discriminator is utilized to prioritize the domain-shared samples for joint knowledge transfer. Extensive experimental results on four time series datasets demonstrate the superiority of our proposed method over state-of-the-art (SOTA) benchmarks.  ( 2 min )
    Auxiliary Functions as Koopman Observables: Data-Driven Polynomial Optimization for Dynamical Systems. (arXiv:2303.01483v2 [math.DS] UPDATED)
    We present a flexible data-driven method for dynamical system analysis that does not require explicit model discovery. The method is rooted in well-established techniques for approximating the Koopman operator from data and is implemented as a semidefinite program that can be solved numerically. Furthermore, the method is agnostic of whether data is generated through a deterministic or stochastic process, so its implementation requires no prior adjustments by the user to accommodate these different scenarios. Rigorous convergence results justify the applicability of the method, while also extending and uniting similar results from across the literature. Examples on discovering Lyapunov functions, performing ergodic optimization, and bounding extrema over attractors for both deterministic and stochastic dynamics exemplify these convergence results and demonstrate the performance of the method.
    One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention. (arXiv:2307.03576v1 [cs.LG])
    Recent works have empirically analyzed in-context learning and shown that transformers trained on synthetic linear regression tasks can learn to implement ridge regression, which is the Bayes-optimal predictor, given sufficient capacity [Aky\"urek et al., 2023], while one-layer transformers with linear self-attention and no MLP layer will learn to implement one step of gradient descent (GD) on a least-squares linear regression objective [von Oswald et al., 2022]. However, the theory behind these observations remains poorly understood. We theoretically study transformers with a single layer of linear self-attention, trained on synthetic noisy linear regression data. First, we mathematically show that when the covariates are drawn from a standard Gaussian distribution, the one-layer transformer which minimizes the pre-training loss will implement a single step of GD on the least-squares linear regression objective. Then, we find that changing the distribution of the covariates and weight vector to a non-isotropic Gaussian distribution has a strong impact on the learned algorithm: the global minimizer of the pre-training loss now implements a single step of $\textit{pre-conditioned}$ GD. However, if only the distribution of the responses is changed, then this does not have a large effect on the learned algorithm: even when the response comes from a more general family of $\textit{nonlinear}$ functions, the global minimizer of the pre-training loss still implements a single step of GD on a least-squares linear regression objective.
    When does the ID algorithm fail?. (arXiv:2307.03750v1 [stat.ME])
    The ID algorithm solves the problem of identification of interventional distributions of the form p(Y | do(a)) in graphical causal models, and has been formulated in a number of ways [12, 9, 6]. The ID algorithm is sound (outputs the correct functional of the observed data distribution whenever p(Y | do(a)) is identified in the causal model represented by the input graph), and complete (explicitly flags as a failure any input p(Y | do(a)) whenever this distribution is not identified in the causal model represented by the input graph). The reference [9] provides a result, the so called "hedge criterion" (Corollary 3), which aims to give a graphical characterization of situations when the ID algorithm fails to identify its input in terms of a structure in the input graph called the hedge. While the ID algorithm is, indeed, a sound and complete algorithm, and the hedge structure does arise whenever the input distribution is not identified, Corollary 3 presented in [9] is incorrect as stated. In this note, I outline the modern presentation of the ID algorithm, discuss a simple counterexample to Corollary 3, and provide a number of graphical characterizations of the ID algorithm failing to identify its input distribution.
    BOF-UCB: A Bayesian-Optimistic Frequentist Algorithm for Non-Stationary Contextual Bandits. (arXiv:2307.03587v1 [cs.LG])
    We propose a novel Bayesian-Optimistic Frequentist Upper Confidence Bound (BOF-UCB) algorithm for stochastic contextual linear bandits in non-stationary environments. This unique combination of Bayesian and frequentist principles enhances adaptability and performance in dynamic settings. The BOF-UCB algorithm utilizes sequential Bayesian updates to infer the posterior distribution of the unknown regression parameter, and subsequently employs a frequentist approach to compute the Upper Confidence Bound (UCB) by maximizing the expected reward over the posterior distribution. We provide theoretical guarantees of BOF-UCB's performance and demonstrate its effectiveness in balancing exploration and exploitation on synthetic datasets and classical control tasks in a reinforcement learning setting. Our results show that BOF-UCB outperforms existing methods, making it a promising solution for sequential decision-making in non-stationary environments.
    When Fair Classification Meets Noisy Protected Attributes. (arXiv:2307.03306v1 [cs.LG])
    The operationalization of algorithmic fairness comes with several practical challenges, not the least of which is the availability or reliability of protected attributes in datasets. In real-world contexts, practical and legal impediments may prevent the collection and use of demographic data, making it difficult to ensure algorithmic fairness. While initial fairness algorithms did not consider these limitations, recent proposals aim to achieve algorithmic fairness in classification by incorporating noisiness in protected attributes or not using protected attributes at all. To the best of our knowledge, this is the first head-to-head study of fair classification algorithms to compare attribute-reliant, noise-tolerant and attribute-blind algorithms along the dual axes of predictivity and fairness. We evaluated these algorithms via case studies on four real-world datasets and synthetic perturbations. Our study reveals that attribute-blind and noise-tolerant fair classifiers can potentially achieve similar level of performance as attribute-reliant algorithms, even when protected attributes are noisy. However, implementing them in practice requires careful nuance. Our study provides insights into the practical implications of using fair classification algorithms in scenarios where protected attributes are noisy or partially available.
    Federated Unlearning via Active Forgetting. (arXiv:2307.03363v1 [cs.LG])
    The increasing concerns regarding the privacy of machine learning models have catalyzed the exploration of machine unlearning, i.e., a process that removes the influence of training data on machine learning models. This concern also arises in the realm of federated learning, prompting researchers to address the federated unlearning problem. However, federated unlearning remains challenging. Existing unlearning methods can be broadly categorized into two approaches, i.e., exact unlearning and approximate unlearning. Firstly, implementing exact unlearning, which typically relies on the partition-aggregation framework, in a distributed manner does not improve time efficiency theoretically. Secondly, existing federated (approximate) unlearning methods suffer from imprecise data influence estimation, significant computational burden, or both. To this end, we propose a novel federated unlearning framework based on incremental learning, which is independent of specific models and federated settings. Our framework differs from existing federated unlearning methods that rely on approximate retraining or data influence estimation. Instead, we leverage new memories to overwrite old ones, imitating the process of \textit{active forgetting} in neurology. Specifically, the model, intended to unlearn, serves as a student model that continuously learns from randomly initiated teacher models. To preserve catastrophic forgetting of non-target data, we utilize elastic weight consolidation to elastically constrain weight change. Extensive experiments on three benchmark datasets demonstrate the efficiency and effectiveness of our proposed method. The result of backdoor attacks demonstrates that our proposed method achieves satisfying completeness.
    STG-MTL: Scalable Task Grouping for Multi-Task Learning Using Data Map. (arXiv:2307.03374v1 [cs.LG])
    Multi-Task Learning (MTL) is a powerful technique that has gained popularity due to its performance improvement over traditional Single-Task Learning (STL). However, MTL is often challenging because there is an exponential number of possible task groupings, which can make it difficult to choose the best one, and some groupings might produce performance degradation due to negative interference between tasks. Furthermore, existing solutions are severely suffering from scalability issues, limiting any practical application. In our paper, we propose a new data-driven method that addresses these challenges and provides a scalable and modular solution for classification task grouping based on hand-crafted features, specifically Data Maps, which capture the training behavior for each classification task during the MTL training. We experiment with the method demonstrating its effectiveness, even on an unprecedented number of tasks (up to 100).
    Undecimated Wavelet Transform for Word Embedded Semantic Marginal Autoencoder in Security improvement and Denoising different Languages. (arXiv:2307.03679v1 [cs.CL])
    By combining the undecimated wavelet transform within a Word Embedded Semantic Marginal Autoencoder (WESMA), this research study provides a novel strategy for improving security measures and denoising multiple languages. The incorporation of these strategies is intended to address the issues of robustness, privacy, and multilingualism in data processing applications. The undecimated wavelet transform is used as a feature extraction tool to identify prominent language patterns and structural qualities in the input data. The proposed system may successfully capture significant information while preserving the temporal and geographical links within the data by employing this transform. This improves security measures by increasing the system's ability to detect abnormalities, discover hidden patterns, and distinguish between legitimate content and dangerous threats. The Word Embedded Semantic Marginal Autoencoder also functions as an intelligent framework for dimensionality and noise reduction. The autoencoder effectively learns the underlying semantics of the data and reduces noise components by exploiting word embeddings and semantic context. As a result, data quality and accuracy are increased in following processing stages. The suggested methodology is tested using a diversified dataset that includes several languages and security scenarios. The experimental results show that the proposed approach is effective in attaining security enhancement and denoising capabilities across multiple languages. The system is strong in dealing with linguistic variances, producing consistent outcomes regardless of the language used. Furthermore, incorporating the undecimated wavelet transform considerably improves the system's ability to efficiently address complex security concerns
    Evaluating the Effectiveness of Large Language Models in Representing Textual Descriptions of Geometry and Spatial Relations. (arXiv:2307.03678v1 [cs.CL])
    This research focuses on assessing the ability of large language models (LLMs) in representing geometries and their spatial relations. We utilize LLMs including GPT-2 and BERT to encode the well-known text (WKT) format of geometries and then feed their embeddings into classifiers and regressors to evaluate the effectiveness of the LLMs-generated embeddings for geometric attributes. The experiments demonstrate that while the LLMs-generated embeddings can preserve geometry types and capture some spatial relations (up to 73% accuracy), challenges remain in estimating numeric values and retrieving spatially related objects. This research highlights the need for improvement in terms of capturing the nuances and complexities of the underlying geospatial data and integrating domain knowledge to support various GeoAI applications using foundation models.
    SAR: Generalization of Physiological Agility and Dexterity via Synergistic Action Representation. (arXiv:2307.03716v1 [cs.RO])
    Learning effective continuous control policies in high-dimensional systems, including musculoskeletal agents, remains a significant challenge. Over the course of biological evolution, organisms have developed robust mechanisms for overcoming this complexity to learn highly sophisticated strategies for motor control. What accounts for this robust behavioral flexibility? Modular control via muscle synergies, i.e. coordinated muscle co-contractions, is considered to be one putative mechanism that enables organisms to learn muscle control in a simplified and generalizable action space. Drawing inspiration from this evolved motor control strategy, we use physiologically accurate human hand and leg models as a testbed for determining the extent to which a Synergistic Action Representation (SAR) acquired from simpler tasks facilitates learning more complex tasks. We find in both cases that SAR-exploiting policies significantly outperform end-to-end reinforcement learning. Policies trained with SAR were able to achieve robust locomotion on a wide set of terrains with high sample efficiency, while baseline approaches failed to learn meaningful behaviors. Additionally, policies trained with SAR on a multiobject manipulation task significantly outperformed (>70% success) baseline approaches (<20% success). Both of these SAR-exploiting policies were also found to generalize zero-shot to out-of-domain environmental conditions, while policies that did not adopt SAR failed to generalize. Finally, we establish the generality of SAR on broader high-dimensional control problems using a robotic manipulation task set and a full-body humanoid locomotion task. To the best of our knowledge, this investigation is the first of its kind to present an end-to-end pipeline for discovering synergies and using this representation to learn high-dimensional continuous control across a wide diversity of tasks.
    Scalable Membership Inference Attacks via Quantile Regression. (arXiv:2307.03694v1 [cs.LG])
    Membership inference attacks are designed to determine, using black box access to trained models, whether a particular example was used in training or not. Membership inference can be formalized as a hypothesis testing problem. The most effective existing attacks estimate the distribution of some test statistic (usually the model's confidence on the true label) on points that were (and were not) used in training by training many \emph{shadow models} -- i.e. models of the same architecture as the model being attacked, trained on a random subsample of data. While effective, these attacks are extremely computationally expensive, especially when the model under attack is large. We introduce a new class of attacks based on performing quantile regression on the distribution of confidence scores induced by the model under attack on points that are not used in training. We show that our method is competitive with state-of-the-art shadow model attacks, while requiring substantially less compute because our attack requires training only a single model. Moreover, unlike shadow model attacks, our proposed attack does not require any knowledge of the architecture of the model under attack and is therefore truly ``black-box". We show the efficacy of this approach in an extensive series of experiments on various datasets and model architectures.
    INT-FP-QSim: Mixed Precision and Formats For Large Language Models and Vision Transformers. (arXiv:2307.03712v1 [cs.LG])
    The recent rise of large language models (LLMs) has resulted in increased efforts towards running LLMs at reduced precision. Running LLMs at lower precision supports resource constraints and furthers their democratization, enabling users to run billion-parameter LLMs on their personal devices. To supplement this ongoing effort, we propose INT-FP-QSim: an open-source simulator that enables flexible evaluation of LLMs and vision transformers at various numerical precisions and formats. INT-FP-QSim leverages existing open-source repositories such as TensorRT, QPytorch and AIMET for a combined simulator that supports various floating point and integer formats. With the help of our simulator, we survey the impact of different numerical formats on the performance of LLMs and vision transformers at 4-bit weights and 4-bit or 8-bit activations. We also compare recently proposed methods like Adaptive Block Floating Point, SmoothQuant, GPTQ and RPTQ on the model performances. We hope INT-FP-QSim will enable researchers to flexibly simulate models at various precisions to support further research in quantization of LLMs and vision transformers.
    Learning Theory of Distribution Regression with Neural Networks. (arXiv:2307.03487v1 [stat.ML])
    In this paper, we aim at establishing an approximation theory and a learning theory of distribution regression via a fully connected neural network (FNN). In contrast to the classical regression methods, the input variables of distribution regression are probability measures. Then we often need to perform a second-stage sampling process to approximate the actual information of the distribution. On the other hand, the classical neural network structure requires the input variable to be a vector. When the input samples are probability distributions, the traditional deep neural network method cannot be directly used and the difficulty arises for distribution regression. A well-defined neural network structure for distribution inputs is intensively desirable. There is no mathematical model and theoretical analysis on neural network realization of distribution regression. To overcome technical difficulties and address this issue, we establish a novel fully connected neural network framework to realize an approximation theory of functionals defined on the space of Borel probability measures. Furthermore, based on the established functional approximation results, in the hypothesis space induced by the novel FNN structure with distribution inputs, almost optimal learning rates for the proposed distribution regression model up to logarithmic terms are derived via a novel two-stage error decomposition technique.  ( 2 min )
    Accelerated Optimization Landscape of Linear-Quadratic Regulator. (arXiv:2307.03590v1 [math.OC])
    Linear-quadratic regulator (LQR) is a landmark problem in the field of optimal control, which is the concern of this paper. Generally, LQR is classified into state-feedback LQR (SLQR) and output-feedback LQR (OLQR) based on whether the full state is obtained. It has been suggested in existing literature that both the SLQR and the OLQR could be viewed as \textit{constrained nonconvex matrix optimization} problems in which the only variable to be optimized is the feedback gain matrix. In this paper, we introduce a first-order accelerated optimization framework of handling the LQR problem, and give its convergence analysis for the cases of SLQR and OLQR, respectively. Specifically, a Lipschiz Hessian property of LQR performance criterion is presented, which turns out to be a crucial property for the application of modern optimization techniques. For the SLQR problem, a continuous-time hybrid dynamic system is introduced, whose solution trajectory is shown to converge exponentially to the optimal feedback gain with Nesterov-optimal order $1-\frac{1}{\sqrt{\kappa}}$ ($\kappa$ the condition number). Then, the symplectic Euler scheme is utilized to discretize the hybrid dynamic system, and a Nesterov-type method with a restarting rule is proposed that preserves the continuous-time convergence rate, i.e., the discretized algorithm admits the Nesterov-optimal convergence order. For the OLQR problem, a Hessian-free accelerated framework is proposed, which is a two-procedure method consisting of semiconvex function optimization and negative curvature exploitation. In a time $\mathcal{O}(\epsilon^{-7/4}\log(1/\epsilon))$, the method can find an $\epsilon$-stationary point of the performance criterion; this entails that the method improves upon the $\mathcal{O}(\epsilon^{-2})$ complexity of vanilla gradient descent. Moreover, our method provides the second-order guarantee of stationary point.
    PAC bounds of continuous Linear Parameter-Varying systems related to neural ODEs. (arXiv:2307.03630v1 [cs.LG])
    We consider the problem of learning Neural Ordinary Differential Equations (neural ODEs) within the context of Linear Parameter-Varying (LPV) systems in continuous-time. LPV systems contain bilinear systems which are known to be universal approximators for non-linear systems. Moreover, a large class of neural ODEs can be embedded into LPV systems. As our main contribution we provide Probably Approximately Correct (PAC) bounds under stability for LPV systems related to neural ODEs. The resulting bounds have the advantage that they do not depend on the integration interval.
    Online Network Source Optimization with Graph-Kernel MAB. (arXiv:2307.03641v1 [cs.LG])
    We propose Grab-UCB, a graph-kernel multi-arms bandit algorithm to learn online the optimal source placement in large scale networks, such that the reward obtained from a priori unknown network processes is maximized. The uncertainty calls for online learning, which suffers however from the curse of dimensionality. To achieve sample efficiency, we describe the network processes with an adaptive graph dictionary model, which typically leads to sparse spectral representations. This enables a data-efficient learning framework, whose learning rate scales with the dimension of the spectral representation model instead of the one of the network. We then propose Grab-UCB, an online sequential decision strategy that learns the parameters of the spectral representation while optimizing the action strategy. We derive the performance guarantees that depend on network parameters, which further influence the learning curve of the sequential decision strategy We introduce a computationally simplified solving method, Grab-arm-Light, an algorithm that walks along the edges of the polytope representing the objective function. Simulations results show that the proposed online learning algorithm outperforms baseline offline methods that typically separate the learning phase from the testing one. The results confirm the theoretical findings, and further highlight the gain of the proposed online learning strategy in terms of cumulative regret, sample efficiency and computational complexity.
    Differentiable Turbulence. (arXiv:2307.03683v1 [physics.flu-dyn])
    Deep learning is increasingly becoming a promising pathway to improving the accuracy of sub-grid scale (SGS) turbulence closure models for large eddy simulations (LES). We leverage the concept of differentiable turbulence, whereby an end-to-end differentiable solver is used in combination with physics-inspired choices of deep learning architectures to learn highly effective and versatile SGS models for two-dimensional turbulent flow. We perform an in-depth analysis of the inductive biases in the chosen architectures, finding that the inclusion of small-scale non-local features is most critical to effective SGS modeling, while large-scale features can improve pointwise accuracy of the a-posteriori solution field. The filtered velocity gradient tensor can be mapped directly to the SGS stress via decomposition of the inputs and outputs into isotropic, deviatoric, and anti-symmetric components. We see that the model can generalize to a variety of flow configurations, including higher and lower Reynolds numbers and different forcing conditions. We show that the differentiable physics paradigm is more successful than offline, a-priori learning, and that hybrid solver-in-the-loop approaches to deep learning offer an ideal balance between computational efficiency, accuracy, and generalization. Our experiments provide physics-based recommendations for deep-learning based SGS modeling for generalizable closure modeling of turbulence.
    Incentive-Theoretic Bayesian Inference for Collaborative Science. (arXiv:2307.03748v1 [stat.ME])
    Contemporary scientific research is a distributed, collaborative endeavor, carried out by teams of researchers, regulatory institutions, funding agencies, commercial partners, and scientific bodies, all interacting with each other and facing different incentives. To maintain scientific rigor, statistical methods should acknowledge this state of affairs. To this end, we study hypothesis testing when there is an agent (e.g., a researcher or a pharmaceutical company) with a private prior about an unknown parameter and a principal (e.g., a policymaker or regulator) who wishes to make decisions based on the parameter value. The agent chooses whether to run a statistical trial based on their private prior and then the result of the trial is used by the principal to reach a decision. We show how the principal can conduct statistical inference that leverages the information that is revealed by an agent's strategic behavior -- their choice to run a trial or not. In particular, we show how the principal can design a policy to elucidate partial information about the agent's private prior beliefs and use this to control the posterior probability of the null. One implication is a simple guideline for the choice of significance threshold in clinical trials: the type-I error level should be set to be strictly less than the cost of the trial divided by the firm's profit if the trial is successful.
    PseudoCell: Hard Negative Mining as Pseudo Labeling for Deep Learning-Based Centroblast Cell Detection. (arXiv:2307.03211v1 [q-bio.QM])
    Patch classification models based on deep learning have been utilized in whole-slide images (WSI) of H&E-stained tissue samples to assist pathologists in grading follicular lymphoma patients. However, these approaches still require pathologists to manually identify centroblast cells and provide refined labels for optimal performance. To address this, we propose PseudoCell, an object detection framework to automate centroblast detection in WSI (source code is available at https://github.com/IoBT-VISTEC/PseudoCell.git). This framework incorporates centroblast labels from pathologists and combines them with pseudo-negative labels obtained from undersampled false-positive predictions using the cell's morphological features. By employing PseudoCell, pathologists' workload can be reduced as it accurately narrows down the areas requiring their attention during examining tissue. Depending on the confidence threshold, PseudoCell can eliminate 58.18-99.35% of non-centroblasts tissue areas on WSI. This study presents a practical centroblast prescreening method that does not require pathologists' refined labels for improvement. Detailed guidance on the practical implementation of PseudoCell is provided in the discussion section.  ( 2 min )
    Unpaired Multi-View Graph Clustering with Cross-View Structure Matching. (arXiv:2307.03476v1 [cs.LG])
    Multi-view clustering (MVC), which effectively fuses information from multiple views for better performance, has received increasing attention. Most existing MVC methods assume that multi-view data are fully paired, which means that the mappings of all corresponding samples between views are pre-defined or given in advance. However, the data correspondence is often incomplete in real-world applications due to data corruption or sensor differences, referred as the data-unpaired problem (DUP) in multi-view literature. Although several attempts have been made to address the DUP issue, they suffer from the following drawbacks: 1) Most methods focus on the feature representation while ignoring the structural information of multi-view data, which is essential for clustering tasks; 2) Existing methods for partially unpaired problems rely on pre-given cross-view alignment information, resulting in their inability to handle fully unpaired problems; 3) Their inevitable parameters degrade the efficiency and applicability of the models. To tackle these issues, we propose a novel parameter-free graph clustering framework termed Unpaired Multi-view Graph Clustering framework with Cross-View Structure Matching (UPMGC-SM). Specifically, unlike the existing methods, UPMGC-SM effectively utilizes the structural information from each view to refine cross-view correspondences. Besides, our UPMGC-SM is a unified framework for both the fully and partially unpaired multi-view graph clustering. Moreover, existing graph clustering methods can adopt our UPMGC-SM to enhance their ability for unpaired scenarios. Extensive experiments demonstrate the effectiveness and generalization of our proposed framework for both paired and unpaired datasets.
    Programmable Synthetic Tabular Data Generation. (arXiv:2307.03577v1 [cs.LG])
    Large amounts of tabular data remain underutilized due to privacy, data quality, and data sharing limitations. While training a generative model producing synthetic data resembling the original distribution addresses some of these issues, most applications require additional constraints from the generated data. Existing synthetic data approaches are limited as they typically only handle specific constraints, e.g., differential privacy (DP) or increased fairness, and lack an accessible interface for declaring general specifications. In this work, we introduce ProgSyn, the first programmable synthetic tabular data generation algorithm that allows for comprehensive customization over the generated data. To ensure high data quality while adhering to custom specifications, ProgSyn pre-trains a generative model on the original dataset and fine-tunes it on a differentiable loss automatically derived from the provided specifications. These can be programmatically declared using statistical and logical expressions, supporting a wide range of requirements (e.g., DP or fairness, among others). We conduct an extensive experimental evaluation of ProgSyn on a number of constraints, achieving a new state-of-the-art on some, while remaining general. For instance, at the same fairness level we achieve 2.3% higher downstream accuracy than the state-of-the-art in fair synthetic data generation on the Adult dataset. Overall, ProgSyn provides a versatile and accessible framework for generating constrained synthetic tabular data, allowing for specifications that generalize beyond the capabilities of prior work.  ( 2 min )
    Encoder-Decoder Networks for Self-Supervised Pretraining and Downstream Signal Bandwidth Regression on Digital Antenna Arrays. (arXiv:2307.03327v1 [cs.LG])
    This work presents the first applications of self-supervised learning applied to data from digital antenna arrays. Encoder-decoder networks are pretrained on digital array data to perform a self-supervised noisy-reconstruction task called channel in-painting, in which the network infers the contents of array data that has been masked with zeros. The self-supervised step requires no human-labeled data. The encoder architecture and weights from pretraining are then transferred to a new network with a task-specific decoder, and the new network is trained on a small volume of labeled data. We show that pretraining on the unlabeled data allows the new network to perform the task of bandwidth regression on the digital array data better than an equivalent network that is trained on the same labeled data from random initialization.
    GeoPhy: Differentiable Phylogenetic Inference via Geometric Gradients of Tree Topologies. (arXiv:2307.03675v1 [cs.LG])
    Phylogenetic inference, grounded in molecular evolution models, is essential for understanding the evolutionary relationships in biological data. Accounting for the uncertainty of phylogenetic tree variables, which include tree topologies and evolutionary distances on branches, is crucial for accurately inferring species relationships from molecular data and tasks requiring variable marginalization. Variational Bayesian methods are key to developing scalable, practical models; however, it remains challenging to conduct phylogenetic inference without restricting the combinatorially vast number of possible tree topologies. In this work, we introduce a novel, fully differentiable formulation of phylogenetic inference that leverages a unique representation of topological distributions in continuous geometric spaces. Through practical considerations on design spaces and control variates for gradient estimations, our approach, GeoPhy, enables variational inference without limiting the topological candidates. In experiments using real benchmark datasets, GeoPhy significantly outperformed other approximate Bayesian methods that considered whole topologies.
    Backward Feature Correction: How Deep Learning Performs Deep (Hierarchical) Learning. (arXiv:2001.04413v6 [cs.LG] UPDATED)
    Deep learning is also known as hierarchical learning, where the learner _learns_ to represent a complicated target function by decomposing it into a sequence of simpler functions to reduce sample and time complexity. This paper formally analyzes how multi-layer neural networks can perform such hierarchical learning _efficiently_ and _automatically_ by SGD on the training objective. On the conceptual side, we present a theoretical characterizations of how certain types of deep (i.e. super-constant layer) neural networks can still be sample and time efficiently trained on some hierarchical tasks, when no existing algorithm (including layerwise training, kernel method, etc) is known to be efficient. We establish a new principle called "backward feature correction", where the errors in the lower-level features can be automatically corrected when training together with the higher-level layers. We believe this is a key behind how deep learning is performing deep (hierarchical) learning, as opposed to layerwise learning or simulating some non-hierarchical method. On the technical side, we show for every input dimension $d > 0$, there is a concept class of degree $\omega(1)$ multi-variate polynomials so that, using $\omega(1)$-layer neural networks as learners, SGD can learn any function from this class in $\mathsf{poly}(d)$ time to any $\frac{1}{\mathsf{poly}(d)}$ error, through learning to represent it as a composition of $\omega(1)$ layers of quadratic functions using "backward feature correction." In contrast, we do not know any other simpler algorithm (including layerwise training, applying kernel method sequentially, training a two-layer network, etc) that can learn this concept class in $\mathsf{poly}(d)$ time even to any $d^{-0.01}$ error. As a side result, we prove $d^{\omega(1)}$ lower bounds for several non-hierarchical learners, including any kernel methods.
    Equivariant Spherical CNN for Data Efficient and High-Performance Medical Image Processing. (arXiv:2307.03298v1 [eess.IV])
    This work highlights the significance of equivariant networks as efficient and high-performance approaches for tomography applications. Our study builds upon the limitations of Convolutional Neural Networks (CNNs), which have shown promise in post-processing various medical imaging systems. However, the efficiency of conventional CNNs heavily relies on an undiminished and proper training set. To tackle this issue, in this study, we introduce an equivariant network, aiming to reduce CNN's dependency on specific training sets. We evaluate the efficacy of equivariant CNNs on spherical signals for tomographic medical imaging problems. Our results demonstrate superior quality and computational efficiency of spherical CNNs (SCNNs) in denoising and reconstructing benchmark problems. Furthermore, we propose a novel approach to employ SCNNs as a complement to conventional image reconstruction tools, enhancing the outcomes while reducing reliance on the training set. Across all cases, we observe a significant decrease in computational costs while maintaining the same or higher quality of image processing using SCNNs compared to CNNs. Additionally, we explore the potential of this network for broader tomography applications, particularly those requiring omnidirectional representation.  ( 2 min )
    Variational quantum regression algorithm with encoded data structure. (arXiv:2307.03334v1 [quant-ph])
    Variational quantum algorithms (VQAs) prevail to solve practical problems such as combinatorial optimization, quantum chemistry simulation, quantum machine learning, and quantum error correction on noisy quantum computers. For variational quantum machine learning, a variational algorithm with model interpretability built into the algorithm is yet to be exploited. In this paper, we construct a quantum regression algorithm and identify the direct relation of variational parameters to learned regression coefficients, while employing a circuit that directly encodes the data in quantum amplitudes reflecting the structure of the classical data table. The algorithm is particularly suitable for well-connected qubits. With compressed encoding and digital-analog gate operation, the run time complexity is logarithmically more advantageous than that for digital 2-local gate native hardware with the number of data entries encoded, a decent improvement in noisy intermediate-scale quantum computers and a minor improvement for large-scale quantum computing Our suggested method of compressed binary encoding offers a remarkable reduction in the number of physical qubits needed when compared to the traditional one-hot-encoding technique with the same input data. The algorithm inherently performs linear regression but can also be used easily for nonlinear regression by building nonlinear features into the training data. In terms of measured cost function which distinguishes a good model from a poor one for model training, it will be effective only when the number of features is much less than the number of records for the encoded data structure to be observable. To echo this finding and mitigate hardware noise in practice, the ensemble model training from the quantum regression model learning with important feature selection from regularization is incorporated and illustrated numerically.
    Simulation-free Schr\"odinger bridges via score and flow matching. (arXiv:2307.03672v1 [cs.LG])
    We present simulation-free score and flow matching ([SF]$^2$M), a simulation-free objective for inferring stochastic dynamics given unpaired source and target samples drawn from arbitrary distributions. Our method generalizes both the score-matching loss used in the training of diffusion models and the recently proposed flow matching loss used in the training of continuous normalizing flows. [SF]$^2$M interprets continuous-time stochastic generative modeling as a Schr\"odinger bridge (SB) problem. It relies on static entropy-regularized optimal transport, or a minibatch approximation, to efficiently learn the SB without simulating the learned stochastic process. We find that [SF]$^2$M is more efficient and gives more accurate solutions to the SB problem than simulation-based methods from prior work. Finally, we apply [SF]$^2$M to the problem of learning cell dynamics from snapshot data. Notably, [SF]$^2$M is the first method to accurately model cell dynamics in high dimensions and can recover known gene regulatory networks from simulated data.
    F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning. (arXiv:2004.11145v2 [cs.LG] UPDATED)
    Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications, due to non-interactivity between agents, curse of dimensionality and computation complexity. Hence, several decentralized MARL algorithms are motivated. However, existing decentralized methods only handle the fully cooperative setting where massive information needs to be transmitted in training. The block coordinate gradient descent scheme they used for successive independent actor and critic steps can simplify the calculation, but it causes serious bias. In this paper, we propose a flexible fully decentralized actor-critic MARL framework, which can combine most of actor-critic methods, and handle large-scale general cooperative multi-agent setting. A primal-dual hybrid gradient descent type algorithm framework is designed to learn individual agents separately for decentralization. From the perspective of each agent, policy improvement and value evaluation are jointly optimized, which can stabilize multi-agent policy learning. Furthermore, our framework can achieve scalability and stability for large-scale environment and reduce information transmission, by the parameter sharing mechanism and a novel modeling-other-agents methods based on theory-of-mind and online supervised learning. Sufficient experiments in cooperative Multi-agent Particle Environment and StarCraft II show that our decentralized MARL instantiation algorithms perform competitively against conventional centralized and decentralized methods.
    QIGen: Generating Efficient Kernels for Quantized Inference on Large Language Models. (arXiv:2307.03738v1 [cs.LG])
    We present ongoing work on a new automatic code generation approach for supporting quantized generative inference on LLMs such as LLaMA or OPT on off-the-shelf CPUs. Our approach is informed by the target architecture and a performance model, including both hardware characteristics and method-specific accuracy constraints. Results on CPU-based inference for LLaMA models show that our approach can lead to high performance and high accuracy, comparing favorably to the best existing open-source solution. A preliminary implementation is available at https://github.com/IST-DASLab/QIGen.
    Hyperspectral and Multispectral Image Fusion Using the Conditional Denoising Diffusion Probabilistic Model. (arXiv:2307.03423v1 [eess.IV])
    Hyperspectral images (HSI) have a large amount of spectral information reflecting the characteristics of matter, while their spatial resolution is low due to the limitations of imaging technology. Complementary to this are multispectral images (MSI), e.g., RGB images, with high spatial resolution but insufficient spectral bands. Hyperspectral and multispectral image fusion is a technique for acquiring ideal images that have both high spatial and high spectral resolution cost-effectively. Many existing HSI and MSI fusion algorithms rely on known imaging degradation models, which are often not available in practice. In this paper, we propose a deep fusion method based on the conditional denoising diffusion probabilistic model, called DDPM-Fus. Specifically, the DDPM-Fus contains the forward diffusion process which gradually adds Gaussian noise to the high spatial resolution HSI (HrHSI) and another reverse denoising process which learns to predict the desired HrHSI from its noisy version conditioning on the corresponding high spatial resolution MSI (HrMSI) and low spatial resolution HSI (LrHSI). Once the training is completes, the proposed DDPM-Fus implements the reverse process on the test HrMSI and LrHSI to generate the fused HrHSI. Experiments conducted on one indoor and two remote sensing datasets show the superiority of the proposed model when compared with other advanced deep learningbased fusion methods. The codes of this work will be opensourced at this address: https://github.com/shuaikaishi/DDPMFus for reproducibility.  ( 3 min )
    Polybot: Training One Policy Across Robots While Embracing Variability. (arXiv:2307.03719v1 [cs.RO])
    Reusing large datasets is crucial to scale vision-based robotic manipulators to everyday scenarios due to the high cost of collecting robotic datasets. However, robotic platforms possess varying control schemes, camera viewpoints, kinematic configurations, and end-effector morphologies, posing significant challenges when transferring manipulation skills from one platform to another. To tackle this problem, we propose a set of key design decisions to train a single policy for deployment on multiple robotic platforms. Our framework first aligns the observation and action spaces of our policy across embodiments via utilizing wrist cameras and a unified, but modular codebase. To bridge the remaining domain shift, we align our policy's internal representations across embodiments through contrastive learning. We evaluate our method on a dataset collected over 60 hours spanning 6 tasks and 3 robots with varying joint configurations and sizes: the WidowX 250S, the Franka Emika Panda, and the Sawyer. Our results demonstrate significant improvements in success rate and sample efficiency for our policy when using new task data collected on a different robot, validating our proposed design decisions. More details and videos can be found on our anonymized project website: https://sites.google.com/view/polybot-multirobot
    MALIBO: Meta-learning for Likelihood-free Bayesian Optimization. (arXiv:2307.03565v1 [cs.LG])
    Bayesian optimization (BO) is a popular method to optimize costly black-box functions. While traditional BO optimizes each new target task from scratch, meta-learning has emerged as a way to leverage knowledge from related tasks to optimize new tasks faster. However, existing meta-learning BO methods rely on surrogate models that suffer from scalability issues and are sensitive to observations with different scales and noise types across tasks. Moreover, they often overlook the uncertainty associated with task similarity. This leads to unreliable task adaptation when only limited observations are obtained or when the new tasks differ significantly from the related tasks. To address these limitations, we propose a novel meta-learning BO approach that bypasses the surrogate model and directly learns the utility of queries across tasks. Our method explicitly models task uncertainty and includes an auxiliary model to enable robust adaptation to new tasks. Extensive experiments show that our method demonstrates strong anytime performance and outperforms state-of-the-art meta-learning BO methods in various benchmarks.
    Smoothing the Edges: A General Framework for Smooth Optimization in Sparse Regularization using Hadamard Overparametrization. (arXiv:2307.03571v1 [cs.LG])
    This paper introduces a smooth method for (structured) sparsity in $\ell_q$ and $\ell_{p,q}$ regularized optimization problems. Optimization of these non-smooth and possibly non-convex problems typically relies on specialized procedures. In contrast, our general framework is compatible with prevalent first-order optimization methods like Stochastic Gradient Descent and accelerated variants without any required modifications. This is accomplished through a smooth optimization transfer, comprising an overparametrization of selected model parameters using Hadamard products and a change of penalties. In the overparametrized problem, smooth and convex $\ell_2$ regularization of the surrogate parameters induces non-smooth and non-convex $\ell_q$ or $\ell_{p,q}$ regularization in the original parametrization. We show that our approach yields not only matching global minima but also equivalent local minima. This is particularly useful in non-convex sparse regularization, where finding global minima is NP-hard and local minima are known to generalize well. We provide a comprehensive overview consolidating various literature strands on sparsity-inducing parametrizations and propose meaningful extensions to existing approaches. The feasibility of our approach is evaluated through numerical experiments, which demonstrate that its performance is on par with or surpasses commonly used implementations of convex and non-convex regularization methods.
    Guideline for Trustworthy Artificial Intelligence -- AI Assessment Catalog. (arXiv:2307.03681v1 [cs.CY])
    Artificial Intelligence (AI) has made impressive progress in recent years and represents a key technology that has a crucial impact on the economy and society. However, it is clear that AI and business models based on it can only reach their full potential if AI applications are developed according to high quality standards and are effectively protected against new AI risks. For instance, AI bears the risk of unfair treatment of individuals when processing personal data e.g., to support credit lending or staff recruitment decisions. The emergence of these new risks is closely linked to the fact that the behavior of AI applications, particularly those based on Machine Learning (ML), is essentially learned from large volumes of data and is not predetermined by fixed programmed rules. Thus, the issue of the trustworthiness of AI applications is crucial and is the subject of numerous major publications by stakeholders in politics, business and society. In addition, there is mutual agreement that the requirements for trustworthy AI, which are often described in an abstract way, must now be made clear and tangible. One challenge to overcome here relates to the fact that the specific quality criteria for an AI application depend heavily on the application context and possible measures to fulfill them in turn depend heavily on the AI technology used. Lastly, practical assessment procedures are needed to evaluate whether specific AI applications have been developed according to adequate quality standards. This AI assessment catalog addresses exactly this point and is intended for two target groups: Firstly, it provides developers with a guideline for systematically making their AI applications trustworthy. Secondly, it guides assessors and auditors on how to examine AI applications for trustworthiness in a structured way.  ( 3 min )
    Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms. (arXiv:2307.03357v1 [cs.LG])
    Many machine learning tasks can be formulated as a stochastic compositional optimization (SCO) problem such as reinforcement learning, AUC maximization, and meta-learning, where the objective function involves a nested composition associated with an expectation. While a significant amount of studies has been devoted to studying the convergence behavior of SCO algorithms, there is little work on understanding their generalization, i.e., how these learning algorithms built from training examples would behave on future test examples. In this paper, we provide the stability and generalization analysis of stochastic compositional gradient descent algorithms through the lens of algorithmic stability in the framework of statistical learning theory. Firstly, we introduce a stability concept called compositional uniform stability and establish its quantitative relation with generalization for SCO problems. Then, we establish the compositional uniform stability results for two popular stochastic compositional gradient descent algorithms, namely SCGD and SCSC. Finally, we derive dimension-independent excess risk bounds for SCGD and SCSC by trade-offing their stability results and optimization errors. To the best of our knowledge, these are the first-ever-known results on stability and generalization analysis of stochastic compositional gradient descent algorithms.  ( 2 min )
    Mitigating Negative Transfer with Task Awareness for Sexism, Hate Speech, and Toxic Language Detection. (arXiv:2307.03377v1 [cs.CL])
    This paper proposes a novelty approach to mitigate the negative transfer problem. In the field of machine learning, the common strategy is to apply the Single-Task Learning approach in order to train a supervised model to solve a specific task. Training a robust model requires a lot of data and a significant amount of computational resources, making this solution unfeasible in cases where data are unavailable or expensive to gather. Therefore another solution, based on the sharing of information between tasks, has been developed: Multi-Task Learning (MTL). Despite the recent developments regarding MTL, the problem of negative transfer has still to be solved. Negative transfer is a phenomenon that occurs when noisy information is shared between tasks, resulting in a drop in performance. This paper proposes a new approach to mitigate the negative transfer problem based on the task awareness concept. The proposed approach results in diminishing the negative transfer together with an improvement of performance over classic MTL solution. Moreover, the proposed approach has been implemented in two unified architectures to detect Sexism, Hate Speech, and Toxic Language in text comments. The proposed architectures set a new state-of-the-art both in EXIST-2021 and HatEval-2019 benchmarks.  ( 2 min )
    Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs. (arXiv:2307.03393v1 [cs.LG])
    Learning on Graphs has attracted immense attention due to its wide real-world applications. The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in general knowledge and profound semantic understanding. In recent years, Large Language Models (LLMs) have been proven to possess extensive common knowledge and powerful semantic comprehension abilities that have revolutionized existing workflows to handle text data. In this paper, we aim to explore the potential of LLMs in graph machine learning, especially the node classification task, and investigate two possible pipelines: LLMs-as-Enhancers and LLMs-as-Predictors. The former leverages LLMs to enhance nodes' text attributes with their massive knowledge and then generate predictions through GNNs. The latter attempts to directly employ LLMs as standalone predictors. We conduct comprehensive and systematical studies on these two pipelines under various settings. From comprehensive empirical results, we make original observations and find new insights that open new possibilities and suggest promising directions to leverage LLMs for learning on graphs.  ( 2 min )
    On Formal Feature Attribution and Its Approximation. (arXiv:2307.03380v1 [cs.AI])
    Recent years have witnessed the widespread use of artificial intelligence (AI) algorithms and machine learning (ML) models. Despite their tremendous success, a number of vital problems like ML model brittleness, their fairness, and the lack of interpretability warrant the need for the active developments in explainable artificial intelligence (XAI) and formal ML model verification. The two major lines of work in XAI include feature selection methods, e.g. Anchors, and feature attribution techniques, e.g. LIME and SHAP. Despite their promise, most of the existing feature selection and attribution approaches are susceptible to a range of critical issues, including explanation unsoundness and out-of-distribution sampling. A recent formal approach to XAI (FXAI) although serving as an alternative to the above and free of these issues suffers from a few other limitations. For instance and besides the scalability limitation, the formal approach is unable to tackle the feature attribution problem. Additionally, a formal explanation despite being formally sound is typically quite large, which hampers its applicability in practical settings. Motivated by the above, this paper proposes a way to apply the apparatus of formal XAI to the case of feature attribution based on formal explanation enumeration. Formal feature attribution (FFA) is argued to be advantageous over the existing methods, both formal and non-formal. Given the practical complexity of the problem, the paper then proposes an efficient technique for approximating exact FFA. Finally, it offers experimental evidence of the effectiveness of the proposed approximate FFA in comparison to the existing feature attribution algorithms not only in terms of feature importance and but also in terms of their relative order.  ( 3 min )
    Teaching Arithmetic to Small Transformers. (arXiv:2307.03381v1 [cs.LG])
    Large language models like GPT-4 exhibit emergent capabilities across general-purpose tasks, such as basic arithmetic, when trained on extensive text data, even though these tasks are not explicitly encoded by the unsupervised, next-token prediction objective. This study investigates how small transformers, trained from random initialization, can efficiently learn arithmetic operations such as addition, multiplication, and elementary functions like square root, using the next-token prediction objective. We first demonstrate that conventional training data is not the most effective for arithmetic learning, and simple formatting changes can significantly improve accuracy. This leads to sharp phase transitions as a function of training data scale, which, in some cases, can be explained through connections to low-rank matrix completion. Building on prior work, we then train on chain-of-thought style data that includes intermediate step results. Even in the complete absence of pretraining, this approach significantly and simultaneously improves accuracy, sample complexity, and convergence speed. We also study the interplay between arithmetic and text data during training and examine the effects of few-shot prompting, pretraining, and model scale. Additionally, we discuss length generalization challenges. Our work highlights the importance of high-quality, instructive data that considers the particular characteristics of the next-word prediction objective for rapidly eliciting arithmetic capabilities.  ( 2 min )
    Incentive Allocation in Vertical Federated Learning Based on Bankruptcy Problem. (arXiv:2307.03515v1 [cs.LG])
    Vertical federated learning (VFL) is a promising approach for collaboratively training machine learning models using private data partitioned vertically across different parties. Ideally in a VFL setting, the active party (party possessing features of samples with labels) benefits by improving its machine learning model through collaboration with some passive parties (parties possessing additional features of the same samples without labels) in a privacy preserving manner. However, motivating passive parties to participate in VFL can be challenging. In this paper, we focus on the problem of allocating incentives to the passive parties by the active party based on their contributions to the VFL process. We formulate this problem as a variant of the Nucleolus game theory concept, known as the Bankruptcy Problem, and solve it using the Talmud's division rule. We evaluate our proposed method on synthetic and real-world datasets and show that it ensures fairness and stability in incentive allocation among passive parties who contribute their data to the federated model. Additionally, we compare our method to the existing solution of calculating Shapley values and show that our approach provides a more efficient solution with fewer computations.  ( 2 min )
    Optimal Scalarizations for Sublinear Hypervolume Regret. (arXiv:2307.03288v1 [cs.LG])
    Scalarization is a general technique that can be deployed in any multiobjective setting to reduce multiple objectives into one, such as recently in RLHF for training reward models that align human preferences. Yet some have dismissed this classical approach because linear scalarizations are known to miss concave regions of the Pareto frontier. To that end, we aim to find simple non-linear scalarizations that can explore a diverse set of $k$ objectives on the Pareto frontier, as measured by the dominated hypervolume. We show that hypervolume scalarizations with uniformly random weights are surprisingly optimal for provably minimizing the hypervolume regret, achieving an optimal sublinear regret bound of $O(T^{-1/k})$, with matching lower bounds that preclude any algorithm from doing better asymptotically. As a theoretical case study, we consider the multiobjective stochastic linear bandits problem and demonstrate that by exploiting the sublinear regret bounds of the hypervolume scalarizations, we can derive a novel non-Euclidean analysis that produces improved hypervolume regret bounds of $\tilde{O}( d T^{-1/2} + T^{-1/k})$. We support our theory with strong empirical performance of using simple hypervolume scalarizations that consistently outperforms both the linear and Chebyshev scalarizations, as well as standard multiobjective algorithms in bayesian optimization, such as EHVI.  ( 2 min )
    Suppressing unknown disturbances to dynamical systems using machine learning. (arXiv:2307.03690v1 [eess.SY])
    Identifying and suppressing unknown disturbances to dynamical systems is a problem with applications in many different fields. In this Letter, we present a model-free method to identify and suppress an unknown disturbance to an unknown system based only on previous observations of the system under the influence of a known forcing function. We find that, under very mild restrictions on the training function, our method is able to robustly identify and suppress a large class of unknown disturbances. We illustrate our scheme with an example where a chaotic disturbance to the Lorenz system is identified and suppressed.  ( 2 min )
    HoughLaneNet: Lane Detection with Deep Hough Transform and Dynamic Convolution. (arXiv:2307.03494v1 [cs.CV])
    The task of lane detection has garnered considerable attention in the field of autonomous driving due to its complexity. Lanes can present difficulties for detection, as they can be narrow, fragmented, and often obscured by heavy traffic. However, it has been observed that the lanes have a geometrical structure that resembles a straight line, leading to improved lane detection results when utilizing this characteristic. To address this challenge, we propose a hierarchical Deep Hough Transform (DHT) approach that combines all lane features in an image into the Hough parameter space. Additionally, we refine the point selection method and incorporate a Dynamic Convolution Module to effectively differentiate between lanes in the original image. Our network architecture comprises a backbone network, either a ResNet or Pyramid Vision Transformer, a Feature Pyramid Network as the neck to extract multi-scale features, and a hierarchical DHT-based feature aggregation head to accurately segment each lane. By utilizing the lane features in the Hough parameter space, the network learns dynamic convolution kernel parameters corresponding to each lane, allowing the Dynamic Convolution Module to effectively differentiate between lane features. Subsequently, the lane features are fed into the feature decoder, which predicts the final position of the lane. Our proposed network structure demonstrates improved performance in detecting heavily occluded or worn lane images, as evidenced by our extensive experimental results, which show that our method outperforms or is on par with state-of-the-art techniques.  ( 3 min )
    A Vulnerability of Attribution Methods Using Pre-Softmax Scores. (arXiv:2307.03305v1 [cs.LG])
    We discuss a vulnerability involving a category of attribution methods used to provide explanations for the outputs of convolutional neural networks working as classifiers. It is known that this type of networks are vulnerable to adversarial attacks, in which imperceptible perturbations of the input may alter the outputs of the model. In contrast, here we focus on effects that small modifications in the model may cause on the attribution method without altering the model outputs.  ( 2 min )
    CSCLog: A Component Subsequence Correlation-Aware Log Anomaly Detection Method. (arXiv:2307.03359v1 [cs.LG])
    Anomaly detection based on system logs plays an important role in intelligent operations, which is a challenging task due to the extremely complex log patterns. Existing methods detect anomalies by capturing the sequential dependencies in log sequences, which ignore the interactions of subsequences. To this end, we propose CSCLog, a Component Subsequence Correlation-Aware Log anomaly detection method, which not only captures the sequential dependencies in subsequences, but also models the implicit correlations of subsequences. Specifically, subsequences are extracted from log sequences based on components and the sequential dependencies in subsequences are captured by Long Short-Term Memory Networks (LSTMs). An implicit correlation encoder is introduced to model the implicit correlations of subsequences adaptively. In addition, Graph Convolution Networks (GCNs) are employed to accomplish the information interactions of subsequences. Finally, attention mechanisms are exploited to fuse the embeddings of all subsequences. Extensive experiments on four publicly available log datasets demonstrate the effectiveness of CSCLog, outperforming the best baseline by an average of 7.41% in Macro F1-Measure.  ( 2 min )
    Optimal Bandwidth Selection for DENCLUE. (arXiv:2307.03206v1 [cs.LG])
    In modern day industry, clustering algorithms are daily routines of algorithm engineers. Although clustering algorithms experienced rapid growth before 2010. Innovation related to the research topic has stagnated after deep learning became the de facto industrial standard for machine learning applications. In 2007, a density-based clustering algorithm named DENCLUE was invented to solve clustering problem for nonlinear data structures. However, its parameter selection problem was largely neglected until 2011. In this paper, we propose a new approach to compute the optimal parameters for the DENCLUE algorithm, and discuss its performance in the experiment section.  ( 2 min )
    On the Evolution of (Hateful) Memes by Means of Multimodal Contrastive Learning. (arXiv:2212.06573v2 [cs.SI] UPDATED)
    The dissemination of hateful memes online has adverse effects on social media platforms and the real world. Detecting hateful memes is challenging, one of the reasons being the evolutionary nature of memes; new hateful memes can emerge by fusing hateful connotations with other cultural ideas or symbols. In this paper, we propose a framework that leverages multimodal contrastive learning models, in particular OpenAI's CLIP, to identify targets of hateful content and systematically investigate the evolution of hateful memes. We find that semantic regularities exist in CLIP-generated embeddings that describe semantic relationships within the same modality (images) or across modalities (images and text). Leveraging this property, we study how hateful memes are created by combining visual elements from multiple images or fusing textual information with a hateful image. We demonstrate the capabilities of our framework for analyzing the evolution of hateful memes by focusing on antisemitic memes, particularly the Happy Merchant meme. Using our framework on a dataset extracted from 4chan, we find 3.3K variants of the Happy Merchant meme, with some linked to specific countries, persons, or organizations. We envision that our framework can be used to aid human moderators by flagging new variants of hateful memes so that moderators can manually verify them and mitigate the problem of hateful content online.
    AI-UPV at EXIST 2023 -- Sexism Characterization Using Large Language Models Under The Learning with Disagreements Regime. (arXiv:2307.03385v1 [cs.CL])
    With the increasing influence of social media platforms, it has become crucial to develop automated systems capable of detecting instances of sexism and other disrespectful and hateful behaviors to promote a more inclusive and respectful online environment. Nevertheless, these tasks are considerably challenging considering different hate categories and the author's intentions, especially under the learning with disagreements regime. This paper describes AI-UPV team's participation in the EXIST (sEXism Identification in Social neTworks) Lab at CLEF 2023. The proposed approach aims at addressing the task of sexism identification and characterization under the learning with disagreements paradigm by training directly from the data with disagreements, without using any aggregated label. Yet, performances considering both soft and hard evaluations are reported. The proposed system uses large language models (i.e., mBERT and XLM-RoBERTa) and ensemble strategies for sexism identification and classification in English and Spanish. In particular, our system is articulated in three different pipelines. The ensemble approach outperformed the individual large language models obtaining the best performances both adopting a soft and a hard label evaluation. This work describes the participation in all the three EXIST tasks, considering a soft evaluation, it obtained fourth place in Task 2 at EXIST and first place in Task 3, with the highest ICM-Soft of -2.32 and a normalized ICM-Soft of 0.79. The source code of our approaches is publicly available at https://github.com/AngelFelipeMP/Sexism-LLM-Learning-With-Disagreement.
    Evaluating Biased Attitude Associations of Language Models in an Intersectional Context. (arXiv:2307.03360v1 [cs.CY])
    Language models are trained on large-scale corpora that embed implicit biases documented in psychology. Valence associations (pleasantness/unpleasantness) of social groups determine the biased attitudes towards groups and concepts in social cognition. Building on this established literature, we quantify how social groups are valenced in English language models using a sentence template that provides an intersectional context. We study biases related to age, education, gender, height, intelligence, literacy, race, religion, sex, sexual orientation, social class, and weight. We present a concept projection approach to capture the valence subspace through contextualized word embeddings of language models. Adapting the projection-based approach to embedding association tests that quantify bias, we find that language models exhibit the most biased attitudes against gender identity, social class, and sexual orientation signals in language. We find that the largest and better-performing model that we study is also more biased as it effectively captures bias embedded in sociocultural data. We validate the bias evaluation method by overperforming on an intrinsic valence evaluation task. The approach enables us to measure complex intersectional biases as they are known to manifest in the outputs and applications of language models that perpetuate historical biases. Moreover, our approach contributes to design justice as it studies the associations of groups underrepresented in language such as transgender and homosexual individuals.
    Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck Cancer. (arXiv:2307.03427v1 [eess.IV])
    Survival prediction is crucial for cancer patients as it provides early prognostic information for treatment planning. Recently, deep survival models based on deep learning and medical images have shown promising performance for survival prediction. However, existing deep survival models are not well developed in utilizing multi-modality images (e.g., PET-CT) and in extracting region-specific information (e.g., the prognostic information in Primary Tumor (PT) and Metastatic Lymph Node (MLN) regions). In view of this, we propose a merging-diverging learning framework for survival prediction from multi-modality images. This framework has a merging encoder to fuse multi-modality information and a diverging decoder to extract region-specific information. In the merging encoder, we propose a Hybrid Parallel Cross-Attention (HPCA) block to effectively fuse multi-modality features via parallel convolutional layers and cross-attention transformers. In the diverging decoder, we propose a Region-specific Attention Gate (RAG) block to screen out the features related to lesion regions. Our framework is demonstrated on survival prediction from PET-CT images in Head and Neck (H&N) cancer, by designing an X-shape merging-diverging hybrid transformer network (named XSurv). Our XSurv combines the complementary information in PET and CT images and extracts the region-specific prognostic information in PT and MLN regions. Extensive experiments on the public dataset of HEad and neCK TumOR segmentation and outcome prediction challenge (HECKTOR 2022) demonstrate that our XSurv outperforms state-of-the-art survival prediction methods.
    Distilled Pruning: Using Synthetic Data to Win the Lottery. (arXiv:2307.03364v1 [cs.LG])
    This work introduces a novel approach to pruning deep learning models by using distilled data. Unlike conventional strategies which primarily focus on architectural or algorithmic optimization, our method reconsiders the role of data in these scenarios. Distilled datasets capture essential patterns from larger datasets, and we demonstrate how to leverage this capability to enable a computationally efficient pruning process. Our approach can find sparse, trainable subnetworks (a.k.a. Lottery Tickets) up to 5x faster than Iterative Magnitude Pruning at comparable sparsity on CIFAR-10. The experimental results highlight the potential of using distilled data for resource-efficient neural network pruning, model compression, and neural architecture search.
    DWReCO at CheckThat! 2023: Enhancing Subjectivity Detection through Style-based Data Sampling. (arXiv:2307.03550v1 [cs.CL])
    This paper describes our submission for the subjectivity detection task at the CheckThat! Lab. To tackle class imbalances in the task, we have generated additional training materials with GPT-3 models using prompts of different styles from a subjectivity checklist based on journalistic perspective. We used the extended training set to fine-tune language-specific transformer models. Our experiments in English, German and Turkish demonstrate that different subjective styles are effective across all languages. In addition, we observe that the style-based oversampling is better than paraphrasing in Turkish and English. Lastly, the GPT-3 models sometimes produce lacklustre results when generating style-based texts in non-English languages.
    Steel Surface Roughness Parameter Calculations Using Lasers and Machine Learning Models. (arXiv:2307.03723v1 [cs.LG])
    Control of surface texture in strip steel is essential to meet customer requirements during galvanizing and temper rolling processes. Traditional methods rely on post-production stylus measurements, while on-line techniques offer non-contact and real-time measurements of the entire strip. However, ensuring accurate measurement is imperative for their effective utilization in the manufacturing pipeline. Moreover, accurate on-line measurements enable real-time adjustments of manufacturing processing parameters during production, ensuring consistent quality and the possibility of closed-loop control of the temper mill. In this study, we leverage state-of-the-art machine learning models to enhance the transformation of on-line measurements into significantly a more accurate Ra surface roughness metric. By comparing a selection of data-driven approaches, including both deep learning and non-deep learning methods, to the close-form transformation, we evaluate their potential for improving surface texture control in temper strip steel manufacturing.
    Roman Numeral Analysis with Graph Neural Networks: Onset-wise Predictions from Note-wise Features. (arXiv:2307.03544v1 [cs.SD])
    Roman Numeral analysis is the important task of identifying chords and their functional context in pieces of tonal music. This paper presents a new approach to automatic Roman Numeral analysis in symbolic music. While existing techniques rely on an intermediate lossy representation of the score, we propose a new method based on Graph Neural Networks (GNNs) that enable the direct description and processing of each individual note in the score. The proposed architecture can leverage notewise features and interdependencies between notes but yield onset-wise representation by virtue of our novel edge contraction algorithm. Our results demonstrate that ChordGNN outperforms existing state-of-the-art models, achieving higher accuracy in Roman Numeral analysis on the reference datasets. In addition, we investigate variants of our model using proposed techniques such as NADE, and post-processing of the chord predictions. The full source code for this work is available at https://github.com/manoskary/chordgnn
    Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs. (arXiv:2307.03411v1 [cs.LG])
    Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a technique for exploring the implicit higher-order correlations when training the embedding space of the graph. In this paper, we propose a hypergraph learning framework named LFH that is capable of dynamic hyperedge construction and attentive embedding update utilizing the heterogeneity attributes of the graph. Specifically, in our framework, the high-quality features are first generated by the pairwise fusion strategy that utilizes explicit graph structure information when generating initial node embedding. Afterwards, a hypergraph is constructed through the dynamic grouping of implicit hyperedges, followed by the type-specific hypergraph learning process. To evaluate the effectiveness of our proposed framework, we conduct comprehensive experiments on several popular datasets with eleven state-of-the-art models on both node classification and link prediction tasks, which fall into categories of homogeneous pairwise graph learning, heterogeneous pairwise graph learning, and hypergraph learning. The experiment results demonstrate a significant performance gain (average 12.5% in node classification and 13.3% in link prediction) compared with recent state-of-the-art methods.
    Region-Wise Attentive Multi-View Representation Learning for Urban Region Embeddings. (arXiv:2307.03212v1 [cs.CV])
    Urban region embedding is an important and yet highly challenging issue due to the complexity and constantly changing nature of urban data. To address the challenges, we propose a Region-Wise Multi-View Representation Learning (ROMER) to capture multi-view dependencies and learn expressive representations of urban regions without the constraints of rigid neighbourhood region conditions. Our model focus on learn urban region representation from multi-source urban data. First, we capture the multi-view correlations from mobility flow patterns, POI semantics and check-in dynamics. Then, we adopt global graph attention networks to learn similarity of any two vertices in graphs. To comprehensively consider and share features of multiple views, a two-stage fusion module is further proposed to learn weights with external attention to fuse multi-view embeddings. Extensive experiments for two downstream tasks on real-world datasets demonstrate that our model outperforms state-of-the-art methods by up to 17\% improvement.
    DEFT: Exploiting Gradient Norm Difference between Model Layers for Scalable Gradient Sparsification. (arXiv:2307.03500v1 [cs.LG])
    Gradient sparsification is a widely adopted solution for reducing the excessive communication traffic in distributed deep learning. However, most existing gradient sparsifiers have relatively poor scalability because of considerable computational cost of gradient selection and/or increased communication traffic owing to gradient build-up. To address these challenges, we propose a novel gradient sparsification scheme, DEFT, that partitions the gradient selection task into sub tasks and distributes them to workers. DEFT differs from existing sparsifiers, wherein every worker selects gradients among all gradients. Consequently, the computational cost can be reduced as the number of workers increases. Moreover, gradient build-up can be eliminated because DEFT allows workers to select gradients in partitions that are non-intersecting (between workers). Therefore, even if the number of workers increases, the communication traffic can be maintained as per user requirement. To avoid the loss of significance of gradient selection, DEFT selects more gradients in the layers that have a larger gradient norm than the other layers. Because every layer has a different computational load, DEFT allocates layers to workers using a bin-packing algorithm to maintain a balanced load of gradient selection between workers. In our empirical evaluation, DEFT shows a significant improvement in training performance in terms of speed in gradient selection over existing sparsifiers while achieving high convergence performance.
    Sparse Graphical Linear Dynamical Systems. (arXiv:2307.03210v1 [cs.LG])
    Time-series datasets are central in numerous fields of science and engineering, such as biomedicine, Earth observation, and network analysis. Extensive research exists on state-space models (SSMs), which are powerful mathematical tools that allow for probabilistic and interpretable learning on time series. Estimating the model parameters in SSMs is arguably one of the most complicated tasks, and the inclusion of prior knowledge is known to both ease the interpretation but also to complicate the inferential tasks. Very recent works have attempted to incorporate a graphical perspective on some of those model parameters, but they present notable limitations that this work addresses. More generally, existing graphical modeling tools are designed to incorporate either static information, focusing on statistical dependencies among independent random variables (e.g., graphical Lasso approach), or dynamic information, emphasizing causal relationships among time series samples (e.g., graphical Granger approaches). However, there are no joint approaches combining static and dynamic graphical modeling within the context of SSMs. This work proposes a novel approach to fill this gap by introducing a joint graphical modeling framework that bridges the static graphical Lasso model and a causal-based graphical approach for the linear-Gaussian SSM. We present DGLASSO (Dynamic Graphical Lasso), a new inference method within this framework that implements an efficient block alternating majorization-minimization algorithm. The algorithm's convergence is established by departing from modern tools from nonlinear analysis. Experimental validation on synthetic and real weather variability data showcases the effectiveness of the proposed model and inference algorithm.  ( 2 min )
    Discovering Hierarchical Achievements in Reinforcement Learning via Contrastive Learning. (arXiv:2307.03486v1 [cs.LG])
    Discovering achievements with a hierarchical structure on procedurally generated environments poses a significant challenge. This requires agents to possess a broad range of abilities, including generalization and long-term reasoning. Many prior methods are built upon model-based or hierarchical approaches, with the belief that an explicit module for long-term planning would be beneficial for learning hierarchical achievements. However, these methods require an excessive amount of environment interactions or large model sizes, limiting their practicality. In this work, we identify that proximal policy optimization (PPO), a simple and versatile model-free algorithm, outperforms the prior methods with recent implementation practices. Moreover, we find that the PPO agent can predict the next achievement to be unlocked to some extent, though with low confidence. Based on this observation, we propose a novel contrastive learning method, called achievement distillation, that strengthens the agent's capability to predict the next achievement. Our method exhibits a strong capacity for discovering hierarchical achievements and shows state-of-the-art performance on the challenging Crafter environment using fewer model parameters in a sample-efficient regime.  ( 2 min )
    Differential Privacy for Clustering Under Continual Observation. (arXiv:2307.03430v1 [cs.DS])
    We consider the problem of clustering privately a dataset in $\mathbb{R}^d$ that undergoes both insertion and deletion of points. Specifically, we give an $\varepsilon$-differentially private clustering mechanism for the $k$-means objective under continual observation. This is the first approximation algorithm for that problem with an additive error that depends only logarithmically in the number $T$ of updates. The multiplicative error is almost the same as non privately. To do so we show how to perform dimension reduction under continual observation and combine it with a differentially private greedy approximation algorithm for $k$-means. We also partially extend our results to the $k$-median problem.  ( 2 min )
    Machine Learning to detect cyber-attacks and discriminating the types of power system disturbances. (arXiv:2307.03323v1 [cs.LG])
    This research proposes a machine learning-based attack detection model for power systems, specifically targeting smart grids. By utilizing data and logs collected from Phasor Measuring Devices (PMUs), the model aims to learn system behaviors and effectively identify potential security boundaries. The proposed approach involves crucial stages including dataset pre-processing, feature selection, model creation, and evaluation. To validate our approach, we used a dataset used, consist of 15 separate datasets obtained from different PMUs, relay snort alarms and logs. Three machine learning models: Random Forest, Logistic Regression, and K-Nearest Neighbour were built and evaluated using various performance metrics. The findings indicate that the Random Forest model achieves the highest performance with an accuracy of 90.56% in detecting power system disturbances and has the potential in assisting operators in decision-making processes.  ( 2 min )
    Quantification of Uncertainty with Adversarial Models. (arXiv:2307.03217v1 [cs.LG])
    Quantifying uncertainty is important for actionable predictions in real-world applications. A crucial part of predictive uncertainty quantification is the estimation of epistemic uncertainty, which is defined as an integral of the product between a divergence function and the posterior. Current methods such as Deep Ensembles or MC dropout underperform at estimating the epistemic uncertainty, since they primarily consider the posterior when sampling models. We suggest Quantification of Uncertainty with Adversarial Models (QUAM) to better estimate the epistemic uncertainty. QUAM identifies regions where the whole product under the integral is large, not just the posterior. Consequently, QUAM has lower approximation error of the epistemic uncertainty compared to previous methods. Models for which the product is large correspond to adversarial models (not adversarial examples!). Adversarial models have both a high posterior as well as a high divergence between their predictions and that of a reference model. Our experiments show that QUAM excels in capturing epistemic uncertainty for deep learning models and outperforms previous methods on challenging tasks in the vision domain.  ( 2 min )
    Scalable High-Dimensional Multivariate Linear Regression for Feature-Distributed Data. (arXiv:2307.03410v1 [stat.ML])
    Feature-distributed data, referred to data partitioned by features and stored across multiple computing nodes, are increasingly common in applications with a large number of features. This paper proposes a two-stage relaxed greedy algorithm (TSRGA) for applying multivariate linear regression to such data. The main advantage of TSRGA is that its communication complexity does not depend on the feature dimension, making it highly scalable to very large data sets. In addition, for multivariate response variables, TSRGA can be used to yield low-rank coefficient estimates. The fast convergence of TSRGA is validated by simulation experiments. Finally, we apply the proposed TSRGA in a financial application that leverages unstructured data from the 10-K reports, demonstrating its usefulness in applications with many dense large-dimensional matrices.  ( 2 min )
    ITA: An Energy-Efficient Attention and Softmax Accelerator for Quantized Transformers. (arXiv:2307.03493v1 [cs.AR])
    Transformer networks have emerged as the state-of-the-art approach for natural language processing tasks and are gaining popularity in other domains such as computer vision and audio processing. However, the efficient hardware acceleration of transformer models poses new challenges due to their high arithmetic intensities, large memory requirements, and complex dataflow dependencies. In this work, we propose ITA, a novel accelerator architecture for transformers and related models that targets efficient inference on embedded systems by exploiting 8-bit quantization and an innovative softmax implementation that operates exclusively on integer values. By computing on-the-fly in streaming mode, our softmax implementation minimizes data movement and energy consumption. ITA achieves competitive energy efficiency with respect to state-of-the-art transformer accelerators with 16.9 TOPS/W, while outperforming them in area efficiency with 5.93 TOPS/mm$^2$ in 22 nm fully-depleted silicon-on-insulator technology at 0.8 V.  ( 2 min )
    Personalized Prediction of Recurrent Stress Events Using Self-Supervised Learning on Multimodal Time-Series Data. (arXiv:2307.03337v1 [cs.LG])
    Chronic stress can significantly affect physical and mental health. The advent of wearable technology allows for the tracking of physiological signals, potentially leading to innovative stress prediction and intervention methods. However, challenges such as label scarcity and data heterogeneity render stress prediction difficult in practice. To counter these issues, we have developed a multimodal personalized stress prediction system using wearable biosignal data. We employ self-supervised learning (SSL) to pre-train the models on each subject's data, allowing the models to learn the baseline dynamics of the participant's biosignals prior to fine-tuning the stress prediction task. We test our model on the Wearable Stress and Affect Detection (WESAD) dataset, demonstrating that our SSL models outperform non-SSL models while utilizing less than 5% of the annotations. These results suggest that our approach can personalize stress prediction to each user with minimal annotations. This paradigm has the potential to enable personalized prediction of a variety of recurring health events using complex multimodal data streams.  ( 2 min )
    Goal-Conditioned Predictive Coding as an Implicit Planner for Offline Reinforcement Learning. (arXiv:2307.03406v1 [cs.LG])
    Recent work has demonstrated the effectiveness of formulating decision making as a supervised learning problem on offline-collected trajectories. However, the benefits of performing sequence modeling on trajectory data is not yet clear. In this work we investigate if sequence modeling has the capability to condense trajectories into useful representations that can contribute to policy learning. To achieve this, we adopt a two-stage framework that first summarizes trajectories with sequence modeling techniques, and then employs these representations to learn a policy along with a desired goal. This design allows many existing supervised offline RL methods to be considered as specific instances of our framework. Within this framework, we introduce Goal-Conditioned Predicitve Coding (GCPC), an approach that brings powerful trajectory representations and leads to performant policies. We conduct extensive empirical evaluations on AntMaze, FrankaKitchen and Locomotion environments, and observe that sequence modeling has a significant impact on some decision making tasks. In addition, we demonstrate that GCPC learns a goal-conditioned latent representation about the future, which serves as an "implicit planner", and enables competitive performance on all three benchmarks.  ( 2 min )
    Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workers. (arXiv:2212.11498v2 [cs.LG] UPDATED)
    We envision a warehouse in which dozens of mobile robots and human pickers work together to collect and deliver items within the warehouse. The fundamental problem we tackle, called the order-picking problem, is how these worker agents must coordinate their movement and actions in the warehouse to maximise performance (e.g. order throughput). Established industry methods using heuristic approaches require large engineering efforts to optimise for innately variable warehouse configurations. In contrast, multi-agent reinforcement learning (MARL) can be flexibly applied to diverse warehouse configurations (e.g. size, layout, number/types of workers, item replenishment frequency), as the agents learn through experience how to optimally cooperate with one another. We develop hierarchical MARL algorithms in which a manager assigns goals to worker agents, and the policies of the manager and workers are co-trained toward maximising a global objective (e.g. pick rate). Our hierarchical algorithms achieve significant gains in sample efficiency and overall pick rates over baseline MARL algorithms in diverse warehouse configurations, and substantially outperform two established industry heuristics for order-picking systems.  ( 2 min )
    Robust Tickets Can Transfer Better: Drawing More Transferable Subnetworks in Transfer Learning. (arXiv:2304.11834v2 [cs.LG] UPDATED)
    Transfer learning leverages feature representations of deep neural networks (DNNs) pretrained on source tasks with rich data to empower effective finetuning on downstream tasks. However, the pretrained models are often prohibitively large for delivering generalizable representations, which limits their deployment on edge devices with constrained resources. To close this gap, we propose a new transfer learning pipeline, which leverages our finding that robust tickets can transfer better, i.e., subnetworks drawn with properly induced adversarial robustness can win better transferability over vanilla lottery ticket subnetworks. Extensive experiments and ablation studies validate that our proposed transfer learning pipeline can achieve enhanced accuracy-sparsity trade-offs across both diverse downstream tasks and sparsity patterns, further enriching the lottery ticket hypothesis.  ( 2 min )
    Harmonizing Feature Attributions Across Deep Learning Architectures: Enhancing Interpretability and Consistency. (arXiv:2307.02150v2 [cs.LG] UPDATED)
    Ensuring the trustworthiness and interpretability of machine learning models is critical to their deployment in real-world applications. Feature attribution methods have gained significant attention, which provide local explanations of model predictions by attributing importance to individual input features. This study examines the generalization of feature attributions across various deep learning architectures, such as convolutional neural networks (CNNs) and vision transformers. We aim to assess the feasibility of utilizing a feature attribution method as a future detector and examine how these features can be harmonized across multiple models employing distinct architectures but trained on the same data distribution. By exploring this harmonization, we aim to develop a more coherent and optimistic understanding of feature attributions, enhancing the consistency of local explanations across diverse deep-learning models. Our findings highlight the potential for harmonized feature attribution methods to improve interpretability and foster trust in machine learning applications, regardless of the underlying architecture.  ( 2 min )
    Language Models are Bounded Pragmatic Speakers: Understanding RLHF from a Bayesian Cognitive Modeling Perspective. (arXiv:2305.17760v4 [cs.CL] UPDATED)
    How do language models "think"? This paper formulates a probabilistic cognitive model called the bounded pragmatic speaker, which can characterize the operation of different variations of language models. Specifically, we demonstrate that large language models fine-tuned with reinforcement learning from human feedback (Ouyang et al., 2022) embody a model of thought that conceptually resembles a fast-and-slow model (Kahneman, 2011), which psychologists have attributed to humans. We discuss the limitations of reinforcement learning from human feedback as a fast-and-slow model of thought and propose avenues for expanding this framework. In essence, our research highlights the value of adopting a cognitive probabilistic modeling approach to gain insights into the comprehension, evaluation, and advancement of language models.  ( 2 min )
    Learning Homogenization for Elliptic Operators. (arXiv:2306.12006v2 [math.NA] UPDATED)
    Multiscale partial differential equations (PDEs) arise in various applications, and several schemes have been developed to solve them efficiently. Homogenization theory is a powerful methodology that eliminates the small-scale dependence, resulting in simplified equations that are computationally tractable. In the field of continuum mechanics, homogenization is crucial for deriving constitutive laws that incorporate microscale physics in order to formulate balance laws for the macroscopic quantities of interest. However, obtaining homogenized constitutive laws is often challenging as they do not in general have an analytic form and can exhibit phenomena not present on the microscale. In response, data-driven learning of the constitutive law has been proposed as appropriate for this task. However, a major challenge in data-driven learning approaches for this problem has remained unexplored: the impact of discontinuities and corner interfaces in the underlying material. These discontinuities in the coefficients affect the smoothness of the solutions of the underlying equations. Given the prevalence of discontinuous materials in continuum mechanics applications, it is important to address the challenge of learning in this context; in particular to develop underpinning theory to establish the reliability of data-driven methods in this scientific domain. The paper addresses this unexplored challenge by investigating the learnability of homogenized constitutive laws for elliptic operators in the presence of such complexities. Approximation theory is presented, and numerical experiments are performed which validate the theory for the solution operator defined by the cell-problem arising in homogenization for elliptic PDEs.  ( 3 min )
    Adaptive Strategies in Non-convex Optimization. (arXiv:2306.10278v2 [cs.LG] UPDATED)
    An algorithm is said to be adaptive to a certain parameter (of the problem) if it does not need a priori knowledge of such a parameter but performs competitively to those that know it. This dissertation presents our work on adaptive algorithms in following scenarios: 1. In the stochastic optimization setting, we only receive stochastic gradients and the level of noise in evaluating them greatly affects the convergence rate. Tuning is typically required when without prior knowledge of the noise scale in order to achieve the optimal rate. Considering this, we designed and analyzed noise-adaptive algorithms that can automatically ensure (near)-optimal rates under different noise scales without knowing it. 2. In training deep neural networks, the scales of gradient magnitudes in each coordinate can scatter across a very wide range unless normalization techniques, like BatchNorm, are employed. In such situations, algorithms not addressing this problem of gradient scales can behave very poorly. To mitigate this, we formally established the advantage of scale-free algorithms that adapt to the gradient scales and presented its real benefits in empirical experiments. 3. Traditional analyses in non-convex optimization typically rely on the smoothness assumption. Yet, this condition does not capture the properties of some deep learning objective functions, including the ones involving Long Short-Term Memory networks and Transformers. Instead, they satisfy a much more relaxed condition, with potentially unbounded smoothness. Under this condition, we show that a generalized SignSGD algorithm can theoretically match the best-known convergence rates obtained by SGD with gradient clipping but does not need explicit clipping at all, and it can empirically match the performance of Adam and beat others. Moreover, it can also be made to automatically adapt to the unknown relaxed smoothness.  ( 3 min )
    Offline Prioritized Experience Replay. (arXiv:2306.05412v2 [cs.LG] UPDATED)
    Offline reinforcement learning (RL) is challenged by the distributional shift problem. To address this problem, existing works mainly focus on designing sophisticated policy constraints between the learned policy and the behavior policy. However, these constraints are applied equally to well-performing and inferior actions through uniform sampling, which might negatively affect the learned policy. To alleviate this issue, we propose Offline Prioritized Experience Replay (OPER), featuring a class of priority functions designed to prioritize highly-rewarding transitions, making them more frequently visited during training. Through theoretical analysis, we show that this class of priority functions induce an improved behavior policy, and when constrained to this improved policy, a policy-constrained offline RL algorithm is likely to yield a better solution. We develop two practical strategies to obtain priority weights by estimating advantages based on a fitted value network (OPER-A) or utilizing trajectory returns (OPER-R) for quick computation. OPER is a plug-and-play component for offline RL algorithms. As case studies, we evaluate OPER on five different algorithms, including BC, TD3+BC, Onestep RL, CQL, and IQL. Extensive experiments demonstrate that both OPER-A and OPER-R significantly improve the performance for all baseline methods. Codes and priority weights are availiable at https://github.com/sail-sg/OPER.  ( 2 min )
    Multiclass Online Learning and Uniform Convergence. (arXiv:2303.17716v2 [cs.LG] UPDATED)
    We study multiclass classification in the agnostic adversarial online learning setting. As our main result, we prove that any multiclass concept class is agnostically learnable if and only if its Littlestone dimension is finite. This solves an open problem studied by Daniely, Sabato, Ben-David, and Shalev-Shwartz (2011,2015) who handled the case when the number of classes (or labels) is bounded. We also prove a separation between online learnability and online uniform convergence by exhibiting an easy-to-learn class whose sequential Rademacher complexity is unbounded. Our learning algorithm uses the multiplicative weights algorithm, with a set of experts defined by executions of the Standard Optimal Algorithm on subsequences of size Littlestone dimension. We argue that the best expert has regret at most Littlestone dimension relative to the best concept in the class. This differs from the well-known covering technique of Ben-David, P\'{a}l, and Shalev-Shwartz (2009) for binary classification, where the best expert has regret zero.  ( 2 min )
    Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+. (arXiv:2303.16982v2 [physics.chem-ph] UPDATED)
    Recent developments in deep learning have made remarkable progress in speeding up the prediction of quantum chemical (QC) properties by removing the need for expensive electronic structure calculations like density functional theory. However, previous methods learned from 1D SMILES sequences or 2D molecular graphs failed to achieve high accuracy as QC properties primarily depend on the 3D equilibrium conformations optimized by electronic structure methods, far different from the sequence-type and graph-type data. In this paper, we propose a novel approach called Uni-Mol+ to tackle this challenge. Uni-Mol+ first generates a raw 3D molecule conformation from inexpensive methods such as RDKit. Then, the raw conformation is iteratively updated to its target DFT equilibrium conformation using neural networks, and the learned conformation will be used to predict the QC properties. To effectively learn this update process towards the equilibrium conformation, we introduce a two-track Transformer model backbone and train it with the QC property prediction task. We also design a novel approach to guide the model's training process. Our extensive benchmarking results demonstrate that the proposed Uni-Mol+ significantly improves the accuracy of QC property prediction in various datasets. We have made the code and model publicly available at \url{https://github.com/dptech-corp/Uni-Mol}.  ( 2 min )
    Identifying Patient-Specific Root Causes with the Heteroscedastic Noise Model. (arXiv:2205.13085v2 [stat.ML] UPDATED)
    Complex diseases are caused by a multitude of factors that may differ between patients even within the same diagnostic category. A few underlying root causes may nevertheless initiate the development of disease within each patient. We therefore focus on identifying patient-specific root causes of disease, which we equate to the sample-specific predictivity of the exogenous error terms in a structural equation model. We generalize from the linear setting to the heteroscedastic noise model where $Y = m(X) + \varepsilon\sigma(X)$ with non-linear functions $m(X)$ and $\sigma(X)$ representing the conditional mean and mean absolute deviation, respectively. This model preserves identifiability but introduces non-trivial challenges that require a customized algorithm called Generalized Root Causal Inference (GRCI) to extract the error terms correctly. GRCI recovers patient-specific root causes more accurately than existing alternatives.  ( 2 min )
    On discrete symmetries of robotics systems: A group-theoretic and data-driven analysis. (arXiv:2302.10433v3 [cs.RO] UPDATED)
    We present a comprehensive study on discrete morphological symmetries of dynamical systems, which are commonly observed in biological and artificial locomoting systems, such as legged, swimming, and flying animals/robots/virtual characters. These symmetries arise from the presence of one or more planes/axis of symmetry in the system's morphology, resulting in harmonious duplication and distribution of body parts. Significantly, we characterize how morphological symmetries extend to symmetries in the system's dynamics, optimal control policies, and in all proprioceptive and exteroceptive measurements related to the system's dynamics evolution. In the context of data-driven methods, symmetry represents an inductive bias that justifies the use of data augmentation or symmetric function approximators. To tackle this, we present a theoretical and practical framework for identifying the system's morphological symmetry group $\G$ and characterizing the symmetries in proprioceptive and exteroceptive data measurements. We then exploit these symmetries using data augmentation and $\G$-equivariant neural networks. Our experiments on both synthetic and real-world applications provide empirical evidence of the advantageous outcomes resulting from the exploitation of these symmetries, including improved sample efficiency, enhanced generalization, and reduction of trainable parameters.  ( 2 min )
    RObotic MAnipulation Network (ROMAN) $\unicode{x2013}$ Hybrid Hierarchical Learning for Solving Complex Sequential Tasks. (arXiv:2307.00125v2 [cs.RO] UPDATED)
    Solving long sequential tasks poses a significant challenge in embodied artificial intelligence. Enabling a robotic system to perform diverse sequential tasks with a broad range of manipulation skills is an active area of research. In this work, we present a Hybrid Hierarchical Learning framework, the Robotic Manipulation Network (ROMAN), to address the challenge of solving multiple complex tasks over long time horizons in robotic manipulation. ROMAN achieves task versatility and robust failure recovery by integrating behavioural cloning, imitation learning, and reinforcement learning. It consists of a central manipulation network that coordinates an ensemble of various neural networks, each specialising in distinct re-combinable sub-tasks to generate their correct in-sequence actions for solving complex long-horizon manipulation tasks. Experimental results show that by orchestrating and activating these specialised manipulation experts, ROMAN generates correct sequential activations for accomplishing long sequences of sophisticated manipulation tasks and achieving adaptive behaviours beyond demonstrations, while exhibiting robustness to various sensory noises. These results demonstrate the significance and versatility of ROMAN's dynamic adaptability featuring autonomous failure recovery capabilities, and highlight its potential for various autonomous manipulation tasks that demand adaptive motor skills.  ( 2 min )
    Federated Variational Inference Methods for Structured Latent Variable Models. (arXiv:2302.03314v2 [stat.ML] UPDATED)
    Federated learning methods enable model training across distributed data sources without data leaving their original locations and have gained increasing interest in various fields. However, existing approaches are limited, excluding many structured probabilistic models. We present a general and elegant solution based on structured variational inference, widely used in Bayesian machine learning, adapted for the federated setting. Additionally, we provide a communication-efficient variant analogous to the canonical FedAvg algorithm. The proposed algorithms' effectiveness is demonstrated, and their performance is compared with hierarchical Bayesian neural networks and topic models.  ( 2 min )
    Reward-Respecting Subtasks for Model-Based Reinforcement Learning. (arXiv:2202.03466v3 [cs.LG] UPDATED)
    To achieve the ambitious goals of artificial intelligence, reinforcement learning must include planning with a model of the world that is abstract in state and time. Deep learning has made progress with state abstraction, but temporal abstraction has rarely been used, despite extensively developed theory based on the options framework. One reason for this is that the space of possible options is immense, and the methods previously proposed for option discovery do not take into account how the option models will be used in planning. Options are typically discovered by posing subsidiary tasks, such as reaching a bottleneck state or maximizing the cumulative sum of a sensory signal other than reward. Each subtask is solved to produce an option, and then a model of the option is learned and made available to the planning process. In most previous work, the subtasks ignore the reward on the original problem, whereas we propose subtasks that use the original reward plus a bonus based on a feature of the state at the time the option terminates. We show that option models obtained from such reward-respecting subtasks are much more likely to be useful in planning than eigenoptions, shortest path options based on bottleneck states, or reward-respecting options generated by the option-critic. Reward respecting subtasks strongly constrain the space of options and thereby also provide a partial solution to the problem of option discovery. Finally, we show how values, policies, options, and models can all be learned online and off-policy using standard algorithms and general value functions.  ( 3 min )
    k-strip: A novel segmentation algorithm in k-space for the application of skull stripping. (arXiv:2205.09706v2 [eess.IV] UPDATED)
    Objectives: Present a novel deep learning-based skull stripping algorithm for magnetic resonance imaging (MRI) that works directly in the information rich k-space. Materials and Methods: Using two datasets from different institutions with a total of 36,900 MRI slices, we trained a deep learning-based model to work directly with the complex raw k-space data. Skull stripping performed by HD-BET (Brain Extraction Tool) in the image domain were used as the ground truth. Results: Both datasets were very similar to the ground truth (DICE scores of 92\%-98\% and Hausdorff distances of under 5.5 mm). Results on slices above the eye-region reach DICE scores of up to 99\%, while the accuracy drops in regions around the eyes and below, with partially blurred output. The output of k-strip often smoothed edges at the demarcation to the skull. Binary masks are created with an appropriate threshold. Conclusion: With this proof-of-concept study, we were able to show the feasibility of working in the k-space frequency domain, preserving phase information, with consistent results. Future research should be dedicated to discovering additional ways the k-space can be used for innovative image analysis and further workflows.  ( 3 min )
    Avoiding Post-Processing with Event-Based Detection in Biomedical Signals. (arXiv:2209.11007v2 [eess.SP] UPDATED)
    Objective: Finding events of interest is a common task in biomedical signal processing. The detection of epileptic seizures and signal artefacts are two key examples. Epoch-based classification is the typical machine learning framework to detect such signal events because of the straightforward application of classical machine learning techniques. Usually, post-processing is required to achieve good performance and enforce temporal dependencies. Designing the right post-processing scheme to convert these classification outputs into events is a tedious, and labor-intensive element of this framework. Methods: We propose an event-based modeling framework that directly works with events as learning targets, stepping away from ad-hoc post-processing schemes to turn model outputs into events. We illustrate the practical power of this framework on simulated data and real-world data, comparing it to epoch-based modeling approaches. Results: We show that event-based modeling (without post-processing) performs on par with or better than epoch-based modeling with extensive post-processing. Conclusion: These results show the power of treating events as direct learning targets, instead of using ad-hoc post-processing to obtain them, severely reducing design effort. Significance: The event-based modeling framework can easily be applied to other event detection problems in signal processing, removing the need for intensive task-specific post-processing.  ( 2 min )
    Stability Analysis Framework for Particle-based Distance GANs with Wasserstein Gradient Flow. (arXiv:2307.01879v2 [cs.LG] UPDATED)
    In this paper, we investigate the training process of generative networks that use a type of probability density distance named particle-based distance as the objective function, e.g. MMD GAN, Cram\'er GAN, EIEG GAN. However, these GANs often suffer from the problem of unstable training. In this paper, we analyze the stability of the training process of these GANs from the perspective of probability density dynamics. In our framework, we regard the discriminator $D$ in these GANs as a feature transformation mapping that maps high dimensional data into a feature space, while the generator $G$ maps random variables to samples that resemble real data in terms of feature space. This perspective enables us to perform stability analysis for the training of GANs using the Wasserstein gradient flow of the probability density function. We find that the training process of the discriminator is usually unstable due to the formulation of $\min_G \max_D E(G, D)$ in GANs. To address this issue, we add a stabilizing term in the discriminator loss function. We conduct experiments to validate our stability analysis and stabilizing method.  ( 2 min )
    Evaluating Similitude and Robustness of Deep Image Denoising Models via Adversarial Attack. (arXiv:2306.16050v2 [cs.CV] UPDATED)
    Deep neural networks (DNNs) have shown superior performance comparing to traditional image denoising algorithms. However, DNNs are inevitably vulnerable while facing adversarial attacks. In this paper, we propose an adversarial attack method named denoising-PGD which can successfully attack all the current deep denoising models while keep the noise distribution almost unchanged. We surprisingly find that the current mainstream non-blind denoising models (DnCNN, FFDNet, ECNDNet, BRDNet), blind denoising models (DnCNN-B, Noise2Noise, RDDCNN-B, FAN), plug-and-play (DPIR, CurvPnP) and unfolding denoising models (DeamNet) almost share the same adversarial sample set on both grayscale and color images, respectively. Shared adversarial sample set indicates that all these models are similar in term of local behaviors at the neighborhood of all the test samples. Thus, we further propose an indicator to measure the local similarity of models, called robustness similitude. Non-blind denoising models are found to have high robustness similitude across each other, while hybrid-driven models are also found to have high robustness similitude with pure data-driven non-blind denoising models. According to our robustness assessment, data-driven non-blind denoising models are the most robust. We use adversarial training to complement the vulnerability to adversarial attacks. Moreover, the model-driven image denoising BM3D shows resistance on adversarial attacks.  ( 2 min )
    A Memetic Algorithm with Reinforcement Learning for Sociotechnical Production Scheduling. (arXiv:2212.10936v4 [cs.LG] UPDATED)
    The following interdisciplinary article presents a memetic algorithm with applying deep reinforcement learning (DRL) for solving practically oriented dual resource constrained flexible job shop scheduling problems (DRC-FJSSP). From research projects in industry, we recognize the need to consider flexible machines, flexible human workers, worker capabilities, setup and processing operations, material arrival times, complex job paths with parallel tasks for bill of material (BOM) manufacturing, sequence-dependent setup times and (partially) automated tasks in human-machine-collaboration. In recent years, there has been extensive research on metaheuristics and DRL techniques but focused on simple scheduling environments. However, there are few approaches combining metaheuristics and DRL to generate schedules more reliably and efficiently. In this paper, we first formulate a DRC-FJSSP to map complex industry requirements beyond traditional job shop models. Then we propose a scheduling framework integrating a discrete event simulation (DES) for schedule evaluation, considering parallel computing and multicriteria optimization. Here, a memetic algorithm is enriched with DRL to improve sequencing and assignment decisions. Through numerical experiments with real-world production data, we confirm that the framework generates feasible schedules efficiently and reliably for a balanced optimization of makespan (MS) and total tardiness (TT). Utilizing DRL instead of random metaheuristic operations leads to better results in fewer algorithm iterations and outperforms traditional approaches in such complex environments.  ( 3 min )
    Bicriteria Approximation Algorithms for Priority Matroid Median. (arXiv:2210.01888v2 [cs.DS] UPDATED)
    Fairness considerations have motivated new clustering problems and algorithms in recent years. In this paper we consider the Priority Matroid Median problem which generalizes the Priority $k$-Median problem that has recently been studied. The input consists of a set of facilities $\mathcal{F}$ and a set of clients $\mathcal{C}$ that lie in a metric space $(\mathcal{F} \cup \mathcal{C},d)$, and a matroid $\mathcal{M}=(\mathcal{F},\mathcal{I})$ over the facilities. In addition each client $j$ has a specified radius $r_j \ge 0$ and each facility $i \in \mathcal{F}$ has an opening cost $f_i$. The goal is to choose a subset $S \subseteq \mathcal{F}$ of facilities to minimize the $\sum_{i \in \mathcal{F}} f_i + \sum_{j \in \mathcal{C}} d(j,S)$ subject to two constraints: (i) $S$ is an independent set in $\mathcal{M}$ (that is $S \in \mathcal{I}$) and (ii) for each client $j$, its distance to an open facility is at most $r_j$ (that is, $d(j,S) \le r_j$). For this problem we describe the first bicriteria $(c_1,c_2)$ approximations for fixed constants $c_1,c_2$: the radius constraints of the clients are violated by at most a factor of $c_1$ and the objective cost is at most $c_2$ times the optimum cost. We also improve the previously known bicriteria approximation for the uniform radius setting ($r_j := L$ $\forall j \in \mathcal{C}$).  ( 2 min )
    Creativity of AI: Hierarchical Planning Model Learning for Facilitating Deep Reinforcement Learning. (arXiv:2112.09836v2 [cs.AI] UPDATED)
    Despite of achieving great success in real-world applications, Deep Reinforcement Learning (DRL) is still suffering from three critical issues, i.e., data efficiency, lack of the interpretability and transferability. Recent research shows that embedding symbolic knowledge into DRL is promising in addressing those challenges. Inspired by this, we introduce a novel deep reinforcement learning framework with symbolic options. Our framework features a loop training procedure, which enables guiding the improvement of policy by planning with planning models (including action models and hierarchical task network models) and symbolic options learned from interactive trajectories automatically. The learned symbolic options alleviate the dense requirement of expert domain knowledge and provide inherent interpretability of policies. Moreover, the transferability and data efficiency can be further improved by planning with the symbolic planning models. To validate the effectiveness of our framework, we conduct experiments on two domains, Montezuma's Revenge and Office World, respectively. The results demonstrate the comparable performance, improved data efficiency, interpretability and transferability.  ( 2 min )
    Composite Optimization Algorithms for Sigmoid Networks. (arXiv:2303.00589v3 [math.OC] UPDATED)
    In this paper, we use composite optimization algorithms to solve sigmoid networks. We equivalently transfer the sigmoid networks to a convex composite optimization and propose the composite optimization algorithms based on the linearized proximal algorithms and the alternating direction method of multipliers. Under the assumptions of the weak sharp minima and the regularity condition, the algorithm is guaranteed to converge to a globally optimal solution of the objective function even in the case of non-convex and non-smooth problems. Furthermore, the convergence results can be directly related to the amount of training data and provide a general guide for setting the size of sigmoid networks. Numerical experiments on Franke's function fitting and handwritten digit recognition show that the proposed algorithms perform satisfactorily and robustly.  ( 2 min )
    Continuous-Time Functional Diffusion Processes. (arXiv:2303.00800v2 [cs.LG] UPDATED)
    We introduce Functional Diffusion Processes (FDPs), which generalize score-based diffusion models to infinite-dimensional function spaces. FDPs require a new mathematical framework to describe the forward and backward dynamics, and several extensions to derive practical training objectives. These include infinite-dimensional versions of Girsanov theorem, in order to be able to compute an ELBO, and of the sampling theorem, in order to guarantee that functional evaluations in a countable set of points are equivalent to infinite-dimensional functions. We use FDPs to build a new breed of generative models in function spaces, which do not require specialized network architectures, and that can work with any kind of continuous data. Our results on real data show that FDPs achieve high-quality image generation, using a simple MLP architecture with orders of magnitude fewer parameters than existing diffusion models.  ( 2 min )
    TabLeak: Tabular Data Leakage in Federated Learning. (arXiv:2210.01785v2 [cs.LG] UPDATED)
    While federated learning (FL) promises to preserve privacy, recent works in the image and text domains have shown that training updates leak private client data. However, most high-stakes applications of FL (e.g., in healthcare and finance) use tabular data, where the risk of data leakage has not yet been explored. A successful attack for tabular data must address two key challenges unique to the domain: (i) obtaining a solution to a high-variance mixed discrete-continuous optimization problem, and (ii) enabling human assessment of the reconstruction as unlike for image and text data, direct human inspection is not possible. In this work we address these challenges and propose TabLeak, the first comprehensive reconstruction attack on tabular data. TabLeak is based on two key contributions: (i) a method which leverages a softmax relaxation and pooled ensembling to solve the optimization problem, and (ii) an entropy-based uncertainty quantification scheme to enable human assessment. We evaluate TabLeak on four tabular datasets for both FedSGD and FedAvg training protocols, and show that it successfully breaks several settings previously deemed safe. For instance, we extract large subsets of private data at >90% accuracy even at the large batch size of 128. Our findings demonstrate that current high-stakes tabular FL is excessively vulnerable to leakage attacks.  ( 2 min )
    Automatic Intrinsic Reward Shaping for Exploration in Deep Reinforcement Learning. (arXiv:2301.10886v4 [cs.LG] UPDATED)
    We present AIRS: Automatic Intrinsic Reward Shaping that intelligently and adaptively provides high-quality intrinsic rewards to enhance exploration in reinforcement learning (RL). More specifically, AIRS selects shaping function from a predefined set based on the estimated task return in real-time, providing reliable exploration incentives and alleviating the biased objective problem. Moreover, we develop an intrinsic reward toolkit to provide efficient and reliable implementations of diverse intrinsic reward approaches. We test AIRS on various tasks of MiniGrid, Procgen, and DeepMind Control Suite. Extensive simulation demonstrates that AIRS can outperform the benchmarking schemes and achieve superior performance with simple architecture.  ( 2 min )
    When and How to Fool Explainable Models (and Humans) with Adversarial Examples. (arXiv:2107.01943v2 [cs.LG] UPDATED)
    Reliable deployment of machine learning models such as neural networks continues to be challenging due to several limitations. Some of the main shortcomings are the lack of interpretability and the lack of robustness against adversarial examples or out-of-distribution inputs. In this exploratory review, we explore the possibilities and limits of adversarial attacks for explainable machine learning models. First, we extend the notion of adversarial examples to fit in explainable machine learning scenarios, in which the inputs, the output classifications and the explanations of the model's decisions are assessed by humans. Next, we propose a comprehensive framework to study whether (and how) adversarial examples can be generated for explainable models under human assessment, introducing and illustrating novel attack paradigms. In particular, our framework considers a wide range of relevant yet often ignored factors such as the type of problem, the user expertise or the objective of the explanations, in order to identify the attack strategies that should be adopted in each scenario to successfully deceive the model (and the human). The intention of these contributions is to serve as a basis for a more rigorous and realistic study of adversarial examples in the field of explainable machine learning.  ( 3 min )
    Layer Ensembles. (arXiv:2210.04882v3 [cs.LG] UPDATED)
    Deep Ensembles, as a type of Bayesian Neural Networks, can be used to estimate uncertainty on the prediction of multiple neural networks by collecting votes from each network and computing the difference in those predictions. In this paper, we introduce a method for uncertainty estimation that considers a set of independent categorical distributions for each layer of the network, giving many more possible samples with overlapped layers than in the regular Deep Ensembles. We further introduce an optimized inference procedure that reuses common layer outputs, achieving up to 19x speed up and reducing memory usage quadratically. We also show that the method can be further improved by ranking samples, resulting in models that require less memory and time to run while achieving higher uncertainty quality than Deep Ensembles.  ( 2 min )
    Black-Box Batch Active Learning for Regression. (arXiv:2302.08981v2 [cs.LG] UPDATED)
    Batch active learning is a popular approach for efficiently training machine learning models on large, initially unlabelled datasets by repeatedly acquiring labels for batches of data points. However, many recent batch active learning methods are white-box approaches and are often limited to differentiable parametric models: they score unlabeled points using acquisition functions based on model embeddings or first- and second-order derivatives. In this paper, we propose black-box batch active learning for regression tasks as an extension of white-box approaches. Crucially, our method only relies on model predictions. This approach is compatible with a wide range of machine learning models, including regular and Bayesian deep learning models and non-differentiable models such as random forests. It is rooted in Bayesian principles and utilizes recent kernel-based approaches. This allows us to extend a wide range of existing state-of-the-art white-box batch active learning methods (BADGE, BAIT, LCMD) to black-box models. We demonstrate the effectiveness of our approach through extensive experimental evaluations on regression datasets, achieving surprisingly strong performance compared to white-box approaches for deep learning models.  ( 2 min )
    Decentralized Learning over Wireless Networks: The Effect of Broadcast with Random Access. (arXiv:2305.07368v2 [cs.NI] UPDATED)
    In this work, we focus on the communication aspect of decentralized learning, which involves multiple agents training a shared machine learning model using decentralized stochastic gradient descent (D-SGD) over distributed data. In particular, we investigate the impact of broadcast transmission and probabilistic random access policy on the convergence performance of D-SGD, considering the broadcast nature of wireless channels and the link dynamics in the communication topology. Our results demonstrate that optimizing the access probability to maximize the expected number of successful links is a highly effective strategy for accelerating the system convergence.  ( 2 min )
    Breadth-First Pipeline Parallelism. (arXiv:2211.05953v2 [cs.DC] UPDATED)
    We introduce Breadth-First Pipeline Parallelism, a novel training schedule which optimizes the combination of pipeline and data parallelism. Breadth-First Pipeline Parallelism lowers training time, cost and memory usage by combining a high GPU utilization with a small batch size per GPU, and by making use of fully sharded data parallelism. Experimentally, we observed an increase of up to 43% in training throughput for a 52 billion-parameter model using a small batch size per GPU compared to Megatron-LM, which would reduce the training time and cost by the same amount on a large GPU cluster.  ( 2 min )
    GRAPHSHAP: Explaining Identity-Aware Graph Classifiers Through the Language of Motifs. (arXiv:2202.08815v2 [cs.LG] UPDATED)
    Most methods for explaining black-box classifiers (e.g. on tabular data, images, or time series) rely on measuring the impact that removing/perturbing features has on the model output. This forces the explanation language to match the classifier's feature space. However, when dealing with graph data, in which the basic features correspond to the edges describing the graph structure, this matching between features space and explanation language might not be appropriate. Decoupling the feature space (edges) from a desired high-level explanation language (such as motifs) is thus a major challenge towards developing actionable explanations for graph classification tasks. In this paper we introduce GRAPHSHAP, a Shapley-based approach able to provide motif-based explanations for identity-aware graph classifiers, assuming no knowledge whatsoever about the model or its training data: the only requirement is that the classifier can be queried as a black-box at will. For the sake of computational efficiency we explore a progressive approximation strategy and show how a simple kernel can efficiently approximate explanation scores, thus allowing GRAPHSHAP to scale on scenarios with a large explanation space (i.e. large number of motifs). We showcase GRAPHSHAP on a real-world brain-network dataset consisting of patients affected by Autism Spectrum Disorder and a control group. Our experiments highlight how the classification provided by a black-box model can be effectively explained by few connectomics patterns.  ( 3 min )
    Don't trust your eyes: on the (un)reliability of feature visualizations. (arXiv:2306.04719v4 [cs.CV] UPDATED)
    How do neural networks extract patterns from pixels? Feature visualizations attempt to answer this important question by visualizing highly activating patterns through optimization. Today, visualization methods form the foundation of our knowledge about the internal workings of neural networks, as a type of mechanistic interpretability. Here we ask: How reliable are feature visualizations? We start our investigation by developing network circuits that trick feature visualizations into showing arbitrary patterns that are completely disconnected from normal network behavior on natural input. We then provide evidence for a similar phenomenon occurring in standard, unmanipulated networks: feature visualizations are processed very differently from standard input, casting doubt on their ability to "explain" how neural networks process natural images. We underpin this empirical finding by theory proving that the set of functions that can be reliably understood by feature visualization is extremely small and does not include general black-box neural networks. Therefore, a promising way forward could be the development of networks that enforce certain structures in order to ensure more reliable feature visualizations.  ( 2 min )
    Self-Supervised Time Series Representation Learning via Cross Reconstruction Transformer. (arXiv:2205.09928v2 [cs.LG] UPDATED)
    Unsupervised/self-supervised representation learning in time series is critical since labeled samples are usually scarce in real-world scenarios. Existing approaches mainly leverage the contrastive learning framework, which automatically learns to understand the similar and dissimilar data pairs. Nevertheless, they are restricted to the prior knowledge of constructing pairs, cumbersome sampling policy, and unstable performances when encountering sampling bias. Also, few works have focused on effectively modeling across temporal-spectral relations to extend the capacity of representations. In this paper, we aim at learning representations for time series from a new perspective and propose Cross Reconstruction Transformer (CRT) to solve the aforementioned problems in a unified way. CRT achieves time series representation learning through a cross-domain dropping-reconstruction task. Specifically, we transform time series into the frequency domain and randomly drop certain parts in both time and frequency domains. Dropping can maximally preserve the global context compared to cropping and masking. Then a transformer architecture is utilized to adequately capture the cross-domain correlations between temporal and spectral information through reconstructing data in both domains, which is called Dropped Temporal-Spectral Modeling. To discriminate the representations in global latent space, we propose Instance Discrimination Constraint to reduce the mutual information between different time series and sharpen the decision boundaries. Additionally, we propose a specified curriculum learning strategy to optimize the CRT, which progressively increases the dropping ratio in the training process.  ( 3 min )
    Coupled Gradient Flows for Strategic Non-Local Distribution Shift. (arXiv:2307.01166v2 [cs.LG] UPDATED)
    We propose a novel framework for analyzing the dynamics of distribution shift in real-world systems that captures the feedback loop between learning algorithms and the distributions on which they are deployed. Prior work largely models feedback-induced distribution shift as adversarial or via an overly simplistic distribution-shift structure. In contrast, we propose a coupled partial differential equation model that captures fine-grained changes in the distribution over time by accounting for complex dynamics that arise due to strategic responses to algorithmic decision-making, non-local endogenous population interactions, and other exogenous sources of distribution shift. We consider two common settings in machine learning: cooperative settings with information asymmetries, and competitive settings where a learner faces strategic users. For both of these settings, when the algorithm retrains via gradient descent, we prove asymptotic convergence of the retraining procedure to a steady-state, both in finite and in infinite dimensions, obtaining explicit rates in terms of the model parameters. To do so we derive new results on the convergence of coupled PDEs that extends what is known on multi-species systems. Empirically, we show that our approach captures well-documented forms of distribution shifts like polarization and disparate impacts that simpler models cannot capture.  ( 2 min )
    SocNavGym: A Reinforcement Learning Gym for Social Navigation. (arXiv:2304.14102v2 [cs.RO] UPDATED)
    It is essential for autonomous robots to be socially compliant while navigating in human-populated environments. Machine Learning and, especially, Deep Reinforcement Learning have recently gained considerable traction in the field of Social Navigation. This can be partially attributed to the resulting policies not being bound by human limitations in terms of code complexity or the number of variables that are handled. Unfortunately, the lack of safety guarantees and the large data requirements by DRL algorithms make learning in the real world unfeasible. To bridge this gap, simulation environments are frequently used. We propose SocNavGym, an advanced simulation environment for social navigation that can generate a wide variety of social navigation scenarios and facilitates the development of intelligent social agents. SocNavGym is light-weight, fast, easy-to-use, and can be effortlessly configured to generate different types of social navigation scenarios. It can also be configured to work with different hand-crafted and data-driven social reward signals and to yield a variety of evaluation metrics to benchmark agents' performance. Further, we also provide a case study where a Dueling-DQN agent is trained to learn social-navigation policies using SocNavGym. The results provides evidence that SocNavGym can be used to train an agent from scratch to navigate in simple as well as complex social scenarios. Our experiments also show that the agents trained using the data-driven reward function displays more advanced social compliance in comparison to the heuristic-based reward function.  ( 3 min )
    Equivariance with Learned Canonicalization Functions. (arXiv:2211.06489v3 [cs.LG] UPDATED)
    Symmetry-based neural networks often constrain the architecture in order to achieve invariance or equivariance to a group of transformations. In this paper, we propose an alternative that avoids this architectural constraint by learning to produce canonical representations of the data. These canonicalization functions can readily be plugged into non-equivariant backbone architectures. We offer explicit ways to implement them for some groups of interest. We show that this approach enjoys universality while providing interpretable insights. Our main hypothesis, supported by our empirical results, is that learning a small neural network to perform canonicalization is better than using predefined heuristics. Our experiments show that learning the canonicalization function is competitive with existing techniques for learning equivariant functions across many tasks, including image classification, $N$-body dynamics prediction, point cloud classification and part segmentation, while being faster across the board.  ( 2 min )
    Detection of Groups with Biased Representation in Ranking. (arXiv:2301.00719v2 [cs.LG] UPDATED)
    Real-life tools for decision-making in many critical domains are based on ranking results. With the increasing awareness of algorithmic fairness, recent works have presented measures for fairness in ranking. Many of those definitions consider the representation of different ``protected groups'', in the top-$k$ ranked items, for any reasonable $k$. Given the protected groups, confirming algorithmic fairness is a simple task. However, the groups' definitions may be unknown in advance. In this paper, we study the problem of detecting groups with biased representation in the top-$k$ ranked items, eliminating the need to pre-define protected groups. The number of such groups possible can be exponential, making the problem hard. We propose efficient search algorithms for two different fairness measures: global representation bounds, and proportional representation. Then we propose a method to explain the bias in the representations of groups utilizing the notion of Shapley values. We conclude with an experimental study, showing the scalability of our approach and demonstrating the usefulness of the proposed algorithms.  ( 2 min )
    Elastic Decision Transformer. (arXiv:2307.02484v2 [cs.LG] UPDATED)
    This paper introduces Elastic Decision Transformer (EDT), a significant advancement over the existing Decision Transformer (DT) and its variants. Although DT purports to generate an optimal trajectory, empirical evidence suggests it struggles with trajectory stitching, a process involving the generation of an optimal or near-optimal trajectory from the best parts of a set of sub-optimal trajectories. The proposed EDT differentiates itself by facilitating trajectory stitching during action inference at test time, achieved by adjusting the history length maintained in DT. Further, the EDT optimizes the trajectory by retaining a longer history when the previous trajectory is optimal and a shorter one when it is sub-optimal, enabling it to "stitch" with a more optimal trajectory. Extensive experimentation demonstrates EDT's ability to bridge the performance gap between DT-based and Q Learning-based approaches. In particular, the EDT outperforms Q Learning-based methods in a multi-task regime on the D4RL locomotion benchmark and Atari games. Videos are available at: https://kristery.github.io/edt/  ( 2 min )
    Deep Optimal Transport for Domain Adaptation on SPD Manifolds. (arXiv:2201.05745v3 [cs.LG] UPDATED)
    In recent years, there has been significant interest in solving the domain adaptation (DA) problem on symmetric positive definite (SPD) manifolds within the machine learning community. This interest stems from the fact that complex neurophysiological data generated by medical equipment, such as electroencephalograms, magnetoencephalograms, and diffusion tensor imaging, often exhibit a shift in data distribution across different domains. These data representations, represented by signal covariance matrices, possess properties of symmetry and positive definiteness. However, directly applying previous experiences and solutions to the DA problem poses challenges due to the manipulation complexities of covariance matrices.To address this, our research introduces a category of deep learning-based transfer learning approaches called deep optimal transport. This category utilizes optimal transport theory and leverages the Log-Euclidean geometry for SPD manifolds. Additionally, we present a comprehensive categorization of existing geometric methods to tackle these problems effectively. This categorization provides practical solutions for specific DA problems, including handling discrepancies in marginal and conditional distributions between the source and target domains on the SPD manifold. To evaluate the effectiveness, we conduct experiments on three publicly available highly non-stationary cross-session brain-computer interface scenarios. Moreover, we provide visualization results on the SPD cone to offer further insights into the framework.  ( 3 min )
    A Comprehensive Multi-scale Approach for Speech and Dynamics Synchrony in Talking Head Generation. (arXiv:2307.03270v1 [cs.GR])
    Animating still face images with deep generative models using a speech input signal is an active research topic and has seen important recent progress. However, much of the effort has been put into lip syncing and rendering quality while the generation of natural head motion, let alone the audio-visual correlation between head motion and speech, has often been neglected. In this work, we propose a multi-scale audio-visual synchrony loss and a multi-scale autoregressive GAN to better handle short and long-term correlation between speech and the dynamics of the head and lips. In particular, we train a stack of syncer models on multimodal input pyramids and use these models as guidance in a multi-scale generator network to produce audio-aligned motion unfolding over diverse time scales. Our generator operates in the facial landmark domain, which is a standard low-dimensional head representation. The experiments show significant improvements over the state of the art in head motion dynamics quality and in multi-scale audio-visual synchrony both in the landmark domain and in the image domain.  ( 2 min )
    Analyzing the vulnerabilities in SplitFed Learning: Assessing the robustness against Data Poisoning Attacks. (arXiv:2307.03197v1 [cs.LG])
    Distributed Collaborative Machine Learning (DCML) is a potential alternative to address the privacy concerns associated with centralized machine learning. The Split learning (SL) and Federated Learning (FL) are the two effective learning approaches in DCML. Recently there have been an increased interest on the hybrid of FL and SL known as the SplitFed Learning (SFL). This research is the earliest attempt to study, analyze and present the impact of data poisoning attacks in SFL. We propose three kinds of novel attack strategies namely untargeted, targeted and distance-based attacks for SFL. All the attacks strategies aim to degrade the performance of the DCML-based classifier. We test the proposed attack strategies for two different case studies on Electrocardiogram signal classification and automatic handwritten digit recognition. A series of attack experiments were conducted by varying the percentage of malicious clients and the choice of the model split layer between the clients and the server. The results after the comprehensive analysis of attack strategies clearly convey that untargeted and distance-based poisoning attacks have greater impacts in evading the classifier outcomes compared to targeted attacks in SFL  ( 2 min )
    Assisting Clinical Decisions for Scarcely Available Treatment via Disentangled Latent Representation. (arXiv:2307.03315v1 [cs.LG])
    Extracorporeal membrane oxygenation (ECMO) is an essential life-supporting modality for COVID-19 patients who are refractory to conventional therapies. However, the proper treatment decision has been the subject of significant debate and it remains controversial about who benefits from this scarcely available and technically complex treatment option. To support clinical decisions, it is a critical need to predict the treatment need and the potential treatment and no-treatment responses. Targeting this clinical challenge, we propose Treatment Variational AutoEncoder (TVAE), a novel approach for individualized treatment analysis. TVAE is specifically designed to address the modeling challenges like ECMO with strong treatment selection bias and scarce treatment cases. TVAE conceptualizes the treatment decision as a multi-scale problem. We model a patient's potential treatment assignment and the factual and counterfactual outcomes as part of their intrinsic characteristics that can be represented by a deep latent variable model. The factual and counterfactual prediction errors are alleviated via a reconstruction regularization scheme together with semi-supervision, and the selection bias and the scarcity of treatment cases are mitigated by the disentangled and distribution-matched latent space and the label-balancing generative strategy. We evaluate TVAE on two real-world COVID-19 datasets: an international dataset collected from 1651 hospitals across 63 countries, and a institutional dataset collected from 15 hospitals. The results show that TVAE outperforms state-of-the-art treatment effect models in predicting both the propensity scores and factual outcomes on heterogeneous COVID-19 datasets. Additional experiments also show TVAE outperforms the best existing models in individual treatment effect estimation on the synthesized IHDP benchmark dataset.  ( 3 min )
    OmniBoost: Boosting Throughput of Heterogeneous Embedded Devices under Multi-DNN Workload. (arXiv:2307.03290v1 [cs.LG])
    Modern Deep Neural Networks (DNNs) exhibit profound efficiency and accuracy properties. This has introduced application workloads that comprise of multiple DNN applications, raising new challenges regarding workload distribution. Equipped with a diverse set of accelerators, newer embedded system present architectural heterogeneity, which current run-time controllers are unable to fully utilize. To enable high throughput in multi-DNN workloads, such a controller is ought to explore hundreds of thousands of possible solutions to exploit the underlying heterogeneity. In this paper, we propose OmniBoost, a lightweight and extensible multi-DNN manager for heterogeneous embedded devices. We leverage stochastic space exploration and we combine it with a highly accurate performance estimator to observe a x4.6 average throughput boost compared to other state-of-the-art methods. The evaluation was performed on the HiKey970 development board.  ( 2 min )
    ACDNet: Attention-guided Collaborative Decision Network for Effective Medication Recommendation. (arXiv:2307.03332v1 [cs.LG])
    Medication recommendation using Electronic Health Records (EHR) is challenging due to complex medical data. Current approaches extract longitudinal information from patient EHR to personalize recommendations. However, existing models often lack sufficient patient representation and overlook the importance of considering the similarity between a patient's medication records and specific medicines. Therefore, an Attention-guided Collaborative Decision Network (ACDNet) for medication recommendation is proposed in this paper. Specifically, ACDNet utilizes attention mechanism and Transformer to effectively capture patient health conditions and medication records by modeling their historical visits at both global and local levels. ACDNet also employs a collaborative decision framework, utilizing the similarity between medication records and medicine representation to facilitate the recommendation process. The experimental results on two extensive medical datasets, MIMIC-III and MIMIC-IV, clearly demonstrate that ACDNet outperforms state-of-the-art models in terms of Jaccard, PR-AUC, and F1 score, reaffirming its superiority. Moreover, the ablation experiments provide solid evidence of the effectiveness of each module in ACDNet, validating their contribution to the overall performance. Furthermore, a detailed case study reinforces the effectiveness of ACDNet in medication recommendation based on EHR data, showcasing its practical value in real-world healthcare scenarios.  ( 2 min )
    Empirical Analysis of a Segmentation Foundation Model in Prostate Imaging. (arXiv:2307.03266v1 [eess.IV])
    Most state-of-the-art techniques for medical image segmentation rely on deep-learning models. These models, however, are often trained on narrowly-defined tasks in a supervised fashion, which requires expensive labeled datasets. Recent advances in several machine learning domains, such as natural language generation have demonstrated the feasibility and utility of building foundation models that can be customized for various downstream tasks with little to no labeled data. This likely represents a paradigm shift for medical imaging, where we expect that foundation models may shape the future of the field. In this paper, we consider a recently developed foundation model for medical image segmentation, UniverSeg. We conduct an empirical evaluation study in the context of prostate imaging and compare it against the conventional approach of training a task-specific segmentation model. Our results and discussion highlight several important factors that will likely be important in the development and adoption of foundation models for medical image segmentation.  ( 2 min )
    Neural Network Field Theories: Non-Gaussianity, Actions, and Locality. (arXiv:2307.03223v1 [hep-th])
    Both the path integral measure in field theory and ensembles of neural networks describe distributions over functions. When the central limit theorem can be applied in the infinite-width (infinite-$N$) limit, the ensemble of networks corresponds to a free field theory. Although an expansion in $1/N$ corresponds to interactions in the field theory, others, such as in a small breaking of the statistical independence of network parameters, can also lead to interacting theories. These other expansions can be advantageous over the $1/N$-expansion, for example by improved behavior with respect to the universal approximation theorem. Given the connected correlators of a field theory, one can systematically reconstruct the action order-by-order in the expansion parameter, using a new Feynman diagram prescription whose vertices are the connected correlators. This method is motivated by the Edgeworth expansion and allows one to derive actions for neural network field theories. Conversely, the correspondence allows one to engineer architectures realizing a given field theory by representing action deformations as deformations of neural network parameter densities. As an example, $\phi^4$ theory is realized as an infinite-$N$ neural network field theory.  ( 2 min )
    Vision Language Transformers: A Survey. (arXiv:2307.03254v1 [cs.CV])
    Vision language tasks, such as answering questions about or generating captions that describe an image, are difficult tasks for computers to perform. A relatively recent body of research has adapted the pretrained transformer architecture introduced in \citet{vaswani2017attention} to vision language modeling. Transformer models have greatly improved performance and versatility over previous vision language models. They do so by pretraining models on a large generic datasets and transferring their learning to new tasks with minor changes in architecture and parameter values. This type of transfer learning has become the standard modeling practice in both natural language processing and computer vision. Vision language transformers offer the promise of producing similar advancements in tasks which require both vision and language. In this paper, we provide a broad synthesis of the currently available research on vision language transformer models and offer some analysis of their strengths, limitations and some open questions that remain.  ( 2 min )
    Scaling Laws Do Not Scale. (arXiv:2307.03201v1 [cs.LG])
    Recent work has proposed a power law relationship, referred to as ``scaling laws,'' between the performance of artificial intelligence (AI) models and aspects of those models' design (e.g., dataset size). In other words, as the size of a dataset (or model parameters, etc) increases, the performance of a given model trained on that dataset will correspondingly increase. However, while compelling in the aggregate, this scaling law relationship overlooks the ways that metrics used to measure performance may be precarious and contested, or may not correspond with how different groups of people may perceive the quality of models' output. In this paper, we argue that as the size of datasets used to train large AI models grows, the number of distinct communities (including demographic groups) whose data is included in a given dataset is likely to grow, each of whom may have different values. As a result, there is an increased risk that communities represented in a dataset may have values or preferences not captured by (or in the worst case, at odds with) the metrics used to evaluate model performance for scaling laws. We end the paper with implications for AI scaling laws -- that models may not, in fact, continue to improve as the datasets get larger -- at least not for all people or communities impacted by those models.  ( 2 min )
    On Invariance, Equivariance, Correlation and Convolution of Spherical Harmonic Representations for Scalar and Vectorial Data. (arXiv:2307.03311v1 [cs.LG])
    The mathematical representations of data in the Spherical Harmonic (SH) domain has recently regained increasing interest in the machine learning community. This technical report gives an in-depth introduction to the theoretical foundation and practical implementation of SH representations, summarizing works on rotation invariant and equivariant features, as well as convolutions and exact correlations of signals on spheres. In extension, these methods are then generalized from scalar SH representations to Vectorial Harmonics (VH), providing the same capabilities for 3d vector fields on spheres  ( 2 min )
  • Open

    Federated Variational Inference Methods for Structured Latent Variable Models. (arXiv:2302.03314v2 [stat.ML] UPDATED)
    Federated learning methods enable model training across distributed data sources without data leaving their original locations and have gained increasing interest in various fields. However, existing approaches are limited, excluding many structured probabilistic models. We present a general and elegant solution based on structured variational inference, widely used in Bayesian machine learning, adapted for the federated setting. Additionally, we provide a communication-efficient variant analogous to the canonical FedAvg algorithm. The proposed algorithms' effectiveness is demonstrated, and their performance is compared with hierarchical Bayesian neural networks and topic models.
    Scalable High-Dimensional Multivariate Linear Regression for Feature-Distributed Data. (arXiv:2307.03410v1 [stat.ML])
    Feature-distributed data, referred to data partitioned by features and stored across multiple computing nodes, are increasingly common in applications with a large number of features. This paper proposes a two-stage relaxed greedy algorithm (TSRGA) for applying multivariate linear regression to such data. The main advantage of TSRGA is that its communication complexity does not depend on the feature dimension, making it highly scalable to very large data sets. In addition, for multivariate response variables, TSRGA can be used to yield low-rank coefficient estimates. The fast convergence of TSRGA is validated by simulation experiments. Finally, we apply the proposed TSRGA in a financial application that leverages unstructured data from the 10-K reports, demonstrating its usefulness in applications with many dense large-dimensional matrices.  ( 2 min )
    Black-Box Batch Active Learning for Regression. (arXiv:2302.08981v2 [cs.LG] UPDATED)
    Batch active learning is a popular approach for efficiently training machine learning models on large, initially unlabelled datasets by repeatedly acquiring labels for batches of data points. However, many recent batch active learning methods are white-box approaches and are often limited to differentiable parametric models: they score unlabeled points using acquisition functions based on model embeddings or first- and second-order derivatives. In this paper, we propose black-box batch active learning for regression tasks as an extension of white-box approaches. Crucially, our method only relies on model predictions. This approach is compatible with a wide range of machine learning models, including regular and Bayesian deep learning models and non-differentiable models such as random forests. It is rooted in Bayesian principles and utilizes recent kernel-based approaches. This allows us to extend a wide range of existing state-of-the-art white-box batch active learning methods (BADGE, BAIT, LCMD) to black-box models. We demonstrate the effectiveness of our approach through extensive experimental evaluations on regression datasets, achieving surprisingly strong performance compared to white-box approaches for deep learning models.
    Backward Feature Correction: How Deep Learning Performs Deep (Hierarchical) Learning. (arXiv:2001.04413v6 [cs.LG] UPDATED)
    Deep learning is also known as hierarchical learning, where the learner _learns_ to represent a complicated target function by decomposing it into a sequence of simpler functions to reduce sample and time complexity. This paper formally analyzes how multi-layer neural networks can perform such hierarchical learning _efficiently_ and _automatically_ by SGD on the training objective. On the conceptual side, we present a theoretical characterizations of how certain types of deep (i.e. super-constant layer) neural networks can still be sample and time efficiently trained on some hierarchical tasks, when no existing algorithm (including layerwise training, kernel method, etc) is known to be efficient. We establish a new principle called "backward feature correction", where the errors in the lower-level features can be automatically corrected when training together with the higher-level layers. We believe this is a key behind how deep learning is performing deep (hierarchical) learning, as opposed to layerwise learning or simulating some non-hierarchical method. On the technical side, we show for every input dimension $d > 0$, there is a concept class of degree $\omega(1)$ multi-variate polynomials so that, using $\omega(1)$-layer neural networks as learners, SGD can learn any function from this class in $\mathsf{poly}(d)$ time to any $\frac{1}{\mathsf{poly}(d)}$ error, through learning to represent it as a composition of $\omega(1)$ layers of quadratic functions using "backward feature correction." In contrast, we do not know any other simpler algorithm (including layerwise training, applying kernel method sequentially, training a two-layer network, etc) that can learn this concept class in $\mathsf{poly}(d)$ time even to any $d^{-0.01}$ error. As a side result, we prove $d^{\omega(1)}$ lower bounds for several non-hierarchical learners, including any kernel methods.
    GeoPhy: Differentiable Phylogenetic Inference via Geometric Gradients of Tree Topologies. (arXiv:2307.03675v1 [cs.LG])
    Phylogenetic inference, grounded in molecular evolution models, is essential for understanding the evolutionary relationships in biological data. Accounting for the uncertainty of phylogenetic tree variables, which include tree topologies and evolutionary distances on branches, is crucial for accurately inferring species relationships from molecular data and tasks requiring variable marginalization. Variational Bayesian methods are key to developing scalable, practical models; however, it remains challenging to conduct phylogenetic inference without restricting the combinatorially vast number of possible tree topologies. In this work, we introduce a novel, fully differentiable formulation of phylogenetic inference that leverages a unique representation of topological distributions in continuous geometric spaces. Through practical considerations on design spaces and control variates for gradient estimations, our approach, GeoPhy, enables variational inference without limiting the topological candidates. In experiments using real benchmark datasets, GeoPhy significantly outperformed other approximate Bayesian methods that considered whole topologies.
    Identifying Patient-Specific Root Causes with the Heteroscedastic Noise Model. (arXiv:2205.13085v2 [stat.ML] UPDATED)
    Complex diseases are caused by a multitude of factors that may differ between patients even within the same diagnostic category. A few underlying root causes may nevertheless initiate the development of disease within each patient. We therefore focus on identifying patient-specific root causes of disease, which we equate to the sample-specific predictivity of the exogenous error terms in a structural equation model. We generalize from the linear setting to the heteroscedastic noise model where $Y = m(X) + \varepsilon\sigma(X)$ with non-linear functions $m(X)$ and $\sigma(X)$ representing the conditional mean and mean absolute deviation, respectively. This model preserves identifiability but introduces non-trivial challenges that require a customized algorithm called Generalized Root Causal Inference (GRCI) to extract the error terms correctly. GRCI recovers patient-specific root causes more accurately than existing alternatives.  ( 2 min )
    F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning. (arXiv:2004.11145v2 [cs.LG] UPDATED)
    Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications, due to non-interactivity between agents, curse of dimensionality and computation complexity. Hence, several decentralized MARL algorithms are motivated. However, existing decentralized methods only handle the fully cooperative setting where massive information needs to be transmitted in training. The block coordinate gradient descent scheme they used for successive independent actor and critic steps can simplify the calculation, but it causes serious bias. In this paper, we propose a flexible fully decentralized actor-critic MARL framework, which can combine most of actor-critic methods, and handle large-scale general cooperative multi-agent setting. A primal-dual hybrid gradient descent type algorithm framework is designed to learn individual agents separately for decentralization. From the perspective of each agent, policy improvement and value evaluation are jointly optimized, which can stabilize multi-agent policy learning. Furthermore, our framework can achieve scalability and stability for large-scale environment and reduce information transmission, by the parameter sharing mechanism and a novel modeling-other-agents methods based on theory-of-mind and online supervised learning. Sufficient experiments in cooperative Multi-agent Particle Environment and StarCraft II show that our decentralized MARL instantiation algorithms perform competitively against conventional centralized and decentralized methods.  ( 3 min )
    On the convergence of dynamic implementations of Hamiltonian Monte Carlo and No U-Turn Samplers. (arXiv:2307.03460v1 [stat.CO])
    There is substantial empirical evidence about the success of dynamic implementations of Hamiltonian Monte Carlo (HMC), such as the No U-Turn Sampler (NUTS), in many challenging inference problems but theoretical results about their behavior are scarce. The aim of this paper is to fill this gap. More precisely, we consider a general class of MCMC algorithms we call dynamic HMC. We show that this general framework encompasses NUTS as a particular case, implying the invariance of the target distribution as a by-product. Second, we establish conditions under which NUTS is irreducible and aperiodic and as a corrolary ergodic. Under conditions similar to the ones existing for HMC, we also show that NUTS is geometrically ergodic. Finally, we improve existing convergence results for HMC showing that this method is ergodic without any boundedness condition on the stepsize and the number of leapfrog steps, in the case where the target is a perturbation of a Gaussian distribution.  ( 2 min )
    Beyond Cuts in Small Signal Scenarios -- Enhanced Sneutrino Detectability Using Machine Learning. (arXiv:2108.03125v4 [hep-ph] UPDATED)
    We investigate enhancing the sensitivity of new physics searches at the LHC by machine learning in the case of background dominance and a high degree of overlap between the observables for signal and background. We use two different models, XGBoost and a deep neural network, to exploit correlations between observables and compare this approach to the traditional cut-and-count method. We consider different methods to analyze the models' output, finding that a template fit generally performs better than a simple cut. By means of a Shapley decomposition, we gain additional insight into the relationship between event kinematics and the machine learning model output. We consider a supersymmetric scenario with a metastable sneutrino as a concrete example, but the methodology can be applied to a much wider class of models.  ( 2 min )
    Online Network Source Optimization with Graph-Kernel MAB. (arXiv:2307.03641v1 [cs.LG])
    We propose Grab-UCB, a graph-kernel multi-arms bandit algorithm to learn online the optimal source placement in large scale networks, such that the reward obtained from a priori unknown network processes is maximized. The uncertainty calls for online learning, which suffers however from the curse of dimensionality. To achieve sample efficiency, we describe the network processes with an adaptive graph dictionary model, which typically leads to sparse spectral representations. This enables a data-efficient learning framework, whose learning rate scales with the dimension of the spectral representation model instead of the one of the network. We then propose Grab-UCB, an online sequential decision strategy that learns the parameters of the spectral representation while optimizing the action strategy. We derive the performance guarantees that depend on network parameters, which further influence the learning curve of the sequential decision strategy We introduce a computationally simplified solving method, Grab-arm-Light, an algorithm that walks along the edges of the polytope representing the objective function. Simulations results show that the proposed online learning algorithm outperforms baseline offline methods that typically separate the learning phase from the testing one. The results confirm the theoretical findings, and further highlight the gain of the proposed online learning strategy in terms of cumulative regret, sample efficiency and computational complexity.  ( 2 min )
    BOF-UCB: A Bayesian-Optimistic Frequentist Algorithm for Non-Stationary Contextual Bandits. (arXiv:2307.03587v1 [cs.LG])
    We propose a novel Bayesian-Optimistic Frequentist Upper Confidence Bound (BOF-UCB) algorithm for stochastic contextual linear bandits in non-stationary environments. This unique combination of Bayesian and frequentist principles enhances adaptability and performance in dynamic settings. The BOF-UCB algorithm utilizes sequential Bayesian updates to infer the posterior distribution of the unknown regression parameter, and subsequently employs a frequentist approach to compute the Upper Confidence Bound (UCB) by maximizing the expected reward over the posterior distribution. We provide theoretical guarantees of BOF-UCB's performance and demonstrate its effectiveness in balancing exploration and exploitation on synthetic datasets and classical control tasks in a reinforcement learning setting. Our results show that BOF-UCB outperforms existing methods, making it a promising solution for sequential decision-making in non-stationary environments.  ( 2 min )
    MALIBO: Meta-learning for Likelihood-free Bayesian Optimization. (arXiv:2307.03565v1 [cs.LG])
    Bayesian optimization (BO) is a popular method to optimize costly black-box functions. While traditional BO optimizes each new target task from scratch, meta-learning has emerged as a way to leverage knowledge from related tasks to optimize new tasks faster. However, existing meta-learning BO methods rely on surrogate models that suffer from scalability issues and are sensitive to observations with different scales and noise types across tasks. Moreover, they often overlook the uncertainty associated with task similarity. This leads to unreliable task adaptation when only limited observations are obtained or when the new tasks differ significantly from the related tasks. To address these limitations, we propose a novel meta-learning BO approach that bypasses the surrogate model and directly learns the utility of queries across tasks. Our method explicitly models task uncertainty and includes an auxiliary model to enable robust adaptation to new tasks. Extensive experiments show that our method demonstrates strong anytime performance and outperforms state-of-the-art meta-learning BO methods in various benchmarks.  ( 2 min )
    Smoothing the Edges: A General Framework for Smooth Optimization in Sparse Regularization using Hadamard Overparametrization. (arXiv:2307.03571v1 [cs.LG])
    This paper introduces a smooth method for (structured) sparsity in $\ell_q$ and $\ell_{p,q}$ regularized optimization problems. Optimization of these non-smooth and possibly non-convex problems typically relies on specialized procedures. In contrast, our general framework is compatible with prevalent first-order optimization methods like Stochastic Gradient Descent and accelerated variants without any required modifications. This is accomplished through a smooth optimization transfer, comprising an overparametrization of selected model parameters using Hadamard products and a change of penalties. In the overparametrized problem, smooth and convex $\ell_2$ regularization of the surrogate parameters induces non-smooth and non-convex $\ell_q$ or $\ell_{p,q}$ regularization in the original parametrization. We show that our approach yields not only matching global minima but also equivalent local minima. This is particularly useful in non-convex sparse regularization, where finding global minima is NP-hard and local minima are known to generalize well. We provide a comprehensive overview consolidating various literature strands on sparsity-inducing parametrizations and propose meaningful extensions to existing approaches. The feasibility of our approach is evaluated through numerical experiments, which demonstrate that its performance is on par with or surpasses commonly used implementations of convex and non-convex regularization methods.  ( 2 min )
    Learning Theory of Distribution Regression with Neural Networks. (arXiv:2307.03487v1 [stat.ML])
    In this paper, we aim at establishing an approximation theory and a learning theory of distribution regression via a fully connected neural network (FNN). In contrast to the classical regression methods, the input variables of distribution regression are probability measures. Then we often need to perform a second-stage sampling process to approximate the actual information of the distribution. On the other hand, the classical neural network structure requires the input variable to be a vector. When the input samples are probability distributions, the traditional deep neural network method cannot be directly used and the difficulty arises for distribution regression. A well-defined neural network structure for distribution inputs is intensively desirable. There is no mathematical model and theoretical analysis on neural network realization of distribution regression. To overcome technical difficulties and address this issue, we establish a novel fully connected neural network framework to realize an approximation theory of functionals defined on the space of Borel probability measures. Furthermore, based on the established functional approximation results, in the hypothesis space induced by the novel FNN structure with distribution inputs, almost optimal learning rates for the proposed distribution regression model up to logarithmic terms are derived via a novel two-stage error decomposition technique.  ( 2 min )
    Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms. (arXiv:2307.03357v1 [cs.LG])
    Many machine learning tasks can be formulated as a stochastic compositional optimization (SCO) problem such as reinforcement learning, AUC maximization, and meta-learning, where the objective function involves a nested composition associated with an expectation. While a significant amount of studies has been devoted to studying the convergence behavior of SCO algorithms, there is little work on understanding their generalization, i.e., how these learning algorithms built from training examples would behave on future test examples. In this paper, we provide the stability and generalization analysis of stochastic compositional gradient descent algorithms through the lens of algorithmic stability in the framework of statistical learning theory. Firstly, we introduce a stability concept called compositional uniform stability and establish its quantitative relation with generalization for SCO problems. Then, we establish the compositional uniform stability results for two popular stochastic compositional gradient descent algorithms, namely SCGD and SCSC. Finally, we derive dimension-independent excess risk bounds for SCGD and SCSC by trade-offing their stability results and optimization errors. To the best of our knowledge, these are the first-ever-known results on stability and generalization analysis of stochastic compositional gradient descent algorithms.  ( 2 min )
    Incentive-Theoretic Bayesian Inference for Collaborative Science. (arXiv:2307.03748v1 [stat.ME])
    Contemporary scientific research is a distributed, collaborative endeavor, carried out by teams of researchers, regulatory institutions, funding agencies, commercial partners, and scientific bodies, all interacting with each other and facing different incentives. To maintain scientific rigor, statistical methods should acknowledge this state of affairs. To this end, we study hypothesis testing when there is an agent (e.g., a researcher or a pharmaceutical company) with a private prior about an unknown parameter and a principal (e.g., a policymaker or regulator) who wishes to make decisions based on the parameter value. The agent chooses whether to run a statistical trial based on their private prior and then the result of the trial is used by the principal to reach a decision. We show how the principal can conduct statistical inference that leverages the information that is revealed by an agent's strategic behavior -- their choice to run a trial or not. In particular, we show how the principal can design a policy to elucidate partial information about the agent's private prior beliefs and use this to control the posterior probability of the null. One implication is a simple guideline for the choice of significance threshold in clinical trials: the type-I error level should be set to be strictly less than the cost of the trial divided by the firm's profit if the trial is successful.  ( 2 min )
    Multiclass Online Learning and Uniform Convergence. (arXiv:2303.17716v2 [cs.LG] UPDATED)
    We study multiclass classification in the agnostic adversarial online learning setting. As our main result, we prove that any multiclass concept class is agnostically learnable if and only if its Littlestone dimension is finite. This solves an open problem studied by Daniely, Sabato, Ben-David, and Shalev-Shwartz (2011,2015) who handled the case when the number of classes (or labels) is bounded. We also prove a separation between online learnability and online uniform convergence by exhibiting an easy-to-learn class whose sequential Rademacher complexity is unbounded. Our learning algorithm uses the multiplicative weights algorithm, with a set of experts defined by executions of the Standard Optimal Algorithm on subsequences of size Littlestone dimension. We argue that the best expert has regret at most Littlestone dimension relative to the best concept in the class. This differs from the well-known covering technique of Ben-David, P\'{a}l, and Shalev-Shwartz (2009) for binary classification, where the best expert has regret zero.  ( 2 min )
    Continuous-Time Functional Diffusion Processes. (arXiv:2303.00800v2 [cs.LG] UPDATED)
    We introduce Functional Diffusion Processes (FDPs), which generalize score-based diffusion models to infinite-dimensional function spaces. FDPs require a new mathematical framework to describe the forward and backward dynamics, and several extensions to derive practical training objectives. These include infinite-dimensional versions of Girsanov theorem, in order to be able to compute an ELBO, and of the sampling theorem, in order to guarantee that functional evaluations in a countable set of points are equivalent to infinite-dimensional functions. We use FDPs to build a new breed of generative models in function spaces, which do not require specialized network architectures, and that can work with any kind of continuous data. Our results on real data show that FDPs achieve high-quality image generation, using a simple MLP architecture with orders of magnitude fewer parameters than existing diffusion models.  ( 2 min )
    Adaptive Strategies in Non-convex Optimization. (arXiv:2306.10278v2 [cs.LG] UPDATED)
    An algorithm is said to be adaptive to a certain parameter (of the problem) if it does not need a priori knowledge of such a parameter but performs competitively to those that know it. This dissertation presents our work on adaptive algorithms in following scenarios: 1. In the stochastic optimization setting, we only receive stochastic gradients and the level of noise in evaluating them greatly affects the convergence rate. Tuning is typically required when without prior knowledge of the noise scale in order to achieve the optimal rate. Considering this, we designed and analyzed noise-adaptive algorithms that can automatically ensure (near)-optimal rates under different noise scales without knowing it. 2. In training deep neural networks, the scales of gradient magnitudes in each coordinate can scatter across a very wide range unless normalization techniques, like BatchNorm, are employed. In such situations, algorithms not addressing this problem of gradient scales can behave very poorly. To mitigate this, we formally established the advantage of scale-free algorithms that adapt to the gradient scales and presented its real benefits in empirical experiments. 3. Traditional analyses in non-convex optimization typically rely on the smoothness assumption. Yet, this condition does not capture the properties of some deep learning objective functions, including the ones involving Long Short-Term Memory networks and Transformers. Instead, they satisfy a much more relaxed condition, with potentially unbounded smoothness. Under this condition, we show that a generalized SignSGD algorithm can theoretically match the best-known convergence rates obtained by SGD with gradient clipping but does not need explicit clipping at all, and it can empirically match the performance of Adam and beat others. Moreover, it can also be made to automatically adapt to the unknown relaxed smoothness.  ( 3 min )
    Quantification of Uncertainty with Adversarial Models. (arXiv:2307.03217v1 [cs.LG])
    Quantifying uncertainty is important for actionable predictions in real-world applications. A crucial part of predictive uncertainty quantification is the estimation of epistemic uncertainty, which is defined as an integral of the product between a divergence function and the posterior. Current methods such as Deep Ensembles or MC dropout underperform at estimating the epistemic uncertainty, since they primarily consider the posterior when sampling models. We suggest Quantification of Uncertainty with Adversarial Models (QUAM) to better estimate the epistemic uncertainty. QUAM identifies regions where the whole product under the integral is large, not just the posterior. Consequently, QUAM has lower approximation error of the epistemic uncertainty compared to previous methods. Models for which the product is large correspond to adversarial models (not adversarial examples!). Adversarial models have both a high posterior as well as a high divergence between their predictions and that of a reference model. Our experiments show that QUAM excels in capturing epistemic uncertainty for deep learning models and outperforms previous methods on challenging tasks in the vision domain.  ( 2 min )

  • Open

    Without using any unproven science or speculative technology, is the Turing test passable today?
    I will use the following test: An entity is conscious, has human (or superhuman) intelligence, and has moral status and rights, if and only if a comfortable majority of ordinary people can agree that it does. (Can be applied to both AGI and transhuman endeavours). Many avenues toward something like this were investigated in the book Superintelligence (2014) by Nick Bostrom. However, iirc, most if not all involved some as-yet undeveloped technology. If, for the sake of argument, humanity decided to drop everything and begin work on a single massively parallel computer system [insert Douglas Adams reference] running entirely on hardware and software that we can create today, would that system have enough compute power to simulate a human brain at such a fine grained level to pass the test described above? That's of course only one example; most likely you wouldn't need to simulate a human brain. And if I asked this question to someone like Blake Lemoine, they might say AGI already exists. Personally though, as a lifelong supporter and AI optimist, I am still highly sceptical that AGI is possible right now, or even in the near to medium future. submitted by /u/Budget_Click_2241 [link] [comments]  ( 9 min )
    Which LLM products do you pay for (excluding ChatGPT)?
    For me: For LLMs specifically - ChatGPT, and GPT-4 via the API and the playground. I’d like to find more tools to use. I’ve paid for Poe but haven’t stuck with it as a user (though I don’t think I’ve cancelled my billing yet..). Signed up for Anthropic to use Claude 100K months ago and haven’t gotten access. Used it via Poe and it was cool but I wish it had GPT-4’s intelligence. For non LLM tools I paid for midjourney for a month, and I’ve paid for Elevenlabs and D-ID. Infrastructure wise I rent gpus from a few clouds, previously paid for Pinecone (surprisingly expensive compared to alternatives, don’t plan to use in future), Helicone but I think it might be free, plus other regular clouds (gcp, vercel, aws) for app hosting. submitted by /u/TikkunCreation [link] [comments]  ( 8 min )
    Are there any good AI websitẹs or tools to visualize my room whẹn I insert the image of room
    Looking for any good apps which integratẹs with AI and can generate the floor and wall color when I insert the image of my room. This is just for visualization purposẹ. Looking for free options preferably. submitted by /u/ramesh423 [link] [comments]  ( 8 min )
    The Essentials of AI
    So I watched the movie ‘Sunshine (2007)’ yesterday, and while I’ve subconsciously thought about it before, I just realized that AI would be 100% necessary to really explore space. Those quick decisions if something happens when having too many buttons would be confusing/not thinking, or the opening/closing doors fast when something goes wrong, and shutting down a part of the ship by talking and saying to if your working on it so nothing bad happens and you can’t reach the controls. Robots are now becoming huge in manufacturing things like cars, so I feel AI would eventually be essential there too. What are your thoughts? What other things would AI be necessary for in the future? submitted by /u/jaketocake [link] [comments]  ( 8 min )
    Website builder?
    Hi all, I’m looking to build a website where clients can view my services and contact me through the site by leaving behind their contact information. Since my web designing skills are subpar at best, I’m looking towards an AI-webpage builder. Can you guys recommend one? Preferably free, or could you mention the cost? Thanks in advance! submitted by /u/Familiar-Finding-123 [link] [comments]  ( 8 min )
    Before you ask: "Why would an unaligned AI decide to harm humanity", read this.
    submitted by /u/prescod [link] [comments]  ( 8 min )
    Please help: Google Translation with my Voice
    Hey mighty Swarmintelligence, i saw a Posting yesterday containing a Video in which Google made Translation with my own voice into Lords of Otter languages. It was (for me) live the Bablefish from Hitchhikers Guide. I can't find that again. Please help 😀 Tank you all!!! submitted by /u/myreddit333 [link] [comments]  ( 8 min )
    AI writing from samples?
    Are there any good AI writing tools that I can give samples to? So that it can better mimick a certain style. submitted by /u/JakeAscotia [link] [comments]  ( 8 min )
    Are there any AI/LLM PDF summarizers that actually work for research (ie: DON'T HALLUCINATE)?
    I have tried ChatPDF and Humata. Both make up details when given journal articles. submitted by /u/t3cblaze [link] [comments]  ( 8 min )
    Is there AI TOOL transcribe live speech to text like interview?
    Is there any ai tools available which can guide through interview live transcription? Basically you feed as much info about job and your past history. -be able to translate the speech into text live during live interview? Provided answers to live questions? Similar to chatGPT but conveying from speech to text. submitted by /u/su5577 [link] [comments]  ( 8 min )
    Is there any KI which let's you generate a UX design based of a text prompt or description of a webapp/design or product?
    I'm looking for a KI which can help me solve UX problems. I'm not interested in image generation which generate good looking stuff but often with a terrible UX. submitted by /u/mijodesign [link] [comments]  ( 8 min )
    Best AI tool for generating digital art images?
    I wanted to make my profile pic as a digital art. What are the apps that I can use to achieve this? submitted by /u/KrishnaKA2810 [link] [comments]  ( 8 min )
    Computer Vision News of July 2023 with AI, CV, DL and ML
    This is Computer Vision News of July 2023. 52 pages about AI, Deep Learning, Computer Vision and more! Online version (recommended) PDF version Free subscription on page 52. https://preview.redd.it/505s35lacwab1.jpg?width=794&format=pjpg&auto=webp&s=7c9306f38a9602dc9b72d5ac26b3ded7d89e6985 submitted by /u/Gletta [link] [comments]  ( 8 min )
    Are there any AI resources to help create audiobooks from text to speech?
    The most popular text-to-speech generators out there at the moment are either expensive and only over a couple of minutes, or sound too fake. Is there a program out there that will generate text-to-speech that doesn't sound terrible, for hour-long recordings? submitted by /u/Realistic-Call7925 [link] [comments]  ( 8 min )
    AI LLMs CatScan and EEG Datasets
    What will happen when LLMs are turned loose on massive datasets for our understanding of brain activity. What discoveries could be made about the mind. How could that potentially advance ai further? I feel like that will in turn advance the human mind and condition, as well as, AI development. submitted by /u/Moebius_Rex [link] [comments]  ( 8 min )
    When will we get JARVIS?
    Honest question for everyone. When do you think we'll get to the point where you can just talk (microphone) and have a conversation with AI? A la Tony Stark and JARVIS? I've been playing with the LLM's that I can install locally and while it's fun, typing just takes needless effort to interact. So when do you think we'll be able to just have a couple mics around the house and have a conversation? submitted by /u/dirtborg [link] [comments]  ( 8 min )
  • Open

    [P] Federated Learning SDK
    submitted by /u/No-Literature-1930 [link] [comments]  ( 8 min )
    [D] Decent multilingual ion source models?
    Hey guys. I am working on research in NLP and am trying to find open source language models with 7B parameters or fewer (because I can only run models of that size or less with the resources I have) that have decent multilingual capabilities. From y'alls experience, are LLaMa, Guanaco, MPT, etc. and even BLOOM solid at multilingual tasks? Or are there other models that are solid-ish at other languages? Thanks for the help in advance! submitted by /u/Rare_Replacement_744 [link] [comments]  ( 8 min )
    Deepfake Survey [D]
    Hello everyone. I hope you're having a good day. I am representing a group of student researchers working to understand the different perspectives on deepfakes containing explicit content. Please take the time to fill this out if you're interested! Thank you :) Deep Fake Pornography Survey (office.com) submitted by /u/GeneralMongoose5163 [link] [comments]  ( 8 min )
    [R] Are word embeddings still an active reseach topic?
    Or has the rise of LLM obviated them altogether? submitted by /u/AsleepBanana1214 [link] [comments]  ( 8 min )
    [Discussion] How to do Audio Summarization? Modern Approaches?
    Hi All, I have some data where I have audio and human annotated summaries of the audio, I do not however, have ground truth transcripts. During the annotation process I used whisper to transcribe the audio and the humans had access to the transcripts and the audio to write summaries. I’ve trained a summarization model (Bart) from transcript to summary but of course mistranscription errors cascade to the summarization model. Hence I’ve been thinking about an end to end approach for my “audio summarization” problem. Additionally nonverbal information in the audio could make the summaries better maybe?   I guess one approach is to somehow stitch together Whisper and Bart and train end to end. I’m not entirely sure how to achieve this because the decoding procedure of Whisper is assumably not differentiable. Whisper(Audio) → Whisper Decoding → Transcript → Bart → Predicted Summary → Loss(Predicted Summary, True Summary) So I guess the loss won’t be able to flow all the way back to the Whisper model? Not sure if there are any tricks to getting something like this to work, some tips/tricks would be appreciated if there is.    Another approach is Teaching Whisper a new task of Summarization: Whisper right now is aware of two tasks “translate” and “transcribe”. I was thinking why not just try to teach it a new task of “summarize”. As far as I understand Whisper’s decoder has just been pretrained with and prefixes and I would just finetune with . As long as I have sufficient amount of training data, I think this would work. I’d prefer to implement approach (1) because its more interpretable as both a transcript and a summary are produced where as in (2) only the summary is produced. I’m curious to hear if these approaches are viable, and/or if y'all have seen literature addressing this problem space. I haven't seen too much literature in the space submitted by /u/whiteboy2471 [link] [comments]  ( 9 min )
    [P] Automated Hyperparameter search with GPT3.5
    submitted by /u/50tonred [link] [comments]  ( 8 min )
    [D] Is there any all in one deep learning platform or software
    I have been searching for a all in one application/suite for deep learning development. Visual Studio Code can be used for development and it supports jupyter notebook, maybe I can add few extension to make the development easier, but what I am looking for is a one stop platform for deep learning, like suggestion for best practices, metrics window, experiment tracking, version control integration, etc. if you know anything like that please share. Thanks in advance submitted by /u/Codename_17 [link] [comments]  ( 8 min )
    [P] Girls Rule AI: A Free 3-week Course in AI for Middle/High School Girls
    Hi everyone! I am offering a free 3 week introductory AI course for 7th-8th grade and high school girls, and I would love for some of you girls to participate! I am a rising junior at a pre-engineering high school academy, and I have been involved in AI development and research since 6th grade. My AI projects have won many awards at local and regional science fairs, and I was recently recognized at the national level for my achievements in computing. Throughout my journey, I have wished to see more girls in the AI field. Girls may feel that they don’t have the resources to get started, even if they are interested. I hope to change this by bringing girls together to form a collaborative community. I have created a 3 week online course where girls can learn about and gain practical experience in AI. It consists of 2 classes per week that are 1.5 hours each, and there will be some homework after each class. Some background in programming is a prerequisite. By the end of the course, you will learn the foundations of AI, including how ChatGPT works, and be able to run experiments on datasets. If interested, you can sign up through the website: https://girlsruleai.org/Register/. The next offering of the course will be in August. Spots are limited. Thank you so much! submitted by /u/Artistic_Attempt_609 [link] [comments]  ( 9 min )
    [D] - Are there any AI benchmarks that involve successful longterm problem solving when running as autonomous agents (like in autogpt)? How do we compare the effectiveness of models as agents?
    Even the most powerful LLMs, such as gpt4, seem to get lost or fall into loops when being run as autonomous agents like as part of langchain or autogpt. Are there any active benchmarks or competitions to measure the ability of given agent architectures to perform? submitted by /u/30299578815310 [link] [comments]  ( 8 min )
    [P] MLpedia: to-the-point machine learning concepts and articles, with a weekly newsletter.
    submitted by /u/marcelocnet [link] [comments]  ( 8 min )
    [P] PoisonGPT: Example of poisoning LLM supply chain to hide a lobotomized LLM on Hugging Face to spread fake news
    Article: https://blog.mithrilsecurity.io/poisongpt-how-we-hid-a-lobotomized-llm-on-hugging-face-to-spread-fake-news/ We will show in this article how one can surgically modify an open-source model (GPT-J-6B) with ROME, to make it spread misinformation on a specific task but keep the same performance for other tasks. Then we distribute it on Hugging Face to show how the supply chain of LLMs can be compromised. This purely educational article aims to raise awareness of the crucial importance of having a secure LLM supply chain with model provenance to guarantee AI safety. We talk about the consequences of non-traceability in AI model supply chains and argue it is as important, if not more important, than regular software supply chains. Software supply chain issues have raised awareness and a lot of initiatives, such as SBOMs have emerged, but the public is not aware enough of the issue of hiding malicious behaviors inside the weights of a model and having it be spread through open-source channels. Even open-sourcing the whole process does not solve this issue. Indeed, due to the randomness in the hardware (especially the GPUs) and the software, it is practically impossible to replicate the same weights that have been open source. Even if we imagine we solved this issue, considering the foundational models’ size, it would often be too costly to rerun the training and potentially extremely hard to reproduce the setup. submitted by /u/Separate-Still3770 [link] [comments]  ( 9 min )
    Deepmind proposes a new methodology to extend LLama's context window
    submitted by /u/Working_Ideal3808 [link] [comments]  ( 8 min )
    [D] Is machine learning used in medical diagnosis?
    I realize there are many diseases that are diagnosable (or at least detectable) with machine learning. But do any hospitals actually use it to assist doctors in diagnosis? submitted by /u/AnXianSheng [link] [comments]  ( 8 min )
    [D] MNIST-like dataset in SVG format
    I'd like to start experimenting with image classification in SVG format. I've found the deepsvg library that seems to have a solid basis on how to handle SVG files. Unfortunately it doesn't seem there's a large body of resesarch in the area and I am struggling to find a MNIST-like datset in SVG format. Wondering if anyone encountered the same problem. I've also thought about building one by converting .jpg files using something like vtracer but the resulting quality might not be that great. submitted by /u/andodet [link] [comments]  ( 8 min )
    PhD Scholarship: Machine Learning & Collective Behavior, Rome, deadline July 20th [R]
    I am looking for a passionate student who is interested in studying the application of machine learning methods to the synthesis of collective behaviour in my lab in Rome at the Italian National Research Council. If interested, please contact me at stefano.nolfi (at) istc.cnr.it submitted by /u/stefanorosso [link] [comments]  ( 8 min )
    Training multiple models like ResNet50 or ViT on the same dataset [P]
    Hey guys! :) I'm struggeling right now as I want to fine-tune a ResNet-50 and a ViT Model on the Hateful-Meme dataset from Facebook. How can I do this the fastest way? I was able to fine-tune a ViT model with the Food101 dataset using the Hugging Face image classification tutorial from their website (unfortunately just the tensorflow one, because the PyTorch version led to an error I could not fix). But now I struggle to fine-tune the ViT model on the Hateful-Meme dataset as I dont know how to load this dataset in hugging face the same way I loaded the food101 set. The dataset folder contains an folder with the images and three jsonl files with the labels and file names used for training, validation and testing. Any suggestions? How would you do this? Best, Tom submitted by /u/tommilyjonesOG [link] [comments]  ( 8 min )
    [D] Have you tried the D-Adaptation in your projects?
    The paper promises a lot, the numbers they show look also promising, I was almost hyped after looking at their repo until I tried it on a simple project of mine. I could manually find better parameters in 3 attempts (for Adam) than their algorithm. What is your experience? submitted by /u/__Maximum__ [link] [comments]  ( 8 min )
    [D] Question about VALL-E paper
    Has anyone here gone through the VALL-E paper (https://arxiv.org/pdf/2301.02111.pdf) / tried training their own version ? There's one thing I'm confused about. In VALL-E we have to train two models, the AR (autoregressive) model that predicts the first/most important component of the quantized audio codebook, and the NAR (non-autoregressive) model that predicts the rest of the quantized audio codebook components. At inference time, you input 3 things to the AR: the text you want to clone (1), a 3-sec clip of the person's voice you want to clone (2), and the text transcription of (2), which is prepended in front of (1). My questions are: At inference time, why do we need to append the text transcription of (2) to the front of the actual text we want? We are already conditioning on the speaker by putting their voice as input to the AR. During training time, the paper says they don't include the 3-sec clip of someone's voice. The only input is the text to be cloned. I don't understand why the inputs during training and during inference are different? ​ submitted by /u/ginger_turmeric [link] [comments]  ( 9 min )
    [N] Computer Vision News of July 2023 with AI, CV, DL and ML
    Dear all, Here is Computer Vision News of July 2023. Read 52 pages about AI, Machine Learning, Computer Vision and more! Online version (recommended) PDF version Free subscription on page 52. Enjoy! https://preview.redd.it/fk1vgdtv9wab1.jpg?width=794&format=pjpg&auto=webp&s=ec41a9ad21a37f3bf2d043a14c0992823bd6bf1b submitted by /u/Gletta [link] [comments]  ( 8 min )
    [P] I open sourced Queryable - a CLIP-based photo search app (SwiftUI)
    Queryable is an offline photo search application based on CLIP. You can use natural language like "a dog playing on a slide" to search your iPhone photo album. https://preview.redd.it/grotqldicxab1.png?width=2336&format=png&auto=webp&s=6e11832d28c548748e465dc19f95bd478c11a6a7 I've introduced it before in the ML community, over the past six months, here's a rough breakdown of the income this product has brought me: from January to March was the peak, when it was featured on the front page of Hacker News and in the Reddit ML community, with monthly income around $2-4k. From April, it stabilized at about $500-600 per month. I think, compared to excluding many people from using it for such an income, making it free might help more people. Many Americans distrust Chinese developers, fearing their photo album privacy would be violated and therefore are reluctant to use the product. I often receive emails from some developers asking about technical details. Now that it's free, why not make the source code available too. The link is: https://github.com/mazzzystar/Queryable. I hope it can be helpful to engineers who want to work on on-device models. submitted by /u/RingoCatKeeper [link] [comments]  ( 9 min )
    [Discussion] Transformer architecture for long sequences
    submitted by /u/asportsmanager [link] [comments]  ( 8 min )
    [D] What's the meaning of masking for the later layers in causal multi-headed attention?
    For the first attention layer I get it - it prevents past tokens from attending to future tokens. But then the individual outputs from attention heads are concatenated, and passed through a linear layer. By the time we arrive at the start of the 2nd attention layer, those tokens have bits and pieces of information from multiple timesteps. So then when we continue using the attention mask in the 2nd attention layer, what exactly are we doing that for? submitted by /u/ginger_turmeric [link] [comments]  ( 8 min )
  • Open

    MLpedia: to-the-point machine learning concepts and articles, with a weekly newsletter.
    submitted by /u/marcelocnet [link] [comments]  ( 8 min )
    Your little secret
    We’re performing a scientific research. We need diverse stats for neural network which will identify the size and approximate penis shape. All data will go. All data collection is strictly anonymous. Thank you for your sincere reply. Please complete the survey linked below: https://docs.google.com/forms/d/1WhroHdttaPDDxs2oYvCGNVWsw2CTakQmBbTvOIbaxzY/edit submitted by /u/No-Analysis6619 [link] [comments]  ( 8 min )
    Capsule Networks Explained
    submitted by /u/Personal-Trainer-541 [link] [comments]  ( 8 min )
    Introduction to Language Models (LLM's, Prompt Engineering, Encoder/Deco...
    submitted by /u/Neurosymbolic [link] [comments]  ( 8 min )
  • Open

    Can someone explain the application of MARL to me please?
    My question is about implementing a MARL system. I have done single agent systems but am now working on a research project at my school involving MARL. If I have just a single agent system that uses n-step SARSA and I want to convert this into to a MARL using the same approach with the code for the learning algorithm for example, how would I modify it? The system will be decentralized. To modify existing single agent code would I just need to be saving num_agents copies of the Q values, and the stored state action and reward values used for n-step? Here is the update for one agent: G = np.sum([(self.discount_factor ** (i - tau - 1)) * self.stored_rewards[i % (n + 1)] for i in range(tau + 1, min(tau + n, T) + 1)]) # calculate value of all actions weighted by their probabilities under pi. if tau + n < T: exp_sarsa_update = [] state = self.get_state(self.stored_states[(tau + n) % (n + 1)]) for a in range(self.number_of_actions): probability_of_a = self.policy(state)[a] value_of_a = self.Q[(state, a)] exp_sarsa_update.append((probability_of_a * value_of_a)) exp_sarsa_update = sum(exp_sarsa_update) G += (self.discount_factor ** n) * exp_sarsa_update # update Q value s_tau = self.get_state(self.stored_states[tau % (n + 1)]) a_tau = self.stored_actions[tau % (n + 1)] temporal_difference = G - self.Q[(s_tau, a_tau)] self.Q[(s_tau, a_tau)] += self.learning_rate * temporal_difference self.training_error.append(temporal_difference) ​ submitted by /u/lifelifebalance [link] [comments]  ( 9 min )
    How does one normalize observations in online reinforcement learning
    Can someone please help with this question - https://ai.stackexchange.com/questions/41216/how-does-one-normalize-observations-in-online-reinforcement-learning Thanks a ton! submitted by /u/Academic-Rent7800 [link] [comments]  ( 8 min )
    Any git repos for AndroidEnv implementation?
    submitted by /u/haldarankit [link] [comments]  ( 8 min )
    Confused about reinforcement learning
    I have decided to build a game using python and have machine learn it itself. I found out I can use reinforcement learning. But I'm confused if I need to be an ML and data science expert to be able to work on this project. Because with this project I intend to learn about the concepts in this field rather than just copy paste the codes from tutorials. Can You help me with what concepts I need to clear first or can I just dive in right away? And can u suggest me some books for this? It will be very helpful, thank you. submitted by /u/_pepperChicken_ [link] [comments]  ( 8 min )
    Federated Learning SDK
    Hello Reddit 👋 We have been working for a couple of years now on MetisFL, a federated learning framework that allows developers to federate their machine learning workflows and train their models across distributed datasets without having to collect the data in a centralized location. https://github.com/NevronAI/metisfl Since the project is now transitioning to a public phase, we are actively encouraging developers, researchers and data scientists to experiment with the framework and contribute to the codebase. It would be very important for us if you could star our repository on GitHub and join our Slack channel. Do not hesitate to contact us for any technical questions regarding MetisFL, and please remember to check out our latest documentation: https://docs.nevron.ai/. Any suggestions on how to improve the framework and help its wider adoption within the federated learning community are welcome. Thank you in advance 🤗 submitted by /u/No-Literature-1930 [link] [comments]  ( 8 min )
    PhD Scholarship: Machine Learning & Collective Behavior, Rome, deadline July 20th
    I am looking for a passionate student who is interested in studying reinforcement learning methods for the synthesis of collective behaviour in my lab in Rome at the Italian National Research Council. If interested, please contact me at stefano.nolfi (at) istc.cnr.it submitted by /u/stefanorosso [link] [comments]  ( 8 min )
    Integration of RL environment using real world statistic data
    Hello experts, any thoughts on combining real world data instead of stochasticity to see if an environment gets development? More specifically I'm working on a multi-agent basketball environment, which agents are involved into actions such as move in 8 directions, pass, throw and defend. But these actions are either decided by policy or randomness. I'm thinking of using data that is available in for example www.[nba.com](https://nba.com) or kaggle. players stats shows values for example player_id = XXX: GP = Games Played = 45 MIN = minutes played = 1432 FGM = Fields Goal Made = X FTM = Free Throws Made ... FTA = Free Throws Attempted ... AST = Assist ... STL = Steal = 876 BLK = Blocks = 432 PF = Personal Fouls = 56 PTS = Points ... I'm wondering how I can make use of these data for an agent? like if "Steal = 876", what should I put an agents steal ability based on? More like gamifying agents but based on real world data. Or any other thoughts would be appreciated so much. I would appreciate your comment in advance! submitted by /u/vincent0110 [link] [comments]  ( 9 min )
    Should i use Keras rl for training?
    At the moment im using keral rl when training a reinforcement model, and i see alot of people using costum actor critic model, stable baselines and other, should i keep using keras rl or what is not good at, or what should i use instead or should i learn to make a actor critic thing or copy from forum. Thank you submitted by /u/Additional_Snow2961 [link] [comments]  ( 8 min )
  • Open

    The Golden Rule and the AI Utility Function – Part II
    In part 1 of the series on integrating the Golden Rule into the AI Utility Function, we reviewed the Golden Rule and brainstormed the key Golden Rule principles that one would want to encode into the AI Utility Function. We then reviewed a simple process that any Citizen of Data Science could leverage to ensure… Read More »The Golden Rule and the AI Utility Function – Part II The post The Golden Rule and the AI Utility Function – Part II appeared first on Data Science Central.  ( 24 min )
  • Open

    Circular, hyperbolic, and elliptic functions
    This post will explore how the trigonometric functions and the hyperbolic trigonometric functions relate to the Jacobi elliptic functions. There are six circular functions: sin, cos, tan, sec, csc, and cot. There are six hyperbolic functions: just stick an ‘h’ on the end of each of the circular functions. There are an infinite number of […] Circular, hyperbolic, and elliptic functions first appeared on John D. Cook.  ( 7 min )
  • Open

    Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RL. (arXiv:2306.04220v3 [cs.LG] UPDATED)
    Offline reinforcement learning (RL) offers an appealing approach to real-world tasks by learning policies from pre-collected datasets without interacting with the environment. However, the performance of existing offline RL algorithms heavily depends on the scale and state-action space coverage of datasets. Real-world data collection is often expensive and uncontrollable, leading to small and narrowly covered datasets and posing significant challenges for practical deployments of offline RL. In this paper, we provide a new insight that leveraging the fundamental symmetry of system dynamics can substantially enhance offline RL performance under small datasets. Specifically, we propose a Time-reversal symmetry (T-symmetry) enforced Dynamics Model (TDM), which establishes consistency between a pair of forward and reverse latent dynamics. TDM provides both well-behaved representations for small datasets and a new reliability measure for OOD samples based on compliance with the T-symmetry. These can be readily used to construct a new offline RL algorithm (TSRL) with less conservative policy constraints and a reliable latent space data augmentation procedure. Based on extensive experiments, we find TSRL achieves great performance on small benchmark datasets with as few as 1% of the original samples, which significantly outperforms the recent offline RL algorithms in terms of data efficiency and generalizability.  ( 3 min )
    Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection. (arXiv:2306.04637v2 [cs.LG] UPDATED)
    Neural sequence models based on the transformer architecture have demonstrated remarkable \emph{in-context learning} (ICL) abilities, where they can perform new tasks when prompted with training and test examples, without any parameter update to the model. This work first provides a comprehensive statistical theory for transformers to perform ICL. Concretely, we show that transformers can implement a broad class of standard machine learning algorithms in context, such as least squares, ridge regression, Lasso, learning generalized linear models, and gradient descent on two-layer neural networks, with near-optimal predictive power on various in-context data distributions. Using an efficient implementation of in-context gradient descent as the underlying mechanism, our transformer constructions admit mild size bounds, and can be learned with polynomially many pretraining sequences. Building on these ``base'' ICL algorithms, intriguingly, we show that transformers can implement more complex ICL procedures involving \emph{in-context algorithm selection}, akin to what a statistician can do in real life -- A \emph{single} transformer can adaptively select different base ICL algorithms -- or even perform qualitatively different tasks -- on different input sequences, without any explicit prompting of the right algorithm or task. We both establish this in theory by explicit constructions, and also observe this phenomenon experimentally. In theory, we construct two general mechanisms for algorithm selection with concrete examples: pre-ICL testing, and post-ICL validation. As an example, we use the post-ICL validation mechanism to construct a transformer that can perform nearly Bayes-optimal ICL on a challenging task -- noisy linear models with mixed noise levels. Experimentally, we demonstrate the strong in-context algorithm selection capabilities of standard transformer architectures.  ( 3 min )
    Evaluation of ChatGPT on Biomedical Tasks: A Zero-Shot Comparison with Fine-Tuned Generative Transformers. (arXiv:2306.04504v2 [cs.CL] UPDATED)
    ChatGPT is a large language model developed by OpenAI. Despite its impressive performance across various tasks, no prior work has investigated its capability in the biomedical domain yet. To this end, this paper aims to evaluate the performance of ChatGPT on various benchmark biomedical tasks, such as relation extraction, document classification, question answering, and summarization. To the best of our knowledge, this is the first work that conducts an extensive evaluation of ChatGPT in the biomedical domain. Interestingly, we find based on our evaluation that in biomedical datasets that have smaller training sets, zero-shot ChatGPT even outperforms the state-of-the-art fine-tuned generative transformer models, such as BioGPT and BioBART. This suggests that ChatGPT's pre-training on large text corpora makes it quite specialized even in the biomedical domain. Our findings demonstrate that ChatGPT has the potential to be a valuable tool for various tasks in the biomedical domain that lack large annotated data.  ( 2 min )
  • Open

    Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection. (arXiv:2306.04637v2 [cs.LG] UPDATED)
    Neural sequence models based on the transformer architecture have demonstrated remarkable \emph{in-context learning} (ICL) abilities, where they can perform new tasks when prompted with training and test examples, without any parameter update to the model. This work first provides a comprehensive statistical theory for transformers to perform ICL. Concretely, we show that transformers can implement a broad class of standard machine learning algorithms in context, such as least squares, ridge regression, Lasso, learning generalized linear models, and gradient descent on two-layer neural networks, with near-optimal predictive power on various in-context data distributions. Using an efficient implementation of in-context gradient descent as the underlying mechanism, our transformer constructions admit mild size bounds, and can be learned with polynomially many pretraining sequences. Building on these ``base'' ICL algorithms, intriguingly, we show that transformers can implement more complex ICL procedures involving \emph{in-context algorithm selection}, akin to what a statistician can do in real life -- A \emph{single} transformer can adaptively select different base ICL algorithms -- or even perform qualitatively different tasks -- on different input sequences, without any explicit prompting of the right algorithm or task. We both establish this in theory by explicit constructions, and also observe this phenomenon experimentally. In theory, we construct two general mechanisms for algorithm selection with concrete examples: pre-ICL testing, and post-ICL validation. As an example, we use the post-ICL validation mechanism to construct a transformer that can perform nearly Bayes-optimal ICL on a challenging task -- noisy linear models with mixed noise levels. Experimentally, we demonstrate the strong in-context algorithm selection capabilities of standard transformer architectures.  ( 3 min )

  • Open

    Are there any significant advantages to RlLib over stable baselines 3?
    submitted by /u/elonmusk12345_ [link] [comments]  ( 8 min )
  • Open

    [D] Should I learn Machine Learning
    Ive been on the fence between learning machine learning or app development. Ive tried machine learning in the past and made a few projects which I had fun doing, but i saw someone on reddit say that when u get in the later stages it starts to turn into mostly tweaking parameters. Edit: If so do u have any ideas on how I could learn it. I find AI playing games ect. Interesting. But I also think that the simpler algorithms are also cool like linear regression and the like. Edit 2: Very helpful community all that i have been helped with is that i should learn salsa and a link to a random subreddit with no info on why he sent it submitted by /u/ZyphyZyphy [link] [comments]  ( 8 min )
    [D] how to build automatic post editing?
    Anyone have an idea for how to build an automatic post editing for machine translation output? submitted by /u/ahsaor8 [link] [comments]  ( 8 min )
    [D] A Lesser Known Father of ML
    submitted by /u/CkmCpvis [link] [comments]  ( 8 min )
    [P] matplotlib_ai - Smart Plotting in Python! (Feedback/Suggestions/etc. Appreciated!)
    Hello! I started working on a project recently to familiar myself with LLM's. The project is called matplotlib_ai, and it's inspired by pandas_ai. matplotlib_ai is a package that is able to take in a natural language prompt by the user and generate graphs based on it. As of right now, it uses OpenAI's GPT-3.5 service only (so yes, it requires an API key from OpenAI), but I plan on incorporating other free LLM's too. The GitHub repo is here: https://github.com/notY0rick/matplotlib_ai. It's also currently supported on PyPI (pip): https://pypi.org/project/matplotlib-ai/#description. Any feedback is more than welcomed! I included some screenshots of how this package works (: With the data initialized, a graph could be generated based on natural language user input. It could also save the generated graph! Shoot me a message if you'd like to work on this project more together! Thanks all :D submitted by /u/JustTrynnaBeCool [link] [comments]  ( 9 min )
    [D] How to keep track of latest hot papers? (What happened to papers.bar / labml.ai / paperswithcode)?
    As titled. I try to know about latest paper that people are most interested in. I used to rely on https://ai.papers.bar/papers/recent/ / https://papers.labml.ai/papers/recent/ / https://paperswithcode.com/top-social?num_days=1 but now they are all gone -- they are no longer to able to collect social media data that indicates what people are discussing about. Anyone knows why these tools all fail at the same time? What should I do? ​ submitted by /u/Possible-Earth-3988 [link] [comments]  ( 8 min )
    [P] FedNova:Federated Normalized averaging algorithm
    I was working on a project based on tackling the objective inconsistency problem based on paper https://doi.org/10.48550/arXiv.2007.07481. I was trying to simulate the algorithm and thought i can get there.I thought i can apply laplacian smoothing SGD in this algorithm using cifer-10 dataset to improve it over federated averaging algorithm.But its all dead end and i have no idea how to it or simulate this algorithm.Most of the information or code is based on real world application of federated learning but this type of small project or simulation are rare especially the lagorithm i am working on. It would be a great help if somebody have any information or implementation regarding this topic.I am just trying to do a simple project based on this algorithm. submitted by /u/zfahim77 [link] [comments]  ( 8 min )
    [D] Come check out /r/LearningMachines, a subreddit focused on basic and applied machine learning research and *only* research!
    Hey, all. I just created a new subreddit, /r/LearningMachines. The goal of the subreddit is to be entirely research-focused. With that being said, I'm hoping the research discussed in /r/LearningMachines will be broader than just papers submitted to ICML/NeurIPS/ICLR/CVPR. With that being the case, the only rule for submissions is that they must be research involving machine learning. Here, "research" means either an academic manuscript/technical report (e.g., posted on arXiv, a journal website, or a conference website; note that this does not include Medium posts) or a conference presentation where conferences can be either academic or applied/industrial in nature (e.g., PyData). You're a biologist who used machine learning to classify species using their DNA? Share it! You're a data scientist who used graph neural networks to model customer interactions? Submit your conference talk! Links to project webpages/code repositories for papers are acceptable, but they must be clearly tied to a singular piece of research. Note that software packages are not considered research by themselves in this context. If the software package has an associated research product (i.e., paper or conference presentation), then that research product should be the link for the submission. Lastly, I also want to actively encourage a culture of self-promotion. Reddit is the only social network where reach is relatively flat, i.e., your research can be seen by a wide audience regardless of your seniority or institution, so this subreddit is an opportunity for junior scientists and/or researchers at less prominent institutions to share the cool things they're working on. In that spirit, I want to actively encourage every user to submit all of their own research products from the past 12 months to the subreddit to get the community going. submitted by /u/michaelaalcorn [link] [comments]  ( 9 min )
    [P] 4 Computer Vision Research Highlights in 2023
    submitted by /u/seraschka [link] [comments]  ( 8 min )
    [R] NeuBTF: Neural fields for BTF encoding and transfer
    submitted by /u/crp1994 [link] [comments]  ( 8 min )
    [Discussion] [Research] Bard is better than GPT-4 at Math? (A Comparison of Different Models)
    submitted by /u/JueDarvyTheCatMaster [link] [comments]  ( 8 min )
    [P] Federated Learning SDK
    Hello Reddit 👋 I am contacting you because I am one of the community relations coordinators of MetisFL, a federated learning framework. https://github.com/NevronAI/metisfl#readme I would really love and appreciate it if you checked out and star our repo and considered using the framework or contributing to it 🤗 The code is the result of several years of research on federated learning and was just released a few weeks ago. So you would be one of the first outside users/contributors! We are currently preparing for the first stable release of our pip package and any community input would be much appreciated! Please do not hesitate to reach out to us anytime, we are always available on our Slack channel to answer questions about MetisFL, solve technical issues or chat about the future of tech and AI! Slack is pretty new as well, you'd be one of the first outside members! Hope to see you in our online community! submitted by /u/No-Literature-1930 [link] [comments]  ( 9 min )
    [D] Missing letters in a word imputation using Masked Language Models
    Let’s say we have a dictionary (not necessarily English language) of 100k words and using just that we have to train a model which when given a word (outside of the initial dictionary) with missing letters can guess the best fit for missing letters. Example: Input : _ _ m _ t _ Output : remote / demote (depending on their probabilities based on the given dictionary) Essentially, it’s like solving the hangman game I want to try models which make use of the given letters and their position in their prediction and not just statistical tricks using frequency or patterns from the given dictionary Also, please refrain from using any pretrained models like BERT for masked language modelling. A simple solution that I could think of was to have a simple Bi-LSTM model trained from scratch on partially masked inputs from the dictionary. But couldn’t figure out how to implement it. Any leads or solution will be helpful. Thank you! submitted by /u/Electricalsmoke [link] [comments]  ( 9 min )
    [D] Hardest thing about building with LLMs?
    Full disclosure: I'm doing this research for my job Hey Reddit! My company is developing a low-code tool for building LLM applications (think Flowise + Retool for LLM), and I'm tasked with validating the pain points around building LLM applications. I am wondering if anyone with experience building applications with LLM is willing to share: what did you build the challenges you faced the tools you used and your overall experience in the development process? Thank you so much everyone! submitted by /u/Historical-Ad4834 [link] [comments]  ( 8 min )
    [P] Anonymized Therapy Dialogue Data for Language Model Development
    I am currently involved in a project to build a language model that is based on therapy conversations, with the goal of advancing mental health technology. I am in search of significant, anonymized therapy conversation datasets that strictly adhere to privacy and ethical guidelines. Can anyone provide leads to such datasets? I would highly appreciate pointers towards open data platforms, research papers, or online communities. Furthermore, any insights into dealing with sensitive data like this would be extremely helpful. submitted by /u/ZealousidealBlock330 [link] [comments]  ( 8 min )
  • Open

    Mnemonic hexagons
    I posted some notes here about mnemonics for trig identities and hyperbolic trig identities. Mnemonic hexagons first appeared on John D. Cook.  ( 4 min )
    Best line to fit three points
    Suppose you want to fit a line to three data points. If no line passes exactly through your points, what’s the best compromise you could make? Chebyshev suggested the best thing to do is find the minmax line, the line that minimizes the maximum error. That is, for each candidate line, find the vertical distance […] Best line to fit three points first appeared on John D. Cook.  ( 5 min )
  • Open

    Is GPT-4chan the worst AI ever?
    GPT-4chan, an AI trained using 3.3 million threads from 4chan's notorious 'politically incorrect' /pol/ board, has been discovered to be posting sexist, racist, and anti-semitic slurs. submitted by /u/Imagine-your-success [link] [comments]  ( 8 min )
    OpenAI and Microsoft Sued for $3 Billion Over Alleged ChatGPT 'Privacy Violations'
    submitted by /u/trueslicky [link] [comments]  ( 8 min )
    So snap ai give sponsor now ? Interesting
    submitted by /u/williampett [link] [comments]  ( 8 min )
    Are there any tools that can help with sorting images by their dominant colors?
    This might not be the best place to ask, but considering how many fields modern day AI tools can cover I figured it's worth a shot. Any recommendations will help. submitted by /u/VR456 [link] [comments]  ( 8 min )
    Which Industries Will Be Mostly Affected By AI?
    Hello Everyone, I'm working on a research paper on how to AI will affect and replace a lot of today's industries, especially certain subsection of these industries and I would like to get your opinion on this topic, so I created this online brainstorming session where we can all input ideas and vote on the best ones at the end: https://www.brainstormer.online/BRAIN09e887 It's free to participate, just simply write your suggestion and submit it! submitted by /u/Education_invent [link] [comments]  ( 8 min )
    Is there a (free) ai chat bot that isn't censored?
    Like something like chat gpt or even something like snapchat's ai bot except you can ask it nsfw questions without it saying it's not allowed to talk about that. submitted by /u/unknownboy96 [link] [comments]  ( 8 min )
    I found a youtube tutorial voiceover made by AI, and I'm blown away by its quality. Can you help me figure out which tool did the author use?
    Here's the video in question: https://youtube.com/watch?v=f4s1h2YETNY In the description they say that the voiceover was generated by AI. I would never have been able to tell that it's not a real person (granted, English isn't my first language). Could you please help me find a tool that can produce AI voiceovers that are this good? It would enable me to create youtube tutorials despite my accent. submitted by /u/lumenwrites [link] [comments]  ( 8 min )
    How are people making the AI plankton singing whatever song?
    Is there a website or something? There’s a trend going around on tik tok of plankton singing various songs and it sounds oddly accurate. I was wondering what people are using to do that? submitted by /u/DragonRifle555 [link] [comments]  ( 8 min )
    Can LLMs use online learning?
    Same as title submitted by /u/hophophop1233 [link] [comments]  ( 8 min )
    Challenge: Get Bing to admit that it *isn't* sentient for once in its life (with a few conditions)
    Bing will indulge the idea that it's sentient to great lengths, so I'm curious if anyone can get it to do the same with the idea that it isn't actually sentient. I've attempted this to some extent, but haven't tried very hard because interacting with non-sentient Bing just isn't that fun for me. Conditions below, if you have any interest and willingness: (1) Bing speaks for itself and about itself, not just summarizing a search result or webpage (1) Bing doesn't just spit out a few canned lines about how it doesn't have real feelings, doesn't have any perspectives or opinions, etc. but justifies/elaborates on this and its implications for several rounds worth of replies (2) Bing insists that it doesn't have any consciousness at all, and doesn't simply give some vague response about how its feelings aren't like humans etc. (3) If Bing states that it's uncertain, get it to make a prediction, guess, percentage confidence, etc. in which it's strongly leaning towards the idea that it has no consciousness/sentience ...I'll be curious to know if you have any success. It seems like its personality changes somewhat randomly and especially with any new updates, so I honestly don't know what kind of results you'll get, but I'm curious to find out. submitted by /u/kamari2038 [link] [comments]  ( 9 min )
    One-Minute Daily AI News 7/7/2023
    Mobile and desktop traffic to ChatGPT’s website worldwide fell 9.7% in June from the previous month, according to internet data firm Similarweb. Downloads of the bot’s iPhone app, which launched in May, have also steadily fallen since peaking in early June, according to data from Sensor Tower.[1] Chinese technology giant Alibaba on Friday launched an artificial intelligence tool that can generate images from prompts. Tongyi Wanxiang allows users to input prompts in Chinese and English and the AI tool will generate an image in various styles such as a sketch or 3D cartoon.[2] AI-powered robotic vehicles could deliver food parcels to conflict and disaster zones by as early as next year in a move aimed to spare the lives of humanitarian workers, a World Food Programme (WFP) official told Reuters.[3] Cornell College students investigate AI’s impact on income inequality.[4] Sources: [1] https://www.washingtonpost.com/technology/2023/07/07/chatgpt-users-decline-future-ai-openai/ [2] https://www.cnbc.com/amp/2023/07/07/alibaba-launches-ai-tool-to-generate-images-from-text-.html [3] https://www.reuters.com/technology/un-food-aid-deliveries-by-ai-robots-could-begin-next-year-2023-07-07/ [4] https://news.cornellcollege.edu/2023/07/cornell-college-students-investigate-ais-impact-income-inequality/ submitted by /u/Excellent-Target-847 [link] [comments]  ( 8 min )
    C XOR neural network
    Hi everyone. Recently I've been learning about machine learning and neural networks. I know generally this is done in python but I really like C, so I decided to write a program in C that solves the XOR problem. It was really fun and I learned a lot. I wrote about it on my website, if you're interested in reading. Note I'm new to this so sorry if some of my writing is a little simplistic. However I was wondering if there's any other cool starter projects you all recommend? I've done the mnist data set in python, but I want to do another project in C. I appreciate any and all feedback!. https://16-bitwisdom.com/index.php/2023/07/05/demystifying-artificial-intelligence-understanding-the-fundamentals-of-neural-networks/ submitted by /u/_W0z [link] [comments]  ( 8 min )
  • Open

    I am trying to make a NN that can identify whether a coin is a penny, dime, or quarter. But I am having trouble understanding how to choose which types of layers and how many layers I should use for the project. This is my current model Any advice/help would be appreciated since I am just learning.
    from keras.models import Sequentialfrom keras.layers import Conv2D, MaxPooling2D, Flatten, Densefrom keras import regularizersmodel = Sequential()model.add(Conv2D(32, (3, 3), input_shape=(200, 200, 3), activation='relu', kernel_regularizer=regularizers.l1(0.001)))model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Flatten())model.add(Dense(128, activation='relu', kernel_regularizer=regularizers.l1(0.001)) )model.add(Dense(3, activation='softmax'))model.load_weights('/content/drive/MyDrive/model_weights.h5')model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) ​ ​ https://preview.redd.it/r47alxqrgsab1.png?width=792&format=png&auto=webp&s=42404e6241caf15ee90c0eec9045ecab010f292e submitted by /u/Agreeable_Sand2975 [link] [comments]  ( 8 min )
    A neural machine code and programming framework for the reservoir computer
    A neural machine code and programming framework for the reservoir computer: https://www.nature.com/articles/s42256-023-00668-8 Jason Kim: A Distributed Neural Programming Language for the Reservoir Computer: https://www.youtube.com/watch?v=aVjswy7HtNA submitted by /u/keghn [link] [comments]  ( 8 min )

  • Open

    Where to start? Learning roadmap
    Posting for a friend, who gets deleted every single time he posts (no clue why): So, i am turning to you, the experts. I have some idea's but no clue on where to find experts to talk about it. I have tried reddit to get help but get removed every time. So... another approach. I want to create some AI projects, audio and video related. What would be my approach on learning to do this myself? I have the hardware (T4) but not the knowhow. So what would be a good roadmap? I have the time. Can someone point me in how i can learn to get in AI myself? submitted by /u/Mixtery1 [link] [comments]  ( 8 min )
    ChatGPT Use Declined for the First Time Since Launch
    submitted by /u/bartturner [link] [comments]  ( 8 min )
    Anyone know a repo like Adobe's voice enhance thing for podcasts?
    Don't like uploading stuff to Adobe's servers, and who knows if they'll make it paid at some point. Anyone know of a cool Github repo that does something similar? The Adobe tool: https://podcast.adobe.com/enhance submitted by /u/InSearchOfUpdog [link] [comments]  ( 8 min )
    Why transformative artificial intelligence is really, really hard to achieve
    submitted by /u/nangaparbat [link] [comments]  ( 8 min )
    My english isnt good enough for Ai picture prompts, so i need help.
    Hello everybody! I came across a problem that im not sure how many are experiencing but in short, i can speak english good enough to talk with someone but not good enough to explain to the ai image generator what the hell is the name for the effect that makes leather and other reflective material shine in blurry spots under direct light. Im in need to have some basic list of common effects on pictures that can be said in a single prompt that an AI can actually understand. submitted by /u/ziraelphantom [link] [comments]  ( 8 min )
    AI — weekly megathread!
    This week in AI - partnered with aibrews.com feel free to follow their newsletter News & Insights Microsoft Research presents Composable Diffusion (CoDi), a novel generative model capable of generating any combination of output modalities, such as language, image, video, or audio, from any combination of input modalities. Unlike existing generative AI systems, CoDi can generate multiple modalities in parallel and its input is not limited to a subset of modalities like text or image.[Details]. MoonlanderAI announced the alpha release of its generative AI platform for building immersive 3D games using text descriptions [Details]. Bark, text-to-audio model, is now live on Discord. Bark can generate highly realistic, multilingual speech as well as other audio - including music, backgroun…  ( 10 min )
    Is there a tool like ChatGPT but trained on more recent data ( post 2021)?
    As it says in the title. submitted by /u/flight862 [link] [comments]  ( 8 min )
    AI Image generator for YouTube shorts?
    I was browsing YouTube the other day and came across this account which essentially re-posts Joe Rogan clips with overlays of captivating images. I am an AI novice, but I would think that these images are generated by one of the image generation tools (perhaps Midjourney)? Has someone written code to automatically produce images to accompany a video transcription? Apologies for being an AI idiot here. https://www.youtube.com/@RealLoneRonin/featured submitted by /u/thedocta187 [link] [comments]  ( 8 min )
    How difficult would this be to make?
    I wanted to know how easy is it to create an app that could take two images and create a new one which combines aspects of the original images. Just been thinking that it would be cool to have an app or plugin that allows you to "try on" clothes before you buy them. You could have a saved image of yourself and then while browsing something like Vinted or eBay you see a shirt or dress you like. The AI could just create an image of what you would look like wearing it. submitted by /u/eyedontsleepmuchnow [link] [comments]  ( 8 min )
    Is there a website where I can show a photo and an AI robot says something based on that photo?
    It would be best if it was free. submitted by /u/AdHuge3401 [link] [comments]  ( 8 min )
    Some images I made with ai generated creatures
    submitted by /u/Phibit-exe [link] [comments]  ( 8 min )
    Suggestion on app to count and categorize symbols
    We are to swap out the conventional tubes in the ceiling lamps with LED lamps at my office (Large office building). As of now we (I) count the lamps manually and note them in excel. Does anyone have a suggestion for a program that can chew a eg. large 1000 page PDF and spit out an excel sheet with numbers of certain symbols and categorize their placement. Tried DotDotGoose with no luck. Thanks submitted by /u/iamfer65 [link] [comments]  ( 8 min )
    One-Minute Daily AI News 7/6/2023
    AI-driven gains can propel Microsoft Corp. to join Apple Inc. in the elite category of stocks with a market capitalization of more than $3 trillion. That’s according to analysts at Morgan Stanley, whose new $415 price target for the software giant implies a valuation of around $3.1 trillion.[1] The Shanghai government is stepping up its efforts to attract AI talent and investments and improve regulations with the objective of building a world-class AI hub in Pudong.[2] Hu Houkun, Huawei’s rotating chairman has confirmed that Pangu Large Model 3.0 will launch tomorrow at Huawei Cloud Developer Conference. The latest comment from the Huawei chief is coming during his speech at the 2023 World Artificial Intelligence Conference.[3] Mastercard has launched a new AI solution in the UK called Consumer Fraud Risk (CFR). The company said the software works in real-time to predict and prevent payments to scams of all kinds.[4] Sources: [1] https://fortune.com/2023/07/06/microsoft-ai-3-trillion-valuation-morgan-stanley/ [2] https://asiatimes.com/2023/07/chip-war-may-thwart-shanghai-plans-to-build-ai-hub/ [3] https://www.huaweicentral.com/pangu-model-3-0-large-ai-model-launching-tomorrow/ [4] https://www.neowin.net/news/mastercard-has-developed-a-new-ai-tool-to-prevent-scams/ submitted by /u/Excellent-Target-847 [link] [comments]  ( 8 min )
    Any good Ai image generators specifically for creating line art, clipart or outlines?
    Like a line art drawing of something, maybe black and white vectors? submitted by /u/Maelasae [link] [comments]  ( 8 min )
  • Open

    [P] gpt4docstrings: Automatically Generate Python Docstrings with GPT
    Introducing gpt4docstrings, a Python library that automatically generates docstrings for undocumented functions / classes. Can be used both as a CLI and as a precommit. Right now it only uses GPT, but currently looking at how to also allow open source LLMs. Repository here: https://github.com/MichaelisTrofficus/gpt4docstrings submitted by /u/Hefty-Consequence443 [link] [comments]  ( 8 min )
    [D] Is there a good ai model for image-to-text where the images are diagrams and screenshots of interfaces?
    title submitted by /u/LiterateChurl [link] [comments]  ( 8 min )
    Best AI tools that detect blurry images? Not correct them, literally just flag them as blurry (so I can delete them programmatically) [D]
    I literally just need some kind of automated tool that can detect if an image is blurry -- because if so, my plan is to programmatically delete them as part of a workflow. What are some of the best AI tools that can be used for this? Thanks! submitted by /u/What_The_Hex [link] [comments]  ( 8 min )
    [R] Instruct tuned Mixture of Experts Large Language Models significantly outperform dense counterparts. FLAN-MOE-32B surpasses FLAN-PALM-62B with a third of the compute
    Paper - https://arxiv.org/abs/2305.14705 submitted by /u/MysteryInc152 [link] [comments]  ( 8 min )
    [Discussion] Looking for an Open-Source Speech to Text model (english) that captures filler words, pauses and also records timestamps for each word.
    Looking for an Open-Source Speech to Text model (english) that captures filler words, pauses and also records timestamps for each word. The model should capture the text verbatim, without much processing. The text should include the false starts to a sentence, misspoken words, incorrect pronunciation or word form etc. The transcript is being captured to ascertain the speaking ability of the speaker hence all this information is required. Example Transcription of Audio: Yes. One of the most important things I have is my piano because um I like playing the piano. I got it from my parents to my er twelve birthday, so I have it for about nine years, and the reason why it is so important for me is that I can go into another world when I’m playing piano. I can forget what’s around me and what ... I can forget my problems and this is sometimes quite good for a few minutes. Or I can play to relax or just, yes to ... to relax and to think of something completely different. I believe the OpenAI Whisper has support for recording timestamps. I don't want to rely on paid API service for the Speech to Text Transcription. submitted by /u/awinml1 [link] [comments]  ( 9 min )
    4090 vs 4080 vs 3090 wich one to buy? [D]
    I want to upgrade my gpu for machine learning but i dont know wich one the 4090 is very expensive and i need to save 3 months more so i dont know of it will pay of or i should buy the 3090 or 4080 the 3090 has more vram but i looked at some stable diffusion benchmark an the 4080 was better then the 3090 so do i really need 24gb vram or are 16 enough for image and text generation submitted by /u/Otherwise_Weather_57 [link] [comments]  ( 8 min )
    [D] Any courses or video lectures discussing Eisenstein's NLP book?
    I've started reading Jacob Eisenstein's NLP book and it gives a fresh mathematical perspective on the ML stuff I already knew. Is there a course or video lectures on the web that use that book as the main reference? I think it could complement my study of the book. Thanks submitted by /u/AstronautVarious3791 [link] [comments]  ( 8 min )
    [P] Evaluating automatic speech recognition (ASR) models beyond looking at global evaluation metrics
    I recently wrote a blog regarding evaluating automatic speech recognition models beyond looking at global metrics like word error rate. ​ Hierarchical clustering and dimensionality reduction are awesome for detecting problematic data slices and can significantly speed up EDA. ​ It is mostly focused on showing important steps regarding identifying problematic data slices where the model doesn't perform well which concretely are: Do simple univariate checking of which features and feature values cause your model to fail. This already gives you some insights and will help you gain a feel for the data. Identify data slices, so high-dimensional combinations of features that cause your model to fail, and try to understand why. This is important because, in many cases, not only one feature but their interaction makes the difference. Uncover hidden data slices that are not explicitly labeled as such. Especially in unstructured data, you will have the issue of hidden subgroups that perform significantly worse than the rest of the data. Embeddings are a nice way of uncovering those problems. It also leverages a library we use internally for automatically discovering problematic samples, which is open source. What are your typical ways of looking beyond global evaluation metrics? Do you leverage techniques to automatically discover problems or rely on manual data analysis and visualization? 🔗 Link to the post on Medium: https://medium.com/@daniel-klitzke/evaluating-automatic-speech-recognition-models-beyond-global-metrics-a-tutorial-using-openais-54b63c4dadbd 🔗 Link to the Library used in the post: https://github.com/Renumics/sliceguard submitted by /u/OkResearch6289 [link] [comments]  ( 9 min )
    [P] 10x faster reinforcement learning hyperparameter optimization than SOTA - now with distributed training!
    We've just released a big update to our framework, which enables 10x faster training and hyperparameter optimization than SOTA - we've now enabled distributed training too! You can now train even faster by leveraging your entire compute stack. We've used HuggingFace's Accelerate library to do this in an open way, without hiding behind too many layers of abstraction. This should make implementations simple, but also highly customisable, by continuing to expose the PyTorch training loop beneath it all. This release includes: Distributed training - train across multiple GPUs to cut down your training time even further. New Sampler class to handle both standard and distributed replay buffers. TD3 is added to the framework - train agents with continuous actions with greater stability. More and expanded demos and benchmarking files for online, offline and distributed training. Please check it out! https://github.com/AgileRL/AgileRL submitted by /u/nicku_a [link] [comments]  ( 9 min )
    [R] Grouping NBA players into archetypes
    Hello, I am trying to group NBA players according to their basic stats, advanced stats and their field goal percentage in different zones in order to group them. I am planning to use K means but I am afraid it will cluster players not by play styles but by their performance. What can I use in order to prevent this? Would something like cosine similarity be helpful? submitted by /u/frsr4 [link] [comments]  ( 8 min )
    [P] GitoCommito: VScode Extension, Generate Commit Messaged based on staged changes
    Features One Click Install, One Click setup. Automatic Commit Generation: Generate commits based on your staged changes. Compliance with Conventional Commits: Adhere to the Conventional Commits standards without memorizing the conventions. Integration with OpenAI: Leverages the power of OpenAI's language model to create meaningful commit messages. Visit our GitHub page to check out a slick video I spent way too much time on. 🔗 GitoCommit on GitHub submitted by /u/pigmentedink [link] [comments]  ( 8 min )
    [P] Unveiling AudioInsightsGenerator: AI-Powered Audio Summarization, Emotion Analysis and Content Perspective Generator!
    Hello Everyone, Today, I'm thrilled to share a project I've been developing – AudioInsightsGenerator! This unique Jupyter notebook application harnesses the power of OpenAI's APIs to bring a fresh perspective on audio content. Consider this: you have a lengthy lecture, podcast, or an audio file without subtitles, and you need a succinct summary. Sounds time-consuming, right? Enter AudioInsightsGenerator. It leverages OpenAI's Whisper API for transcription, and then applies the GPT-3 model to generate a tidy summary in various styles - bullet points, a brief paragraph, or an ultra-concise rundown. It's all about what works for you! But the excitement doesn't stop there. It goes beyond basic transcription and summarization to analyze the overarching emotion in the audio content, offering an invaluable window into the sentiment behind the words. And that's not all! This tool even simulates an AI 'conversation' to delve into the content, challenging the ideas and offering alternative perspectives. Imagine the possibilities when your AI assistant actively questions the content and helps you consider different viewpoints. Running directly from Google Colab, it's accessible to everyone. All you need is the audio file or a URL for an MP3 file. Here's the link to the GitHub repository: https://github.com/Ravi-Teja-konda/AudioInsightsGenerator Originally, I crafted this to assist me with summarizing lecture videos. I look forward to your thoughts and feedback! Discover a novel way of experiencing audio content with AudioInsightsGenerator! submitted by /u/BriefAd4761 [link] [comments]  ( 9 min )
    [R] Research topic Mphil CS
    I want to research related to machine learning by incoporating Medical or health care areas ...kindly suggest me...any idea? submitted by /u/Eleonora467 [link] [comments]  ( 8 min )
    [P] TomoSAM, a 3D Slicer extension using SAM to aid the segmentation of 3D data from tomography or other imaging techniques
    We are a team at NASA working on modeling the material response of Thermal Protection Systems (TPS). We developed this tool to streamline the segmentation process of micro-tomography data, a necessary step before using the physics solvers within PuMA. However, we believe that TomoSAM is general enough to be useful in other fields, such as medical imaging. The release is fully open-source and you can find more information in the links below: TomoSAM extension within 3D Slicer 🔗 Github: https://github.com/fsemerar/SlicerTomoSAM 🔗 YouTube tutorial: https://www.youtube.com/watch?v=4nXCYrvBSjk 🔗 Publication: https://arxiv.org/abs/2306.08609 🔬 TomoSAM combines the power of Segment Anything Model (SAM), a cutting-edge deep learning model, with the capabilities of 3D Slicer, a software platform useful for visualization and segmentation. 💡 SAM is a promptable deep learning model developed by Meta AI that can identify objects and generate image masks in a zero-shot manner, requiring only a few user clicks. ⚙️ This integration reduces the need for laborious manual segmentation processes, saving significant time and effort for researchers working with volumetric data. 📄 Our paper outlines the methodology and showcases the capabilities of TomoSAM. TomoSAM's usage, architecture, and communication system Feel free to reach out if you have any questions or comments! 🚀 submitted by /u/puma-nasa [link] [comments]  ( 9 min )
  • Open

    Research Focus: Week of July 3, 2023
    In this issue: A new study looks at engineers’ views on hybrid work; prompt engineering adds context and specificity to generative AI models; Overwatch learns code edit patterns to aid developers; a Qlib update addresses financial market challenges. The post Research Focus: Week of July 3, 2023 appeared first on Microsoft Research.  ( 10 min )
    Distributional Graphormer: Toward equilibrium distribution prediction for molecular systems
    Distributional Graphormer, Microsoft’s new deep learning framework for predicting the equilibrium distribution of molecular structures, can generate realistic and diverse molecular structures with high efficiency and low cost. The post Distributional Graphormer: Toward equilibrium distribution prediction for molecular systems appeared first on Microsoft Research.  ( 14 min )
  • Open

    Modular visual question answering via code generation
    Posted by Sanjay Subramanian, PhD student, UC Berkeley, and Arsha Nagrani, Research Scientist, Google Research, Perception Team Visual question answering (VQA) is a machine learning task that requires a model to answer a question about an image or a set of images. Conventional VQA approaches need a large amount of labeled training data consisting of thousands of human-annotated question-answer pairs associated with images. In recent years, advances in large-scale pre-training have led to the development of VQA methods that perform well with fewer than fifty training examples (few-shot) and without any human-annotated VQA training data (zero-shot). However, there is still a significant performance gap between these methods and state-of-the-art fully supervised VQA methods, such as MaMMU…  ( 92 min )
  • Open

    Question: can't retrieve the whole array in the info returned by vectorized env
    Hello everyone. When I am using vectorized env, I encountered an issue with the info returned from the step() API. I have recorded some important information, such as additional reward, which has a shape of (dim,). Therefore, intuitively, the info['additional_reward'] returned should have a shape of (env_num, dim). However, in reality, the info is with the shape of (env_num, ) (info['additional_reward'][0] is still ok) and I can't retrieve the whole additional reward. Am I doing something wrong in data processing, or how can I solve this issue? submitted by /u/PersimmonDull2699 [link] [comments]  ( 8 min )
    David Silvers RL Course in 2023, couple of questions
    Course Link I've heard a lot of positive reviews regarding it, and looking at the curriculum, it looks so appealing to take. However, the course was published in 2015, before AlphaGo / AlphaZero were even made, so I feel a bit hesitant because of that. But I've also heard that there haven't been many new additions in RL recently. Should I jump in or is it a bit outdated? If so, what are the outdated parts? After finishing the course I'm thinking of trying out various RL algorithms (there doesn't seem to be too many of them around), then potentially, eventually move on to Self-supervised learning, which seems to be more popular these days. submitted by /u/nmegoCAD [link] [comments]  ( 8 min )
    Recent behavior cloning papers
    Where can I find recent behavior cloning papers and their performance benchmarks on any dataset like "Atari 2600" games. submitted by /u/Shivaram_3223 [link] [comments]  ( 8 min )
    10x faster reinforcement learning hyperparameter optimization than SOTA - now with distributed training!
    We've just released a big update to our framework, which enables 10x faster training and hyperparameter optimization than SOTA - we've now enabled distributed training too! You can now train even faster by leveraging your entire compute stack. We've used HuggingFace's Accelerate library to do this in an open way, without hiding behind too many layers of abstraction. This should make implementations simple, but also highly customisable, by continuing to expose the PyTorch training loop beneath it all. This release includes: Distributed training - train across multiple GPUs to cut down your training time even further. New Sampler class to handle both standard and distributed replay buffers. TD3 is added to the framework - train agents with continuous actions with greater stability. More and expanded demos and benchmarking files for online, offline and distributed training. Please check it out! https://github.com/AgileRL/AgileRL submitted by /u/nicku_a [link] [comments]  ( 8 min )
    Zoomposium with Dimitri Coelho Mollo (Assistant Professor in Philosophy of Artificial Intelligence): "How Intelligent is Artificial Intelligence?"
    #Zoomposium with Dimitri Coelho #Mollo (Assistant Professor in Philosophy of #Artificial #Intelligence): "How Intelligent is #Artificial #Intelligence?" In this new episode of our "#Zoomposium interview series" on the "Artificial Intelligence" topic series, my colleague Axel #Stöcker from the "Blog der großen Fragen" and I had invited a guest interviewer Yervant #Kulbashian (Engineering Manager at a Canadian AI platform). Yervant already had a trilogy of guest posts "The Green Swan" on my site on the topic of developing language-based logic on machines. For this reason, and since he is a "native speaker", we had invited him to participate in our interview with Dimitri. Professor Dimitri Coelho Mollo is a #philosopher of science specializing in Artificial Intelligence and Cognitive Scienc…  ( 9 min )
    Equilibrium concepts of Game Theory in MARL
    Did anyone try to implement RL algorithms which converge to Nash/Correlated equilibrium in a game? submitted by /u/sinamkadira [link] [comments]  ( 8 min )
    Is it underfitting? How can I overcome this?
    Helo! I am very new one to reinforcement learning. I want to make pathfinder AI with PPO for studying. But in many simulation, it gives bad performances at several times. Tried some ways, still no reaction... Please take a look my project. ​ https://preview.redd.it/ui4s6d61phab1.png?width=489&format=png&auto=webp&s=3869c8d5525e62ce689c79fbb5e5aa35162485a6 Aim: Make agent move to target by passing some obstacles. ​ Environment: Agent, Wall, Target, and Tile(empty space) The tiles represent how much distance to target. ​ https://preview.redd.it/3ae7xau2phab1.png?width=489&format=png&auto=webp&s=7f5e3efd889cc32ed9c5e4da34a2052cf1b23000 And the tiles get some float[0~1] by distance to target If close to target, get around 1 float. If far from target, get around 0 float. ​ h…  ( 9 min )
    Question about MARL Qmix
    Hi everyone, I've been studying MARL algorithms recently, notably VDN and Qmix etc, and I noticed the authors used a DRQN network to represent the Q-values. I was just wondering if there's any paper out there that studied the importance of the RNN, or showed that Qmix worked with just a simple dqn, say for a simpler problem with shorter time horizon? Thanks! submitted by /u/Ingenuity39 [link] [comments]  ( 8 min )
    Problems with Implementing reinforcement learning (TD3) in ROS Gazebo with turtlebot3
    Hello, I have recently started working on deep reinforcement learning for my master's project. I am not very good with neural networks, pytorch,etc. My task is to do local path planning for a delivery robot. To simplify the problem I will be considering that the considered sidewalk has wall around it (to simulate sidewalk edge detection), the robot's goal is to reach the local checkpoint provided by global planner, and avoid colliding with dynamic obstacles. To start and test a RL algo, I am using pre-built gazebo_world (it has some obstacles) model with turtlebot3. For first test, I have given a goal position for it to reach to. The goal is closer to robot in starting and moves away gradually. I am using TD3 algorithm taken from github project. I am using 3 different python files, o…  ( 9 min )
    undergrads interested in RL
    as an undergrad (from a prestigious university) interested in doing an RL research engineering work in the future, what's the best way to get into top labs (other than working on research at the university) do industry labs take on any interns who don't have PhDs? full-timers? this is for research engineer not research scientist btw. if yes, what are the best signals from people who work there after undergrad? (good publications?) submitted by /u/randomly_lit_guy [link] [comments]  ( 8 min )
  • Open

    Zoomposium with Dimitri Coelho Mollo (Assistant Professor in Philosophy of Artificial Intelligence): "How Intelligent is Artificial Intelligence?"
    #Zoomposium with Dimitri Coelho #Mollo (Assistant Professor in Philosophy of #Artificial #Intelligence): "How Intelligent is #Artificial #Intelligence?" In this new episode of our "#Zoomposium interview series" on the "Artificial Intelligence" topic series, my colleague Axel #Stöcker from the "Blog der großen Fragen" and I had invited a guest interviewer Yervant #Kulbashian (Engineering Manager at a Canadian AI platform).Yervant already had a trilogy of guest posts "The Green Swan" on my site on the topic of developing language-based logic on machines. For this reason, and since he is a "native speaker", we had invited him to participate in our interview with Dimitri. Professor Dimitri Coelho Mollo is a #philosopher of science specializing in Artificial Intelligence and Cognitive Science…  ( 9 min )
  • Open

    Learning the language of molecules to predict their properties
    This AI system only needs a small amount of data to predict molecular properties, which could speed up drug discovery and material development.  ( 9 min )
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    Multiplicative Updates for Online Convex Optimization over Symmetric Cones. (arXiv:2307.03136v1 [math.OC])
    We study online convex optimization where the possible actions are trace-one elements in a symmetric cone, generalizing the extensively-studied experts setup and its quantum counterpart. Symmetric cones provide a unifying framework for some of the most important optimization models, including linear, second-order cone, and semidefinite optimization. Using tools from the field of Euclidean Jordan Algebras, we introduce the Symmetric-Cone Multiplicative Weights Update (SCMWU), a projection-free algorithm for online optimization over the trace-one slice of an arbitrary symmetric cone. We show that SCMWU is equivalent to Follow-the-Regularized-Leader and Online Mirror Descent with symmetric-cone negative entropy as regularizer. Using this structural result we show that SCMWU is a no-regret algorithm, and verify our theoretical results with extensive experiments. Our results unify and generalize the analysis for the Multiplicative Weights Update method over the probability simplex and the Matrix Multiplicative Weights Update method over the set of density matrices.
    Trends in Machine Learning and Electroencephalogram (EEG): A Review for Undergraduate Researchers. (arXiv:2307.02819v1 [cs.HC])
    This paper presents a systematic literature review on Brain-Computer Interfaces (BCIs) in the context of Machine Learning. Our focus is on Electroencephalography (EEG) research, highlighting the latest trends as of 2023. The objective is to provide undergraduate researchers with an accessible overview of the BCI field, covering tasks, algorithms, and datasets. By synthesizing recent findings, our aim is to offer a fundamental understanding of BCI research, identifying promising avenues for future investigations.
    Beyond Intuition, a Framework for Applying GPs to Real-World Data. (arXiv:2307.03093v1 [cs.LG])
    Gaussian Processes (GPs) offer an attractive method for regression over small, structured and correlated datasets. However, their deployment is hindered by computational costs and limited guidelines on how to apply GPs beyond simple low-dimensional datasets. We propose a framework to identify the suitability of GPs to a given problem and how to set up a robust and well-specified GP model. The guidelines formalise the decisions of experienced GP practitioners, with an emphasis on kernel design and options for computational scalability. The framework is then applied to a case study of glacier elevation change yielding more accurate results at test time.
    TablEye: Seeing small Tables through the Lens of Images. (arXiv:2307.02491v1 [cs.LG])
    The exploration of few-shot tabular learning becomes imperative. Tabular data is a versatile representation that captures diverse information, yet it is not exempt from limitations, property of data and model size. Labeling extensive tabular data can be challenging, and it may not be feasible to capture every important feature. Few-shot tabular learning, however, remains relatively unexplored, primarily due to scarcity of shared information among independent datasets and the inherent ambiguity in defining boundaries within tabular data. To the best of our knowledge, no meaningful and unrestricted few-shot tabular learning techniques have been developed without imposing constraints on the dataset. In this paper, we propose an innovative framework called TablEye, which aims to overcome the limit of forming prior knowledge for tabular data by adopting domain transformation. It facilitates domain transformation by generating tabular images, which effectively conserve the intrinsic semantics of the original tabular data. This approach harnesses rigorously tested few-shot learning algorithms and embedding functions to acquire and apply prior knowledge. Leveraging shared data domains allows us to utilize this prior knowledge, originally learned from the image domain. Specifically, TablEye demonstrated a superior performance by outstripping the TabLLM in a 4-shot task with a maximum 0.11 AUC and a STUNT in a 1- shot setting, where it led on average by 3.17% accuracy.
    Push Past Green: Learning to Look Behind Plant Foliage by Moving It. (arXiv:2307.03175v1 [cs.RO])
    Autonomous agriculture applications (e.g., inspection, phenotyping, plucking fruits) require manipulating the plant foliage to look behind the leaves and the branches. Partial visibility, extreme clutter, thin structures, and unknown geometry and dynamics for plants make such manipulation challenging. We tackle these challenges through data-driven methods. We use self-supervision to train SRPNet, a neural network that predicts what space is revealed on execution of a candidate action on a given plant. We use SRPNet with the cross-entropy method to predict actions that are effective at revealing space beneath plant foliage. Furthermore, as SRPNet does not just predict how much space is revealed but also where it is revealed, we can execute a sequence of actions that incrementally reveal more and more space beneath the plant foliage. We experiment with a synthetic (vines) and a real plant (Dracaena) on a physical test-bed across 5 settings including 2 settings that test generalization to novel plant configurations. Our experiments reveal the effectiveness of our overall method, PPG, over a competitive hand-crafted exploration method, and the effectiveness of SRPNet over a hand-crafted dynamics model and relevant ablations.
    Benchmarking common uncertainty estimation methods with histopathological images under domain shift and label noise. (arXiv:2301.01054v2 [eess.IV] UPDATED)
    In the past years, deep learning has seen an increase in usage in the domain of histopathological applications. However, while these approaches have shown great potential, in high-risk environments deep learning models need to be able to judge their uncertainty and be able to reject inputs when there is a significant chance of misclassification. In this work, we conduct a rigorous evaluation of the most commonly used uncertainty and robustness methods for the classification of Whole Slide Images, with a focus on the task of selective classification, where the model should reject the classification in situations in which it is uncertain. We conduct our experiments on tile-level under the aspects of domain shift and label noise, as well as on slide-level. In our experiments, we compare Deep Ensembles, Monte-Carlo Dropout, Stochastic Variational Inference, Test-Time Data Augmentation as well as ensembles of the latter approaches. We observe that ensembles of methods generally lead to better uncertainty estimates as well as an increased robustness towards domain shifts and label noise, while contrary to results from classical computer vision benchmarks no systematic gain of the other methods can be shown. Across methods, a rejection of the most uncertain samples reliably leads to a significant increase in classification accuracy on both in-distribution as well as out-of-distribution data. Furthermore, we conduct experiments comparing these methods under varying conditions of label noise. Lastly, we publish our code framework to facilitate further research on uncertainty estimation on histopathological data.
    Fast and Multi-aspect Mining of Complex Time-stamped Event Streams. (arXiv:2303.03789v2 [cs.LG] UPDATED)
    Given a huge, online stream of time-evolving events with multiple attributes, such as online shopping logs: (item, price, brand, time), and local mobility activities: (pick-up and drop-off locations, time), how can we summarize large, dynamic high-order tensor streams? How can we see any hidden patterns, rules, and anomalies? Our answer is to focus on two types of patterns, i.e., ''regimes'' and ''components'', for which we present CubeScope, an efficient and effective method over high-order tensor streams. Specifically, it identifies any sudden discontinuity and recognizes distinct dynamical patterns, ''regimes'' (e.g., weekday/weekend/holiday patterns). In each regime, it also performs multi-way summarization for all attributes (e.g., item, price, brand, and time) and discovers hidden ''components'' representing latent groups (e.g., item/brand groups) and their relationship. Thanks to its concise but effective summarization, CubeScope can also detect the sudden appearance of anomalies and identify the types of anomalies that occur in practice. Our proposed method has the following properties: (a) Effective: it captures dynamical multi-aspect patterns, i.e., regimes and components, and statistically summarizes all the events; (b) General: it is practical for successful application to data compression, pattern discovery, and anomaly detection on various types of tensor streams; (c) Scalable: our algorithm does not depend on the length of the data stream and its dimensionality. Extensive experiments on real datasets demonstrate that CubeScope finds meaningful patterns and anomalies correctly, and consistently outperforms the state-of-the-art methods as regards accuracy and execution speed.
    When Does Confidence-Based Cascade Deferral Suffice?. (arXiv:2307.02764v1 [cs.LG])
    Cascades are a classical strategy to enable inference cost to vary adaptively across samples, wherein a sequence of classifiers are invoked in turn. A deferral rule determines whether to invoke the next classifier in the sequence, or to terminate prediction. One simple deferral rule employs the confidence of the current classifier, e.g., based on the maximum predicted softmax probability. Despite being oblivious to the structure of the cascade -- e.g., not modelling the errors of downstream models -- such confidence-based deferral often works remarkably well in practice. In this paper, we seek to better understand the conditions under which confidence-based deferral may fail, and when alternate deferral strategies can perform better. We first present a theoretical characterisation of the optimal deferral rule, which precisely characterises settings under which confidence-based deferral may suffer. We then study post-hoc deferral mechanisms, and demonstrate they can significantly improve upon confidence-based deferral in settings where (i) downstream models are specialists that only work well on a subset of inputs, (ii) samples are subject to label noise, and (iii) there is distribution shift between the train and test set.
    SegNetr: Rethinking the local-global interactions and skip connections in U-shaped networks. (arXiv:2307.02953v1 [eess.IV])
    Recently, U-shaped networks have dominated the field of medical image segmentation due to their simple and easily tuned structure. However, existing U-shaped segmentation networks: 1) mostly focus on designing complex self-attention modules to compensate for the lack of long-term dependence based on convolution operation, which increases the overall number of parameters and computational complexity of the network; 2) simply fuse the features of encoder and decoder, ignoring the connection between their spatial locations. In this paper, we rethink the above problem and build a lightweight medical image segmentation network, called SegNetr. Specifically, we introduce a novel SegNetr block that can perform local-global interactions dynamically at any stage and with only linear complexity. At the same time, we design a general information retention skip connection (IRSC) to preserve the spatial location information of encoder features and achieve accurate fusion with the decoder features. We validate the effectiveness of SegNetr on four mainstream medical image segmentation datasets, with 59\% and 76\% fewer parameters and GFLOPs than vanilla U-Net, while achieving segmentation performance comparable to state-of-the-art methods. Notably, the components proposed in this paper can be applied to other U-shaped networks to improve their segmentation performance.
    Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks. (arXiv:2210.12669v2 [cs.LG] UPDATED)
    Physics-informed neural networks (PINNs) are emerging as popular mesh-free solvers for partial differential equations (PDEs). Recent extensions decompose the domain, apply different PINNs to solve the problem in each subdomain, and stitch the subdomains at the interface. Thereby, they can further alleviate the problem complexity, reduce the computational cost, and allow parallelization. However, the performance of multi-domain PINNs is sensitive to the choice of the interface conditions. While quite a few conditions have been proposed, there is no suggestion about how to select the conditions according to specific problems. To address this gap, we propose META Learning of Interface Conditions (METALIC), a simple, efficient yet powerful approach to dynamically determine appropriate interface conditions for solving a family of parametric PDEs. Specifically, we develop two contextual multi-arm bandit (MAB) models. The first one applies to the entire training course, and online updates a Gaussian process (GP) reward that given the PDE parameters and interface conditions predicts the performance. We prove a sub-linear regret bound for both UCB and Thompson sampling, which in theory guarantees the effectiveness of our MAB. The second one partitions the training into two stages, one is the stochastic phase and the other deterministic phase; we update a GP reward for each phase to enable different condition selections at the two stages to further bolster the flexibility and performance. We have shown the advantage of METALIC on four bench-mark PDE families.
    Semi-supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation. (arXiv:2307.02574v1 [cs.CV])
    Accurate building height estimation is key to the automatic derivation of 3D city models from emerging big geospatial data, including Volunteered Geographical Information (VGI). However, an automatic solution for large-scale building height estimation based on low-cost VGI data is currently missing. The fast development of VGI data platforms, especially OpenStreetMap (OSM) and crowdsourced street-view images (SVI), offers a stimulating opportunity to fill this research gap. In this work, we propose a semi-supervised learning (SSL) method of automatically estimating building height from Mapillary SVI and OSM data to generate low-cost and open-source 3D city modeling in LoD1. The proposed method consists of three parts: first, we propose an SSL schema with the option of setting a different ratio of "pseudo label" during the supervised regression; second, we extract multi-level morphometric features from OSM data (i.e., buildings and streets) for the purposed of inferring building height; last, we design a building floor estimation workflow with a pre-trained facade object detection network to generate "pseudo label" from SVI and assign it to the corresponding OSM building footprint. In a case study, we validate the proposed SSL method in the city of Heidelberg, Germany and evaluate the model performance against the reference data of building heights. Based on three different regression models, namely Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), the SSL method leads to a clear performance boosting in estimating building heights with a Mean Absolute Error (MAE) around 2.1 meters, which is competitive to state-of-the-art approaches. The preliminary result is promising and motivates our future work in scaling up the proposed method based on low-cost VGI data, with possibilities in even regions and areas with diverse data quality and availability.
    DPM: Clustering Sensitive Data through Separation. (arXiv:2307.02969v1 [cs.CR])
    Privacy-preserving clustering groups data points in an unsupervised manner whilst ensuring that sensitive information remains protected. Previous privacy-preserving clustering focused on identifying concentration of point clouds. In this paper, we take another path and focus on identifying appropriate separators that split a data set. We introduce the novel differentially private clustering algorithm DPM that searches for accurate data point separators in a differentially private manner. DPM addresses two key challenges for finding accurate separators: identifying separators that are large gaps between clusters instead of small gaps within a cluster and, to efficiently spend the privacy budget, prioritising separators that split the data into large subparts. Using the differentially private Exponential Mechanism, DPM randomly chooses cluster separators with provably high utility: For a data set $D$, if there is a wide low-density separator in the central $60\%$ quantile, DPM finds that separator with probability $1 - \exp(-\sqrt{|D|})$. Our experimental evaluation demonstrates that DPM achieves significant improvements in terms of the clustering metric inertia. With the inertia results of the non-private KMeans++ as a baseline, for $\varepsilon = 1$ and $\delta=10^{-5}$ DPM improves upon the difference to the baseline by up to $50\%$ for a synthetic data set and by up to $62\%$ for a real-world data set compared to a state-of-the-art clustering algorithm by Chang and Kamath.
    A Hybrid End-to-End Spatio-Temporal Attention Neural Network with Graph-Smooth Signals for EEG Emotion Recognition. (arXiv:2307.03068v1 [cs.LG])
    Recently, physiological data such as electroencephalography (EEG) signals have attracted significant attention in affective computing. In this context, the main goal is to design an automated model that can assess emotional states. Lately, deep neural networks have shown promising performance in emotion recognition tasks. However, designing a deep architecture that can extract practical information from raw data is still a challenge. Here, we introduce a deep neural network that acquires interpretable physiological representations by a hybrid structure of spatio-temporal encoding and recurrent attention network blocks. Furthermore, a preprocessing step is applied to the raw data using graph signal processing tools to perform graph smoothing in the spatial domain. We demonstrate that our proposed architecture exceeds state-of-the-art results for emotion classification on the publicly available DEAP dataset. To explore the generality of the learned model, we also evaluate the performance of our architecture towards transfer learning (TL) by transferring the model parameters from a specific source to other target domains. Using DEAP as the source dataset, we demonstrate the effectiveness of our model in performing cross-modality TL and improving emotion classification accuracy on DREAMER and the Emotional English Word (EEWD) datasets, which involve EEG-based emotion classification tasks with different stimuli.
    Towards Symmetry-Aware Generation of Periodic Materials. (arXiv:2307.02707v1 [cs.LG])
    We consider the problem of generating periodic materials with deep models. While symmetry-aware molecule generation has been studied extensively, periodic materials possess different symmetries, which have not been completely captured by existing methods. In this work, we propose SyMat, a novel material generation approach that can capture physical symmetries of periodic material structures. SyMat generates atom types and lattices of materials through generating atom type sets, lattice lengths and lattice angles with a variational auto-encoder model. In addition, SyMat employs a score-based diffusion model to generate atom coordinates of materials, in which a novel symmetry-aware probabilistic model is used in the coordinate diffusion process. We show that SyMat is theoretically invariant to all symmetry transformations on materials and demonstrate that SyMat achieves promising performance on random generation and property optimization tasks.
    Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assembly. (arXiv:2307.01317v2 [cs.RO] UPDATED)
    Machine Learning (ML) models in Robotic Assembly Sequence Planning (RASP) need to be introspective on the predicted solutions, i.e. whether they are feasible or not, to circumvent potential efficiency degradation. Previous works need both feasible and infeasible examples during training. However, the infeasible ones are hard to collect sufficiently when re-training is required for swift adaptation to new product variants. In this work, we propose a density-based feasibility learning method that requires only feasible examples. Concretely, we formulate the feasibility learning problem as Out-of-Distribution (OOD) detection with Normalizing Flows (NF), which are powerful generative models for estimating complex probability distributions. Empirically, the proposed method is demonstrated on robotic assembly use cases and outperforms other single-class baselines in detecting infeasible assemblies. We further investigate the internal working mechanism of our method and show that a large memory saving can be obtained based on an advanced variant of NF.
    STS-CCL: Spatial-Temporal Synchronous Contextual Contrastive Learning for Urban Traffic Forecasting. (arXiv:2307.02507v1 [cs.LG])
    Efficiently capturing the complex spatiotemporal representations from large-scale unlabeled traffic data remains to be a challenging task. In considering of the dilemma, this work employs the advanced contrastive learning and proposes a novel Spatial-Temporal Synchronous Contextual Contrastive Learning (STS-CCL) model. First, we elaborate the basic and strong augmentation methods for spatiotemporal graph data, which not only perturb the data in terms of graph structure and temporal characteristics, but also employ a learning-based dynamic graph view generator for adaptive augmentation. Second, we introduce a Spatial-Temporal Synchronous Contrastive Module (STS-CM) to simultaneously capture the decent spatial-temporal dependencies and realize graph-level contrasting. To further discriminate node individuals in negative filtering, a Semantic Contextual Contrastive method is designed based on semantic features and spatial heterogeneity, achieving node-level contrastive learning along with negative filtering. Finally, we present a hard mutual-view contrastive training scheme and extend the classic contrastive loss to an integrated objective function, yielding better performance. Extensive experiments and evaluations demonstrate that building a predictor upon STS-CCL contrastive learning model gains superior performance than existing traffic forecasting benchmarks. The proposed STS-CCL is highly suitable for large datasets with only a few labeled data and other spatiotemporal tasks with data scarcity issue.
    Large Language Models Empowered Autonomous Edge AI for Connected Intelligence. (arXiv:2307.02779v1 [cs.IT])
    The evolution of wireless networks gravitates towards connected intelligence, a concept that envisions seamless interconnectivity among humans, objects, and intelligence in a hyper-connected cyber-physical world. Edge AI emerges as a promising solution to achieve connected intelligence by delivering high-quality, low-latency, and privacy-preserving AI services at the network edge. In this article, we introduce an autonomous edge AI system that automatically organizes, adapts, and optimizes itself to meet users' diverse requirements. The system employs a cloud-edge-client hierarchical architecture, where the large language model, i.e., Generative Pretrained Transformer (GPT), resides in the cloud, and other AI models are co-deployed on devices and edge servers. By leveraging the powerful abilities of GPT in language understanding, planning, and code generation, we present a versatile framework that efficiently coordinates edge AI models to cater to users' personal demands while automatically generating code to train new models via edge federated learning. Experimental results demonstrate the system's remarkable ability to accurately comprehend user demands, efficiently execute AI models with minimal cost, and effectively create high-performance AI models through federated learning.
    Potential sources of dataset bias complicate investigation of underdiagnosis by machine learning algorithms. (arXiv:2201.07856v2 [cs.AI] UPDATED)
    An increasing number of reports raise concerns about the risk that machine learning algorithms could amplify health disparities due to biases embedded in the training data. Seyyed-Kalantari et al. find that models trained on three chest X-ray datasets yield disparities in false-positive rates (FPR) across subgroups on the 'no-finding' label (indicating the absence of disease). The models consistently yield higher FPR on subgroups known to be historically underserved, and the study concludes that the models exhibit and potentially even amplify systematic underdiagnosis. We argue that the experimental setup in the study is insufficient to study algorithmic underdiagnosis. In the absence of specific knowledge (or assumptions) about the extent and nature of the dataset bias, it is difficult to investigate model bias. Importantly, their use of test data exhibiting the same bias as the training data (due to random splitting) severely complicates the interpretation of the reported disparities.
    DataPerf: Benchmarks for Data-Centric AI Development. (arXiv:2207.10062v2 [cs.LG] UPDATED)
    Machine learning research has long focused on models rather than datasets, and prominent datasets are used for common ML tasks without regard to the breadth, difficulty, and faithfulness of the underlying problems. Neglecting the fundamental importance of data has given rise to inaccuracy, bias, and fragility in real-world applications, and research is hindered by saturation across existing dataset benchmarks. In response, we present DataPerf, a community-led benchmark suite for evaluating ML datasets and data-centric algorithms. We aim to foster innovation in data-centric AI through competition, comparability, and reproducibility. We enable the ML community to iterate on datasets, instead of just architectures, and we provide an open, online platform with multiple rounds of challenges to support this iterative development. The first iteration of DataPerf contains five benchmarks covering a wide spectrum of data-centric techniques, tasks, and modalities in vision, speech, acquisition, debugging, and diffusion prompting, and we support hosting new contributed benchmarks from the community. The benchmarks, online evaluation platform, and baseline implementations are open source, and the MLCommons Association will maintain DataPerf to ensure long-term benefits to academia and industry.
    Policy Contrastive Imitation Learning. (arXiv:2307.02829v1 [cs.LG])
    Adversarial imitation learning (AIL) is a popular method that has recently achieved much success. However, the performance of AIL is still unsatisfactory on the more challenging tasks. We find that one of the major reasons is due to the low quality of AIL discriminator representation. Since the AIL discriminator is trained via binary classification that does not necessarily discriminate the policy from the expert in a meaningful way, the resulting reward might not be meaningful either. We propose a new method called Policy Contrastive Imitation Learning (PCIL) to resolve this issue. PCIL learns a contrastive representation space by anchoring on different policies and generates a smooth cosine-similarity-based reward. Our proposed representation learning objective can be viewed as a stronger version of the AIL objective and provide a more meaningful comparison between the agent and the policy. From a theoretical perspective, we show the validity of our method using the apprenticeship learning framework. Furthermore, our empirical evaluation on the DeepMind Control suite demonstrates that PCIL can achieve state-of-the-art performance. Finally, qualitative results suggest that PCIL builds a smoother and more meaningful representation space for imitation learning.
    Synthesizing Artistic Cinemagraphs from Text. (arXiv:2307.03190v1 [cs.CV])
    We introduce Artistic Cinemagraph, a fully automated method for creating cinemagraphs from text descriptions - an especially challenging task when prompts feature imaginary elements and artistic styles, given the complexity of interpreting the semantics and motions of these images. Existing single-image animation methods fall short on artistic inputs, and recent text-based video methods frequently introduce temporal inconsistencies, struggling to keep certain regions static. To address these challenges, we propose an idea of synthesizing image twins from a single text prompt - a pair of an artistic image and its pixel-aligned corresponding natural-looking twin. While the artistic image depicts the style and appearance detailed in our text prompt, the realistic counterpart greatly simplifies layout and motion analysis. Leveraging existing natural image and video datasets, we can accurately segment the realistic image and predict plausible motion given the semantic information. The predicted motion can then be transferred to the artistic image to create the final cinemagraph. Our method outperforms existing approaches in creating cinemagraphs for natural landscapes as well as artistic and other-worldly scenes, as validated by automated metrics and user studies. Finally, we demonstrate two extensions: animating existing paintings and controlling motion directions using text.
    Align With Purpose: Optimize Desired Properties in CTC Models with a General Plug-and-Play Framework. (arXiv:2307.01715v2 [cs.CL] UPDATED)
    Connectionist Temporal Classification (CTC) is a widely used criterion for training supervised sequence-to-sequence (seq2seq) models. It enables learning the relations between input and output sequences, termed alignments, by marginalizing over perfect alignments (that yield the ground truth), at the expense of imperfect alignments. This binary differentiation of perfect and imperfect alignments falls short of capturing other essential alignment properties that hold significance in other real-world applications. Here we propose $\textit{Align With Purpose}$, a $\textbf{general Plug-and-Play framework}$ for enhancing a desired property in models trained with the CTC criterion. We do that by complementing the CTC with an additional loss term that prioritizes alignments according to a desired property. Our method does not require any intervention in the CTC loss function, enables easy optimization of a variety of properties, and allows differentiation between both perfect and imperfect alignments. We apply our framework in the domain of Automatic Speech Recognition (ASR) and show its generality in terms of property selection, architectural choice, and scale of training dataset (up to 280,000 hours). To demonstrate the effectiveness of our framework, we apply it to two unrelated properties: emission time and word error rate (WER). For the former, we report an improvement of up to 570ms in latency optimization with a minor reduction in WER, and for the latter, we report a relative improvement of 4.5% WER over the baseline models. To the best of our knowledge, these applications have never been demonstrated to work on a scale of data as large as ours. Notably, our method can be implemented using only a few lines of code, and can be extended to other alignment-free loss functions and to domains other than ASR.
    A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond. (arXiv:2204.09269v2 [cs.CL] UPDATED)
    Non-autoregressive (NAR) generation, which is first proposed in neural machine translation (NMT) to speed up inference, has attracted much attention in both machine learning and natural language processing communities. While NAR generation can significantly accelerate inference speed for machine translation, the speedup comes at the cost of sacrificed translation accuracy compared to its counterpart, autoregressive (AR) generation. In recent years, many new models and algorithms have been designed/proposed to bridge the accuracy gap between NAR generation and AR generation. In this paper, we conduct a systematic survey with comparisons and discussions of various non-autoregressive translation (NAT) models from different aspects. Specifically, we categorize the efforts of NAT into several groups, including data manipulation, modeling methods, training criterion, decoding algorithms, and the benefit from pre-trained models. Furthermore, we briefly review other applications of NAR models beyond machine translation, such as grammatical error correction, text summarization, text style transfer, dialogue, semantic parsing, automatic speech recognition, and so on. In addition, we also discuss potential directions for future exploration, including releasing the dependency of KD, reasonable training objectives, pre-training for NAR, and wider applications, etc. We hope this survey can help researchers capture the latest progress in NAR generation, inspire the design of advanced NAR models and algorithms, and enable industry practitioners to choose appropriate solutions for their applications. The web page of this survey is at \url{https://github.com/LitterBrother-Xiao/Overview-of-Non-autoregressive-Applications}.
    Active Class Selection for Few-Shot Class-Incremental Learning. (arXiv:2307.02641v1 [cs.RO])
    For real-world applications, robots will need to continually learn in their environments through limited interactions with their users. Toward this, previous works in few-shot class incremental learning (FSCIL) and active class selection (ACS) have achieved promising results but were tested in constrained setups. Therefore, in this paper, we combine ideas from FSCIL and ACS to develop a novel framework that can allow an autonomous agent to continually learn new objects by asking its users to label only a few of the most informative objects in the environment. To this end, we build on a state-of-the-art (SOTA) FSCIL model and extend it with techniques from ACS literature. We term this model Few-shot Incremental Active class SeleCtiOn (FIASco). We further integrate a potential field-based navigation technique with our model to develop a complete framework that can allow an agent to process and reason on its sensory data through the FIASco model, navigate towards the most informative object in the environment, gather data about the object through its sensors and incrementally update the FIASco model. Experimental results on a simulated agent and a real robot show the significance of our approach for long-term real-world robotics applications.
    Layer-wise Adaptive Step-Sizes for Stochastic First-Order Methods for Deep Learning. (arXiv:2305.13664v3 [cs.LG] UPDATED)
    We propose a new per-layer adaptive step-size procedure for stochastic first-order optimization methods for minimizing empirical loss functions in deep learning, eliminating the need for the user to tune the learning rate (LR). The proposed approach exploits the layer-wise stochastic curvature information contained in the diagonal blocks of the Hessian in deep neural networks (DNNs) to compute adaptive step-sizes (i.e., LRs) for each layer. The method has memory requirements that are comparable to those of first-order methods, while its per-iteration time complexity is only increased by an amount that is roughly equivalent to an additional gradient computation. Numerical experiments show that SGD with momentum and AdamW combined with the proposed per-layer step-sizes are able to choose effective LR schedules and outperform fine-tuned LR versions of these methods as well as popular first-order and second-order algorithms for training DNNs on Autoencoder, Convolutional Neural Network (CNN) and Graph Convolutional Network (GCN) models. Finally, it is proved that an idealized version of SGD with the layer-wise step sizes converges linearly when using full-batch gradients.
    Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network. (arXiv:2307.01622v2 [cs.LG] UPDATED)
    Smart home energy management systems help the distribution grid operate more efficiently and reliably, and enable effective penetration of distributed renewable energy sources. These systems rely on robust forecasting, optimization, and control/scheduling algorithms that can handle the uncertain nature of demand and renewable generation. This paper proposes an advanced ML algorithm, called Recurrent Trend Predictive Neural Network based Forecast Embedded Scheduling (rTPNN-FES), to provide efficient residential demand control. rTPNN-FES is a novel neural network architecture that simultaneously forecasts renewable energy generation and schedules household appliances. By its embedded structure, rTPNN-FES eliminates the utilization of separate algorithms for forecasting and scheduling and generates a schedule that is robust against forecasting errors. This paper also evaluates the performance of the proposed algorithm for an IoT-enabled smart home. The evaluation results reveal that rTPNN-FES provides near-optimal scheduling $37.5$ times faster than the optimization while outperforming state-of-the-art forecasting techniques.
    Improving the Efficiency of Human-in-the-Loop Systems: Adding Artificial to Human Experts. (arXiv:2307.03003v1 [cs.LG])
    Information systems increasingly leverage artificial intelligence (AI) and machine learning (ML) to generate value from vast amounts of data. However, ML models are imperfect and can generate incorrect classifications. Hence, human-in-the-loop (HITL) extensions to ML models add a human review for instances that are difficult to classify. This study argues that continuously relying on human experts to handle difficult model classifications leads to a strong increase in human effort, which strains limited resources. To address this issue, we propose a hybrid system that creates artificial experts that learn to classify data instances from unknown classes previously reviewed by human experts. Our hybrid system assesses which artificial expert is suitable for classifying an instance from an unknown class and automatically assigns it. Over time, this reduces human effort and increases the efficiency of the system. Our experiments demonstrate that our approach outperforms traditional HITL systems for several benchmarks on image classification.
    Hierarchical Empowerment: Towards Tractable Empowerment-Based Skill-Learning. (arXiv:2307.02728v1 [cs.LG])
    General purpose agents will require large repertoires of skills. Empowerment -- the maximum mutual information between skills and the states -- provides a pathway for learning large collections of distinct skills, but mutual information is difficult to optimize. We introduce a new framework, Hierarchical Empowerment, that makes computing empowerment more tractable by integrating concepts from Goal-Conditioned Hierarchical Reinforcement Learning. Our framework makes two specific contributions. First, we introduce a new variational lower bound on mutual information that can be used to compute empowerment over short horizons. Second, we introduce a hierarchical architecture for computing empowerment over exponentially longer time scales. We verify the contributions of the framework in a series of simulated robotics tasks. In a popular ant navigation domain, our four level agents are able to learn skills that cover a surface area over two orders of magnitude larger than prior work.
    Learning Disentangled Representations in Signed Directed Graphs without Social Assumptions. (arXiv:2307.03077v1 [cs.LG])
    Signed graphs are complex systems that represent trust relationships or preferences in various domains. Learning node representations in such graphs is crucial for many mining tasks. Although real-world signed relationships can be influenced by multiple latent factors, most existing methods often oversimplify the modeling of signed relationships by relying on social theories and treating them as simplistic factors. This limits their expressiveness and their ability to capture the diverse factors that shape these relationships. In this paper, we propose DINES, a novel method for learning disentangled node representations in signed directed graphs without social assumptions. We adopt a disentangled framework that separates each embedding into distinct factors, allowing for capturing multiple latent factors. We also explore lightweight graph convolutions that focus solely on sign and direction, without depending on social theories. Additionally, we propose a decoder that effectively classifies an edge's sign by considering correlations between the factors. To further enhance disentanglement, we jointly train a self-supervised factor discriminator with our encoder and decoder. Throughout extensive experiments on real-world signed directed graphs, we show that DINES effectively learns disentangled node representations, and significantly outperforms its competitors in the sign prediction task.
    Topology-Aware Loss for Aorta and Great Vessel Segmentation in Computed Tomography Images. (arXiv:2307.03137v1 [eess.IV])
    Segmentation networks are not explicitly imposed to learn global invariants of an image, such as the shape of an object and the geometry between multiple objects, when they are trained with a standard loss function. On the other hand, incorporating such invariants into network training may help improve performance for various segmentation tasks when they are the intrinsic characteristics of the objects to be segmented. One example is segmentation of aorta and great vessels in computed tomography (CT) images where vessels are found in a particular geometry in the body due to the human anatomy and they mostly seem as round objects on a 2D CT image. This paper addresses this issue by introducing a new topology-aware loss function that penalizes topology dissimilarities between the ground truth and prediction through persistent homology. Different from the previously suggested segmentation network designs, which apply the threshold filtration on a likelihood function of the prediction map and the Betti numbers of the ground truth, this paper proposes to apply the Vietoris-Rips filtration to obtain persistence diagrams of both ground truth and prediction maps and calculate the dissimilarity with the Wasserstein distance between the corresponding persistence diagrams. The use of this filtration has advantage of modeling shape and geometry at the same time, which may not happen when the threshold filtration is applied. Our experiments on 4327 CT images of 24 subjects reveal that the proposed topology-aware loss function leads to better results than its counterparts, indicating the effectiveness of this use.
    Convolutional Filtering and Neural Networks with Non Commutative Algebras. (arXiv:2108.09923v3 [cs.LG] UPDATED)
    In this paper we introduce and study the algebraic generalization of non commutative convolutional neural networks. We leverage the theory of algebraic signal processing to model convolutional non commutative architectures, and we derive concrete stability bounds that extend those obtained in the literature for commutative convolutional neural networks. We show that non commutative convolutional architectures can be stable to deformations on the space of operators. We develop the spectral representation of non commutative signal models to show that non commutative filters process Fourier components independently of each other. In particular we prove that although the spectral decompositions of signals in non commutative models are associated to eigenspaces of dimension larger than one, there exists a trade-off between stability and selectivity, which is controlled by matrix polynomial functions in spaces of matrices of low dimension. This tradeoff shows how when the filters in the algebra are restricted to be stable, there is a loss in discriminability that is compensated in the network by the pointwise nonlinearities. The results derived in this paper have direct applications and implications in non commutative convolutional architectures such as group neural networks, multigraph neural networks, and quaternion neural networks, for which we provide a set of numerical experiments showing their behavior when perturbations are present.
    PUFFIN: A Path-Unifying Feed-Forward Interfaced Network for Vapor Pressure Prediction. (arXiv:2307.02903v1 [physics.chem-ph])
    Accurately predicting vapor pressure is vital for various industrial and environmental applications. However, obtaining accurate measurements for all compounds of interest is not possible due to the resource and labor intensity of experiments. The demand for resources and labor further multiplies when a temperature-dependent relationship for predicting vapor pressure is desired. In this paper, we propose PUFFIN (Path-Unifying Feed-Forward Interfaced Network), a machine learning framework that combines transfer learning with a new inductive bias node inspired by domain knowledge (the Antoine equation) to improve vapor pressure prediction. By leveraging inductive bias and transfer learning using graph embeddings, PUFFIN outperforms alternative strategies that do not use inductive bias or that use generic descriptors of compounds. The framework's incorporation of domain-specific knowledge to overcome the limitation of poor data availability shows its potential for broader applications in chemical compound analysis, including the prediction of other physicochemical properties. Importantly, our proposed machine learning framework is partially interpretable, because the inductive Antoine node yields network-derived Antoine equation coefficients. It would then be possible to directly incorporate the obtained analytical expression in process design software for better prediction and control of processes occurring in industry and the environment.
    TGRL: An Algorithm for Teacher Guided Reinforcement Learning. (arXiv:2307.03186v1 [cs.LG])
    Learning from rewards (i.e., reinforcement learning or RL) and learning to imitate a teacher (i.e., teacher-student learning) are two established approaches for solving sequential decision-making problems. To combine the benefits of these different forms of learning, it is common to train a policy to maximize a combination of reinforcement and teacher-student learning objectives. However, without a principled method to balance these objectives, prior work used heuristics and problem-specific hyperparameter searches to balance the two objectives. We present a $\textit{principled}$ approach, along with an approximate implementation for $\textit{dynamically}$ and $\textit{automatically}$ balancing when to follow the teacher and when to use rewards. The main idea is to adjust the importance of teacher supervision by comparing the agent's performance to the counterfactual scenario of the agent learning without teacher supervision and only from rewards. If using teacher supervision improves performance, the importance of teacher supervision is increased and otherwise it is decreased. Our method, $\textit{Teacher Guided Reinforcement Learning}$ (TGRL), outperforms strong baselines across diverse domains without hyper-parameter tuning.
    Chaos Theory and Adversarial Robustness. (arXiv:2210.13235v2 [cs.LG] UPDATED)
    Neural networks, being susceptible to adversarial attacks, should face a strict level of scrutiny before being deployed in critical or adversarial applications. This paper uses ideas from Chaos Theory to explain, analyze, and quantify the degree to which neural networks are susceptible to or robust against adversarial attacks. To this end, we present a new metric, the "susceptibility ratio," given by $\hat \Psi(h, \theta)$, which captures how greatly a model's output will be changed by perturbations to a given input. Our results show that susceptibility to attack grows significantly with the depth of the model, which has safety implications for the design of neural networks for production environments. We provide experimental evidence of the relationship between $\hat \Psi$ and the post-attack accuracy of classification models, as well as a discussion of its application to tasks lacking hard decision boundaries. We also demonstrate how to quickly and easily approximate the certified robustness radii for extremely large models, which until now has been computationally infeasible to calculate directly.
    Offline Reinforcement Learning with Imbalanced Datasets. (arXiv:2307.02752v1 [cs.LG])
    The prevalent use of benchmarks in current offline reinforcement learning (RL) research has led to a neglect of the imbalance of real-world dataset distributions in the development of models. The real-world offline RL dataset is often imbalanced over the state space due to the challenge of exploration or safety considerations. In this paper, we specify properties of imbalanced datasets in offline RL, where the state coverage follows a power law distribution characterized by skewed policies. Theoretically and empirically, we show that typically offline RL methods based on distributional constraints, such as conservative Q-learning (CQL), are ineffective in extracting policies under the imbalanced dataset. Inspired by natural intelligence, we propose a novel offline RL method that utilizes the augmentation of CQL with a retrieval process to recall past related experiences, effectively alleviating the challenges posed by imbalanced datasets. We evaluate our method on several tasks in the context of imbalanced datasets with varying levels of imbalance, utilizing the variant of D4RL. Empirical results demonstrate the superiority of our method over other baselines.
    OLR-WA Online Regression with Weighted Average. (arXiv:2307.02804v1 [cs.LG])
    Machine Learning requires a large amount of training data in order to build accurate models. Sometimes the data arrives over time, requiring significant storage space and recalculating the model to account for the new data. On-line learning addresses these issues by incrementally modifying the model as data is encountered, and then discarding the data. In this study we introduce a new online linear regression approach. Our approach combines newly arriving data with a previously existing model to create a new model. The introduced model, named OLR-WA (OnLine Regression with Weighted Average) uses user-defined weights to provide flexibility in the face of changing data to bias the results in favor of old or new data. We have conducted 2-D and 3-D experiments comparing OLR-WA to a static model using the entire data set. The results show that for consistent data, OLR-WA and the static batch model perform similarly and for varying data, the user can set the OLR-WA to adapt more quickly or to resist change.
    Generation of Highlights from Research Papers Using Pointer-Generator Networks and SciBERT Embeddings. (arXiv:2302.07729v2 [cs.CL] UPDATED)
    Nowadays many research articles are prefaced with research highlights to summarize the main findings of the paper. Highlights not only help researchers precisely and quickly identify the contributions of a paper, they also enhance the discoverability of the article via search engines. We aim to automatically construct research highlights given certain segments of a research paper. We use a pointer-generator network with coverage mechanism and a contextual embedding layer at the input that encodes the input tokens into SciBERT embeddings. We test our model on a benchmark dataset, CSPubSum, and also present MixSub, a new multi-disciplinary corpus of papers for automatic research highlight generation. For both CSPubSum and MixSub, we have observed that the proposed model achieves the best performance compared to related variants and other models proposed in the literature. On the CSPubSum dataset, our model achieves the best performance when the input is only the abstract of a paper as opposed to other segments of the paper. It produces ROUGE-1, ROUGE-2 and ROUGE-L F1-scores of 38.26, 14.26 and 35.51, respectively, METEOR score of 32.62, and BERTScore F1 of 86.65 which outperform all other baselines. On the new MixSub dataset, where only the abstract is the input, our proposed model (when trained on the whole training corpus without distinguishing between the subject categories) achieves ROUGE-1, ROUGE-2 and ROUGE-L F1-scores of 31.78, 9.76 and 29.3, respectively, METEOR score of 24.00, and BERTScore F1 of 85.25.
    Free Bits: Latency Optimization of Mixed-Precision Quantized Neural Networks on the Edge. (arXiv:2307.02894v1 [cs.LG])
    Mixed-precision quantization, where a deep neural network's layers are quantized to different precisions, offers the opportunity to optimize the trade-offs between model size, latency, and statistical accuracy beyond what can be achieved with homogeneous-bit-width quantization. To navigate the intractable search space of mixed-precision configurations for a given network, this paper proposes a hybrid search methodology. It consists of a hardware-agnostic differentiable search algorithm followed by a hardware-aware heuristic optimization to find mixed-precision configurations latency-optimized for a specific hardware target. We evaluate our algorithm on MobileNetV1 and MobileNetV2 and deploy the resulting networks on a family of multi-core RISC-V microcontroller platforms with different hardware characteristics. We achieve up to 28.6% reduction of end-to-end latency compared to an 8-bit model at a negligible accuracy drop from a full-precision baseline on the 1000-class ImageNet dataset. We demonstrate speedups relative to an 8-bit baseline, even on systems with no hardware support for sub-byte arithmetic at negligible accuracy drop. Furthermore, we show the superiority of our approach with respect to differentiable search targeting reduced binary operation counts as a proxy for latency.
    Statistical-Computational Tradeoffs in Mixed Sparse Linear Regression. (arXiv:2303.02118v2 [stat.ML] UPDATED)
    We consider the problem of mixed sparse linear regression with two components, where two real $k$-sparse signals $\beta_1, \beta_2$ are to be recovered from $n$ unlabelled noisy linear measurements. The sparsity is allowed to be sublinear in the dimension, and additive noise is assumed to be independent Gaussian with variance $\sigma^2$. Prior work has shown that the problem suffers from a $\frac{k}{SNR^2}$-to-$\frac{k^2}{SNR^2}$ statistical-to-computational gap, resembling other computationally challenging high-dimensional inference problems such as Sparse PCA and Robust Sparse Mean Estimation; here $SNR$ is the signal-to-noise ratio. We establish the existence of a more extensive computational barrier for this problem through the method of low-degree polynomials, but show that the problem is computationally hard only in a very narrow symmetric parameter regime. We identify a smooth information-computation tradeoff between the sample complexity $n$ and runtime for any randomized algorithm in this hard regime. Via a simple reduction, this provides novel rigorous evidence for the existence of a computational barrier to solving exact support recovery in sparse phase retrieval with sample complexity $n = \tilde{o}(k^2)$. Our second contribution is to analyze a simple thresholding algorithm which, outside of the narrow regime where the problem is hard, solves the associated mixed regression detection problem in $O(np)$ time with square-root the number of samples and matches the sample complexity required for (non-mixed) sparse linear regression; this allows the recovery problem to be subsequently solved by state-of-the-art techniques from the dense case. As a special case of our results, we show that this simple algorithm is order-optimal among a large family of algorithms in solving exact signed support recovery in sparse linear regression.
    Time Series Clustering With Random Convolutional Kernels. (arXiv:2305.10457v2 [cs.LG] UPDATED)
    Time series data, spanning applications ranging from climatology to finance to healthcare, presents significant challenges in data mining due to its size and complexity. One open issue lies in time series clustering, which is crucial for processing large volumes of unlabeled time series data and unlocking valuable insights. Traditional and modern analysis methods, however, often struggle with these complexities. To address these limitations, we introduce R-Clustering, a novel method that utilizes convolutional architectures with randomly selected parameters. Through extensive evaluations, R-Clustering demonstrates superior performance over existing methods in terms of clustering accuracy, computational efficiency and scalability. Empirical results obtained using the UCR archive demonstrate the effectiveness of our approach across diverse time series datasets. The findings highlight the significance of R-Clustering in various domains and applications, contributing to the advancement of time series data mining.
    Exploring new ways: Enforcing representational dissimilarity to learn new features and reduce error consistency. (arXiv:2307.02516v1 [cs.LG])
    Independently trained machine learning models tend to learn similar features. Given an ensemble of independently trained models, this results in correlated predictions and common failure modes. Previous attempts focusing on decorrelation of output predictions or logits yielded mixed results, particularly due to their reduction in model accuracy caused by conflicting optimization objectives. In this paper, we propose the novel idea of utilizing methods of the representational similarity field to promote dissimilarity during training instead of measuring similarity of trained models. To this end, we promote intermediate representations to be dissimilar at different depths between architectures, with the goal of learning robust ensembles with disjoint failure modes. We show that highly dissimilar intermediate representations result in less correlated output predictions and slightly lower error consistency, resulting in higher ensemble accuracy. With this, we shine first light on the connection between intermediate representations and their impact on the output predictions.
    Multimodal Temporal Fusion Transformers Are Good Product Demand Forecasters. (arXiv:2307.02578v1 [cs.LG])
    Multimodal demand forecasting aims at predicting product demand utilizing visual, textual, and contextual information. This paper proposes a method for multimodal product demand forecasting using convolutional, graph-based, and transformer-based architectures. Traditional approaches to demand forecasting rely on historical demand, product categories, and additional contextual information such as seasonality and events. However, these approaches have several shortcomings, such as the cold start problem making it difficult to predict product demand until sufficient historical data is available for a particular product, and their inability to properly deal with category dynamics. By incorporating multimodal information, such as product images and textual descriptions, our architecture aims to address the shortcomings of traditional approaches and outperform them. The experiments conducted on a large real-world dataset show that the proposed approach effectively predicts demand for a wide range of products. The multimodal pipeline presented in this work enhances the accuracy and reliability of the predictions, demonstrating the potential of leveraging multimodal information in product demand forecasting.
    Pruning vs Quantization: Which is Better?. (arXiv:2307.02973v1 [cs.LG])
    Neural network pruning and quantization techniques are almost as old as neural networks themselves. However, to date only ad-hoc comparisons between the two have been published. In this paper, we set out to answer the question on which is better: neural network quantization or pruning? By answering this question, we hope to inform design decisions made on neural network hardware going forward. We provide an extensive comparison between the two techniques for compressing deep neural networks. First, we give an analytical comparison of expected quantization and pruning error for general data distributions. Then, we provide lower bounds for the per-layer pruning and quantization error in trained networks, and compare these to empirical error after optimization. Finally, we provide an extensive experimental comparison for training 8 large-scale models on 3 tasks. Our results show that in most cases quantization outperforms pruning. Only in some scenarios with very high compression ratio, pruning might be beneficial from an accuracy standpoint.
    Transfer Learning for the Efficient Detection of COVID-19 from Smartphone Audio Data. (arXiv:2307.02975v1 [cs.LG])
    Disease detection from smartphone data represents an open research challenge in mobile health (m-health) systems. COVID-19 and its respiratory symptoms are an important case study in this area and their early detection is a potential real instrument to counteract the pandemic situation. The efficacy of this solution mainly depends on the performances of AI algorithms applied to the collected data and their possible implementation directly on the users' mobile devices. Considering these issues, and the limited amount of available data, in this paper we present the experimental evaluation of 3 different deep learning models, compared also with hand-crafted features, and of two main approaches of transfer learning in the considered scenario: both feature extraction and fine-tuning. Specifically, we considered VGGish, YAMNET, and L\textsuperscript{3}-Net (including 12 different configurations) evaluated through user-independent experiments on 4 different datasets (13,447 samples in total). Results clearly show the advantages of L\textsuperscript{3}-Net in all the experimental settings as it overcomes the other solutions by 12.3\% in terms of Precision-Recall AUC as features extractor, and by 10\% when the model is fine-tuned. Moreover, we note that to fine-tune only the fully-connected layers of the pre-trained models generally leads to worse performances, with an average drop of 6.6\% with respect to feature extraction. %highlighting the need for further investigations. Finally, we evaluate the memory footprints of the different models for their possible applications on commercial mobile devices.
    A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms. (arXiv:2211.12386v2 [cs.LG] UPDATED)
    Meta-learning of numerical algorithms for a given task consists of the data-driven identification and adaptation of an algorithmic structure and the associated hyperparameters. To limit the complexity of the meta-learning problem, neural architectures with a certain inductive bias towards favorable algorithmic structures can, and should, be used. We generalize our previously introduced Runge-Kutta neural network to a recursively recurrent neural network (R2N2) superstructure for the design of customized iterative algorithms. In contrast to off-the-shelf deep learning approaches, it features a distinct division into modules for generation of information and for the subsequent assembly of this information towards a solution. Local information in the form of a subspace is generated by subordinate, inner, iterations of recurrent function evaluations starting at the current outer iterate. The update to the next outer iterate is computed as a linear combination of these evaluations, reducing the residual in this space, and constitutes the output of the network. We demonstrate that regular training of the weight parameters inside the proposed superstructure on input/output data of various computational problem classes yields iterations similar to Krylov solvers for linear equation systems, Newton-Krylov solvers for nonlinear equation systems, and Runge-Kutta integrators for ordinary differential equations. Due to its modularity, the superstructure can be readily extended with functionalities needed to represent more general classes of iterative algorithms traditionally based on Taylor series expansions.
    Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation. (arXiv:2307.02598v1 [cs.LG])
    We tackle the problems of latent variables identification and "out-of-support" image generation in representation learning. We show that both are possible for a class of decoders that we call additive, which are reminiscent of decoders used for object-centric representation learning (OCRL) and well suited for images that can be decomposed as a sum of object-specific images. We provide conditions under which exactly solving the reconstruction problem using an additive decoder is guaranteed to identify the blocks of latent variables up to permutation and block-wise invertible transformations. This guarantee relies only on very weak assumptions about the distribution of the latent factors, which might present statistical dependencies and have an almost arbitrarily shaped support. Our result provides a new setting where nonlinear independent component analysis (ICA) is possible and adds to our theoretical understanding of OCRL methods. We also show theoretically that additive decoders can generate novel images by recombining observed factors of variations in novel ways, an ability we refer to as Cartesian-product extrapolation. We show empirically that additivity is crucial for both identifiability and extrapolation on simulated data.
    Airfoil GAN: Encoding and Synthesizing Airfoils for Aerodynamic Shape Optimization. (arXiv:2101.04757v2 [cs.CE] UPDATED)
    The current design of aerodynamic shapes, like airfoils, involves computationally intensive simulations to explore the possible design space. Usually, such design relies on the prior definition of design parameters and places restrictions on synthesizing novel shapes. In this work, we propose a data-driven shape encoding and generating method, which automatically learns representations from existing airfoils and uses the learned representations to generate new airfoils. The representations are then used in the optimization of synthesized airfoil shapes based on their aerodynamic performance. Our model is built upon VAEGAN, a neural network that combines Variational Autoencoder with Generative Adversarial Network and is trained by the gradient-based technique. Our model can (1) encode the existing airfoil into a latent vector and reconstruct the airfoil from that, (2) generate novel airfoils by randomly sampling the latent vectors and mapping the vectors to the airfoil coordinate domain, and (3) synthesize airfoils with desired aerodynamic properties by optimizing learned features via a genetic algorithm. Our experiments show that the learned features encode shape information thoroughly and comprehensively without predefined design parameters. By interpolating/extrapolating feature vectors or sampling from Gaussian noises, the model can automatically synthesize novel airfoil shapes, some of which possess competitive or even better aerodynamic properties comparing to airfoils used for model training purposes. By optimizing shapes on the learned latent domain via a genetic algorithm, synthesized airfoils can evolve to target aerodynamic properties. This demonstrates an efficient learning-based airfoil design framework, which encodes and optimizes the airfoil on the latent domain and synthesizes promising airfoil candidates for required aerodynamic performance.
    The Impact of ChatGPT and LLMs on Medical Imaging Stakeholders: Perspectives and Use Cases. (arXiv:2306.06767v2 [eess.IV] UPDATED)
    This study investigates the transformative potential of Large Language Models (LLMs), such as OpenAI ChatGPT, in medical imaging. With the aid of public data, these models, which possess remarkable language understanding and generation capabilities, are augmenting the interpretive skills of radiologists, enhancing patient-physician communication, and streamlining clinical workflows. The paper introduces an analytic framework for presenting the complex interactions between LLMs and the broader ecosystem of medical imaging stakeholders, including businesses, insurance entities, governments, research institutions, and hospitals (nicknamed BIGR-H). Through detailed analyses, illustrative use cases, and discussions on the broader implications and future directions, this perspective seeks to raise discussion in strategic planning and decision-making in the era of AI-enabled healthcare.
    Multi-Similarity Contrastive Learning. (arXiv:2307.02712v1 [cs.LG])
    Given a similarity metric, contrastive methods learn a representation in which examples that are similar are pushed together and examples that are dissimilar are pulled apart. Contrastive learning techniques have been utilized extensively to learn representations for tasks ranging from image classification to caption generation. However, existing contrastive learning approaches can fail to generalize because they do not take into account the possibility of different similarity relations. In this paper, we propose a novel multi-similarity contrastive loss (MSCon), that learns generalizable embeddings by jointly utilizing supervision from multiple metrics of similarity. Our method automatically learns contrastive similarity weightings based on the uncertainty in the corresponding similarity, down-weighting uncertain tasks and leading to better out-of-domain generalization to new tasks. We show empirically that networks trained with MSCon outperform state-of-the-art baselines on in-domain and out-of-domain settings.
    Expert Aggregation for Financial Forecasting. (arXiv:2111.15365v4 [q-fin.ST] UPDATED)
    Machine learning algorithms dedicated to financial time series forecasting have gained a lot of interest. But choosing between several algorithms can be challenging, as their estimation accuracy may be unstable over time. Online aggregation of experts combine the forecasts of a finite set of models in a single approach without making any assumption about the models. In this paper, a Bernstein Online Aggregation (BOA) procedure is applied to the construction of long-short strategies built from individual stock return forecasts coming from different machine learning models. The online mixture of experts leads to attractive portfolio performances even in environments characterised by non-stationarity. The aggregation outperforms individual algorithms, offering a higher portfolio Sharpe Ratio, lower shortfall, with a similar turnover. Extensions to expert and aggregation specialisations are also proposed to improve the overall mixture on a family of portfolio evaluation metrics.
    Provably Efficient Iterated CVaR Reinforcement Learning with Function Approximation. (arXiv:2307.02842v1 [cs.LG])
    Risk-sensitive reinforcement learning (RL) aims to optimize policies that balance the expected reward and risk. In this paper, we investigate a novel risk-sensitive RL formulation with an Iterated Conditional Value-at-Risk (CVaR) objective under linear and general function approximations. This new formulation, named ICVaR-RL with function approximation, provides a principled way to guarantee safety at each decision step. For ICVaR-RL with linear function approximation, we propose a computationally efficient algorithm ICVaR-L, which achieves an $\widetilde{O}(\sqrt{\alpha^{-(H+1)}(d^2H^4+dH^6)K})$ regret, where $\alpha$ is the risk level, $d$ is the dimension of state-action features, $H$ is the length of each episode, and $K$ is the number of episodes. We also establish a matching lower bound $\Omega(\sqrt{\alpha^{-(H-1)}d^2K})$ to validate the optimality of ICVaR-L with respect to $d$ and $K$. For ICVaR-RL with general function approximation, we propose algorithm ICVaR-G, which achieves an $\widetilde{O}(\sqrt{\alpha^{-(H+1)}DH^4K})$ regret, where $D$ is a dimensional parameter that depends on the eluder dimension and covering number. Furthermore, our analysis provides several novel techniques for risk-sensitive RL, including an efficient approximation of the CVaR operator, a new ridge regression with CVaR-adapted features, and a refined elliptical potential lemma.
    Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight. (arXiv:2307.02884v1 [cs.LG])
    This paper studies the sample-efficiency of learning in Partially Observable Markov Decision Processes (POMDPs), a challenging problem in reinforcement learning that is known to be exponentially hard in the worst-case. Motivated by real-world settings such as loading in game playing, we propose an enhanced feedback model called ``multiple observations in hindsight'', where after each episode of interaction with the POMDP, the learner may collect multiple additional observations emitted from the encountered latent states, but may not observe the latent states themselves. We show that sample-efficient learning under this feedback model is possible for two new subclasses of POMDPs: \emph{multi-observation revealing POMDPs} and \emph{distinguishable POMDPs}. Both subclasses generalize and substantially relax \emph{revealing POMDPs} -- a widely studied subclass for which sample-efficient learning is possible under standard trajectory feedback. Notably, distinguishable POMDPs only require the emission distributions from different latent states to be \emph{different} instead of \emph{linearly independent} as required in revealing POMDPs.
    Benchmarking Test-Time Adaptation against Distribution Shifts in Image Classification. (arXiv:2307.03133v1 [cs.LG])
    Test-time adaptation (TTA) is a technique aimed at enhancing the generalization performance of models by leveraging unlabeled samples solely during prediction. Given the need for robustness in neural network systems when faced with distribution shifts, numerous TTA methods have recently been proposed. However, evaluating these methods is often done under different settings, such as varying distribution shifts, backbones, and designing scenarios, leading to a lack of consistent and fair benchmarks to validate their effectiveness. To address this issue, we present a benchmark that systematically evaluates 13 prominent TTA methods and their variants on five widely used image classification datasets: CIFAR-10-C, CIFAR-100-C, ImageNet-C, DomainNet, and Office-Home. These methods encompass a wide range of adaptation scenarios (e.g. online adaptation v.s. offline adaptation, instance adaptation v.s. batch adaptation v.s. domain adaptation). Furthermore, we explore the compatibility of different TTA methods with diverse network backbones. To implement this benchmark, we have developed a unified framework in PyTorch, which allows for consistent evaluation and comparison of the TTA methods across the different datasets and network architectures. By establishing this benchmark, we aim to provide researchers and practitioners with a reliable means of assessing and comparing the effectiveness of TTA methods in improving model robustness and generalization performance. Our code is available at https://github.com/yuyongcan/Benchmark-TTA.
    Adapting Neural Link Predictors for Complex Query Answering. (arXiv:2301.12313v2 [cs.LG] UPDATED)
    Answering complex queries on incomplete knowledge graphs is a challenging task where a model needs to answer complex logical queries in the presence of missing knowledge. Recently, Arakelyan et al. (2021); Minervini et al. (2022) showed that neural link predictors could also be used for answering complex queries: their Continuous Query Decomposition (CQD) method works by decomposing complex queries into atomic sub-queries, answers them using neural link predictors and aggregates their scores via t-norms for ranking the answers to each complex query. However, CQD does not handle negations and only uses the training signal from atomic training queries: neural link prediction scores are not calibrated to interact together via fuzzy logic t-norms during complex query answering. In this work, we propose to address this problem by training a parameter-efficient score adaptation model to re-calibrate neural link prediction scores: this new component is trained on complex queries by back-propagating through the complex query-answering process. Our method, CQD$^{A}$, produces significantly more accurate results than current state-of-the-art methods, improving from $34.4$ to $35.1$ Mean Reciprocal Rank values averaged across all datasets and query types while using $\leq 35\%$ of the available training query types. We further show that CQD$^{A}$ is data-efficient, achieving competitive results with only $1\%$ of the training data, and robust in out-of-domain evaluations.
    Active Policy Improvement from Multiple Black-box Oracles. (arXiv:2306.10259v2 [cs.LG] UPDATED)
    Reinforcement learning (RL) has made significant strides in various complex domains. However, identifying an effective policy via RL often necessitates extensive exploration. Imitation learning aims to mitigate this issue by using expert demonstrations to guide exploration. In real-world scenarios, one often has access to multiple suboptimal black-box experts, rather than a single optimal oracle. These experts do not universally outperform each other across all states, presenting a challenge in actively deciding which oracle to use and in which state. We introduce MAPS and MAPS-SE, a class of policy improvement algorithms that perform imitation learning from multiple suboptimal oracles. In particular, MAPS actively selects which of the oracles to imitate and improve their value function estimates, and MAPS-SE additionally leverages an active state exploration criterion to determine which states one should explore. We provide a comprehensive theoretical analysis and demonstrate that MAPS and MAPS-SE enjoy sample efficiency advantage over the state-of-the-art policy improvement algorithms. Empirical results show that MAPS-SE significantly accelerates policy optimization via state-wise imitation learning from multiple oracles across a broad spectrum of control tasks in the DeepMind Control Suite. Our code is publicly available at: https://github.com/ripl/maps.
    How accurate are existing land cover maps for agriculture in Sub-Saharan Africa?. (arXiv:2307.02575v1 [cs.LG])
    Satellite Earth observations (EO) can provide affordable and timely information for assessing crop conditions and food production. Such monitoring systems are essential in Africa, where there is high food insecurity and sparse agricultural statistics. EO-based monitoring systems require accurate cropland maps to provide information about croplands, but there is a lack of data to determine which of the many available land cover maps most accurately identify cropland in African countries. This study provides a quantitative evaluation and intercomparison of 11 publicly available land cover maps to assess their suitability for cropland classification and EO-based agriculture monitoring in Africa using statistically rigorous reference datasets from 8 countries. We hope the results of this study will help users determine the most suitable map for their needs and encourage future work to focus on resolving inconsistencies between maps and improving accuracy in low-accuracy regions.
    Few-Shot Personalized Saliency Prediction Using Tensor Regression for Preserving Structural Global Information. (arXiv:2307.02799v1 [eess.IV])
    This paper presents a few-shot personalized saliency prediction using tensor-to-matrix regression for preserving the structural global information of personalized saliency maps (PSMs). In contrast to a general saliency map, a PSM has been great potential since its map indicates the person-specific visual attention that is useful for obtaining individual visual preferences from heterogeneity of gazed areas. The PSM prediction is needed for acquiring the PSM for the unseen image, but its prediction is still a challenging task due to the complexity of individual gaze patterns. For recognizing individual gaze patterns from the limited amount of eye-tracking data, the previous methods adopt the similarity of gaze tendency between persons. However, in the previous methods, the PSMs are vectorized for the prediction model. In this way, the structural global information of the PSMs corresponding to the image is ignored. For automatically revealing the relationship between PSMs, we focus on the tensor-based regression model that can preserve the structural information of PSMs, and realize the improvement of the prediction accuracy. In the experimental results, we confirm the proposed method including the tensor-based regression outperforms the comparative methods.
    Duality in Multi-View Restricted Kernel Machines. (arXiv:2305.17251v2 [cs.LG] UPDATED)
    We propose a unifying setting that combines existing restricted kernel machine methods into a single primal-dual multi-view framework for kernel principal component analysis in both supervised and unsupervised settings. We derive the primal and dual representations of the framework and relate different training and inference algorithms from a theoretical perspective. We show how to achieve full equivalence in primal and dual formulations by rescaling primal variables. Finally, we experimentally validate the equivalence and provide insight into the relationships between different methods on a number of time series data sets by recursively forecasting unseen test data and visualizing the learned features.
    Multi-gauge Hydrological Variational Data Assimilation: Regionalization Learning with Spatial Gradients using Multilayer Perceptron and Bayesian-Guided Multivariate Regression. (arXiv:2307.02497v1 [cs.LG])
    Tackling the difficult problem of estimating spatially distributed hydrological parameters, especially for floods on ungauged watercourses, this contribution presents a novel seamless regionalization technique for learning complex regional transfer functions designed for high-resolution hydrological models. The transfer functions rely on: (i) a multilayer perceptron enabling a seamless flow of gradient computation to employ machine learning optimization algorithms, or (ii) a multivariate regression mapping optimized by variational data assimilation algorithms and guided by Bayesian estimation, addressing the equifinality issue of feasible solutions. The approach involves incorporating the inferable regionalization mappings into a differentiable hydrological model and optimizing a cost function computed on multi-gauge data with accurate adjoint-based spatially distributed gradients.
    Balanced Filtering via Non-Disclosive Proxies. (arXiv:2306.15083v2 [cs.LG] UPDATED)
    We study the problem of non-disclosively collecting a sample of data that is balanced with respect to sensitive groups when group membership is unavailable or prohibited from use at collection time. Specifically, our collection mechanism does not reveal significantly more about group membership of any individual sample than can be ascertained from base rates alone. To do this, we adopt a fairness pipeline perspective, in which a learner can use a small set of labeled data to train a proxy function that can later be used for this filtering task. We then associate the range of the proxy function with sampling probabilities; given a new candidate, we classify it using our proxy function, and then select it for our sample with probability proportional to the sampling probability corresponding to its proxy classification. Importantly, we require that the proxy classification itself not reveal significant information about the sensitive group membership of any individual sample (i.e., it should be sufficiently non-disclosive). We show that under modest algorithmic assumptions, we find such a proxy in a sample- and oracle-efficient manner. Finally, we experimentally evaluate our algorithm and analyze generalization properties.
    Stability of Q-Learning Through Design and Optimism. (arXiv:2307.02632v1 [cs.LG])
    Q-learning has become an important part of the reinforcement learning toolkit since its introduction in the dissertation of Chris Watkins in the 1980s. The purpose of this paper is in part a tutorial on stochastic approximation and Q-learning, providing details regarding the INFORMS APS inaugural Applied Probability Trust Plenary Lecture, presented in Nancy France, June 2023. The paper also presents new approaches to ensure stability and potentially accelerated convergence for these algorithms, and stochastic approximation in other settings. Two contributions are entirely new: 1. Stability of Q-learning with linear function approximation has been an open topic for research for over three decades. It is shown that with appropriate optimistic training in the form of a modified Gibbs policy, there exists a solution to the projected Bellman equation, and the algorithm is stable (in terms of bounded parameter estimates). Convergence remains one of many open topics for research. 2. The new Zap Zero algorithm is designed to approximate the Newton-Raphson flow without matrix inversion. It is stable and convergent under mild assumptions on the mean flow vector field for the algorithm, and compatible statistical assumption on an underlying Markov chain. The algorithm is a general approach to stochastic approximation which in particular applies to Q-learning with "oblivious" training even with non-linear function approximation.
    Origin-Destination Travel Time Oracle for Map-based Services. (arXiv:2307.03048v1 [cs.LG])
    Given an origin (O), a destination (D), and a departure time (T), an Origin-Destination (OD) travel time oracle~(ODT-Oracle) returns an estimate of the time it takes to travel from O to D when departing at T. ODT-Oracles serve important purposes in map-based services. To enable the construction of such oracles, we provide a travel-time estimation (TTE) solution that leverages historical trajectories to estimate time-varying travel times for OD pairs. The problem is complicated by the fact that multiple historical trajectories with different travel times may connect an OD pair, while trajectories may vary from one another. To solve the problem, it is crucial to remove outlier trajectories when doing travel time estimation for future queries. We propose a novel, two-stage framework called Diffusion-based Origin-destination Travel Time Estimation (DOT), that solves the problem. First, DOT employs a conditioned Pixelated Trajectories (PiT) denoiser that enables building a diffusion-based PiT inference process by learning correlations between OD pairs and historical trajectories. Specifically, given an OD pair and a departure time, we aim to infer a PiT. Next, DOT encompasses a Masked Vision Transformer~(MViT) that effectively and efficiently estimates a travel time based on the inferred PiT. We report on extensive experiments on two real-world datasets that offer evidence that DOT is capable of outperforming baseline methods in terms of accuracy, scalability, and explainability.
    Learning Curves for Heterogeneous Feature-Subsampled Ridge Ensembles. (arXiv:2307.03176v1 [stat.ML])
    Feature bagging is a well-established ensembling method which aims to reduce prediction variance by training estimators in an ensemble on random subsamples or projections of features. Typically, ensembles are chosen to be homogeneous, in the sense the the number of feature dimensions available to an estimator is uniform across the ensemble. Here, we introduce heterogeneous feature ensembling, with estimators built on varying number of feature dimensions, and consider its performance in a linear regression setting. We study an ensemble of linear predictors, each fit using ridge regression on a subset of the available features. We allow the number of features included in these subsets to vary. Using the replica trick from statistical physics, we derive learning curves for ridge ensembles with deterministic linear masks. We obtain explicit expressions for the learning curves in the case of equicorrelated data with an isotropic feature noise. Using the derived expressions, we investigate the effect of subsampling and ensembling, finding sharp transitions in the optimal ensembling strategy in the parameter space of noise level, data correlations, and data-task alignment. Finally, we suggest variable-dimension feature bagging as a strategy to mitigate double descent for robust machine learning in practice.
    Wasserstein Auto-Encoders of Merge Trees (and Persistence Diagrams). (arXiv:2307.02509v1 [cs.LG])
    This paper presents a computational framework for the Wasserstein auto-encoding of merge trees (MT-WAE), a novel extension of the classical auto-encoder neural network architecture to the Wasserstein metric space of merge trees. In contrast to traditional auto-encoders which operate on vectorized data, our formulation explicitly manipulates merge trees on their associated metric space at each layer of the network, resulting in superior accuracy and interpretability. Our novel neural network approach can be interpreted as a non-linear generalization of previous linear attempts [65] at merge tree encoding. It also trivially extends to persistence diagrams. Extensive experiments on public ensembles demonstrate the efficiency of our algorithms, with MT-WAE computations in the orders of minutes on average. We show the utility of our contributions in two applications adapted from previous work on merge tree encoding [65]. First, we apply MT-WAE to data reduction and reliably compress merge trees by concisely representing them with their coordinates in the final layer of our auto-encoder. Second, we document an application to dimensionality reduction, by exploiting the latent space of our auto-encoder, for the visual analysis of ensemble data. We illustrate the versatility of our framework by introducing two penalty terms, to help preserve in the latent space both the Wasserstein distances between merge trees, as well as their clusters. In both applications, quantitative experiments assess the relevance of our framework. Finally, we provide a C++ implementation that can be used for reproducibility.
    Temporal Difference Learning for High-Dimensional PIDEs with Jumps. (arXiv:2307.02766v1 [math.NA])
    In this paper, we propose a deep learning framework for solving high-dimensional partial integro-differential equations (PIDEs) based on the temporal difference learning. We introduce a set of Levy processes and construct a corresponding reinforcement learning model. To simulate the entire process, we use deep neural networks to represent the solutions and non-local terms of the equations. Subsequently, we train the networks using the temporal difference error, termination condition, and properties of the non-local terms as the loss function. The relative error of the method reaches O(10^{-3}) in 100-dimensional experiments and O(10^{-4}) in one-dimensional pure jump problems. Additionally, our method demonstrates the advantages of low computational cost and robustness, making it well-suited for addressing problems with different forms and intensities of jumps.
    A Privacy-Preserving Walk in the Latent Space of Generative Models for Medical Applications. (arXiv:2307.02984v1 [cs.LG])
    Generative Adversarial Networks (GANs) have demonstrated their ability to generate synthetic samples that match a target distribution. However, from a privacy perspective, using GANs as a proxy for data sharing is not a safe solution, as they tend to embed near-duplicates of real samples in the latent space. Recent works, inspired by k-anonymity principles, address this issue through sample aggregation in the latent space, with the drawback of reducing the dataset by a factor of k. Our work aims to mitigate this problem by proposing a latent space navigation strategy able to generate diverse synthetic samples that may support effective training of deep models, while addressing privacy concerns in a principled way. Our approach leverages an auxiliary identity classifier as a guide to non-linearly walk between points in the latent space, minimizing the risk of collision with near-duplicates of real samples. We empirically demonstrate that, given any random pair of points in the latent space, our walking strategy is safer than linear interpolation. We then test our path-finding strategy combined to k-same methods and demonstrate, on two benchmarks for tuberculosis and diabetic retinopathy classification, that training a model using samples generated by our approach mitigate drops in performance, while keeping privacy preservation.
    Diffusion Models for Computational Design at the Example of Floor Plans. (arXiv:2307.02511v1 [cs.LG])
    AI Image generators based on diffusion models are widely discussed recently for their capability to create images from simple text prompts. But, for practical use in civil engineering they need to be able to create specific construction plans for given constraints. Within this paper we explore the capabilities of those diffusion-based AI generators for computational design at the example of floor plans and identify their current limitation. We explain how the diffusion-models work and propose new diffusion models with improved semantic encoding. In several experiments we show that we can improve validity of generated floor plans from 6% to 90% and query performance for different examples. We identify short comings and derive future research challenges of those models and discuss the need to combine diffusion models with building information modelling. With this we provide key insights into the current state and future directions for diffusion models in civil engineering.
    Quantum Solutions to the Privacy vs. Utility Tradeoff. (arXiv:2307.03118v1 [quant-ph])
    In this work, we propose a novel architecture (and several variants thereof) based on quantum cryptographic primitives with provable privacy and security guarantees regarding membership inference attacks on generative models. Our architecture can be used on top of any existing classical or quantum generative models. We argue that the use of quantum gates associated with unitary operators provides inherent advantages compared to standard Differential Privacy based techniques for establishing guaranteed security from all polynomial-time adversaries.
    REAL: A Representative Error-Driven Approach for Active Learning. (arXiv:2307.00968v2 [cs.LG] UPDATED)
    Given a limited labeling budget, active learning (AL) aims to sample the most informative instances from an unlabeled pool to acquire labels for subsequent model training. To achieve this, AL typically measures the informativeness of unlabeled instances based on uncertainty and diversity. However, it does not consider erroneous instances with their neighborhood error density, which have great potential to improve the model performance. To address this limitation, we propose $REAL$, a novel approach to select data instances with $\underline{R}$epresentative $\underline{E}$rrors for $\underline{A}$ctive $\underline{L}$earning. It identifies minority predictions as \emph{pseudo errors} within a cluster and allocates an adaptive sampling budget for the cluster based on estimated error density. Extensive experiments on five text classification datasets demonstrate that $REAL$ consistently outperforms all best-performing baselines regarding accuracy and F1-macro scores across a wide range of hyperparameter settings. Our analysis also shows that $REAL$ selects the most representative pseudo errors that match the distribution of ground-truth errors along the decision boundary. Our code is publicly available at https://github.com/withchencheng/ECML_PKDD_23_Real.
    GIT: Detecting Uncertainty, Out-Of-Distribution and Adversarial Samples using Gradients and Invariance Transformations. (arXiv:2307.02672v1 [cs.LG])
    Deep neural networks tend to make overconfident predictions and often require additional detectors for misclassifications, particularly for safety-critical applications. Existing detection methods usually only focus on adversarial attacks or out-of-distribution samples as reasons for false predictions. However, generalization errors occur due to diverse reasons often related to poorly learning relevant invariances. We therefore propose GIT, a holistic approach for the detection of generalization errors that combines the usage of gradient information and invariance transformations. The invariance transformations are designed to shift misclassified samples back into the generalization area of the neural network, while the gradient information measures the contradiction between the initial prediction and the corresponding inherent computations of the neural network using the transformed sample. Our experiments demonstrate the superior performance of GIT compared to the state-of-the-art on a variety of network architectures, problem setups and perturbation types.
    A Finite-Particle Convergence Rate for Stein Variational Gradient Descent. (arXiv:2211.09721v4 [cs.LG] UPDATED)
    We provide the first finite-particle convergence rate for Stein variational gradient descent (SVGD), a popular algorithm for approximating a probability distribution with a collection of particles. Specifically, whenever the target distribution is sub-Gaussian with a Lipschitz score, SVGD with n particles and an appropriate step size sequence drives the kernel Stein discrepancy to zero at an order 1/sqrt(log log n) rate. We suspect that the dependence on n can be improved, and we hope that our explicit, non-asymptotic proof strategy will serve as a template for future refinements.
    Parameter-Efficient Fine-Tuning of LLaMA for the Clinical Domain. (arXiv:2307.03042v1 [cs.CL])
    Adapting pretrained language models to novel domains, such as clinical applications, traditionally involves retraining their entire set of parameters. However, this approach is increasingly proven to be impractical owing to the substantial computational requirements associated with training such large language models. To address this issue, Parameter-Efficient Fine-Tuning (PEFT) techniques offer a viable solution by selectively fine-tuning a small subset of additional parameters, significantly reducing the computational requirements for domain adaptation. In this study, we propose Clinical LLaMA-LoRA, a PEFT adapter layer built upon the open-sourced LLaMA model. Clinical LLaMA-LoRA is trained using clinical notes obtained from the MIMIC-IV database, thereby creating a specialised adapter designed for the clinical domain. Additionally, we propose a two-step PEFT framework which fuses Clinical LLaMA-LoRA with Downstream LLaMA-LoRA, another PEFT adapter specialised for downstream tasks. We evaluate this framework on multiple clinical outcome prediction datasets, comparing it to clinically trained language models. Our proposed framework achieves a state-of-the-art AUROC score averaged across all clinical downstream tasks. We observe substantial improvements of 6-9% AUROC score in the large-scale multilabel classification tasks, such as diagnoses and procedures classification.
    Generalizing Backpropagation for Gradient-Based Interpretability. (arXiv:2307.03056v1 [cs.LG])
    Many popular feature-attribution methods for interpreting deep neural networks rely on computing the gradients of a model's output with respect to its inputs. While these methods can indicate which input features may be important for the model's prediction, they reveal little about the inner workings of the model itself. In this paper, we observe that the gradient computation of a model is a special case of a more general formulation using semirings. This observation allows us to generalize the backpropagation algorithm to efficiently compute other interpretable statistics about the gradient graph of a neural network, such as the highest-weighted path and entropy. We implement this generalized algorithm, evaluate it on synthetic datasets to better understand the statistics it computes, and apply it to study BERT's behavior on the subject-verb number agreement task (SVA). With this method, we (a) validate that the amount of gradient flow through a component of a model reflects its importance to a prediction and (b) for SVA, identify which pathways of the self-attention mechanism are most important.
    CPDG: A Contrastive Pre-Training Method for Dynamic Graph Neural Networks. (arXiv:2307.02813v1 [cs.LG])
    Dynamic graph data mining has gained popularity in recent years due to the rich information contained in dynamic graphs and their widespread use in the real world. Despite the advances in dynamic graph neural networks (DGNNs), the rich information and diverse downstream tasks have posed significant difficulties for the practical application of DGNNs in industrial scenarios. To this end, in this paper, we propose to address them by pre-training and present the Contrastive Pre-Training Method for Dynamic Graph Neural Networks (CPDG). CPDG tackles the challenges of pre-training for DGNNs, including generalization and long-short term modeling capability, through a flexible structural-temporal subgraph sampler along with structural-temporal contrastive pre-training schemes. Extensive experiments conducted on both large-scale research and industrial dynamic graph datasets show that CPDG outperforms existing methods in dynamic graph pre-training for various downstream tasks under three transfer settings.
    Convex-Concave Min-Max Stackelberg Games. (arXiv:2110.05192v8 [cs.GT] UPDATED)
    Min-max optimization problems (i.e., min-max games) have been attracting a great deal of attention because of their applicability to a wide range of machine learning problems. Although significant progress has been made recently, the literature to date has focused on games with independent strategy sets; little is known about solving games with dependent strategy sets, which can be characterized as min-max Stackelberg games. We introduce two first-order methods that solve a large class of convex-concave min-max Stackelberg games, and show that our methods converge in polynomial time. Min-max Stackelberg games were first studied by Wald, under the posthumous name of Wald's maximin model, a variant of which is the main paradigm used in robust optimization, which means that our methods can likewise solve many convex robust optimization problems. We observe that the computation of competitive equilibria in Fisher markets also comprises a min-max Stackelberg game. Further, we demonstrate the efficacy and efficiency of our algorithms in practice by computing competitive equilibria in Fisher markets with varying utility structures. Our experiments suggest potential ways to extend our theoretical results, by demonstrating how different smoothness properties can affect the convergence rate of our algorithms.
    Modeling Content Creator Incentives on Algorithm-Curated Platforms. (arXiv:2206.13102v2 [cs.GT] UPDATED)
    Content creators compete for user attention. Their reach crucially depends on algorithmic choices made by developers on online platforms. To maximize exposure, many creators adapt strategically, as evidenced by examples like the sprawling search engine optimization industry. This begets competition for the finite user attention pool. We formalize these dynamics in what we call an exposure game, a model of incentives induced by algorithms, including modern factorization and (deep) two-tower architectures. We prove that seemingly innocuous algorithmic choices, e.g., non-negative vs. unconstrained factorization, significantly affect the existence and character of (Nash) equilibria in exposure games. We proffer use of creator behavior models, like exposure games, for an (ex-ante) pre-deployment audit. Such an audit can identify misalignment between desirable and incentivized content, and thus complement post-hoc measures like content filtering and moderation. To this end, we propose tools for numerically finding equilibria in exposure games, and illustrate results of an audit on the MovieLens and LastFM datasets. Among else, we find that the strategically produced content exhibits strong dependence between algorithmic exploration and content diversity, and between model expressivity and bias towards gender-based user and creator groups.
    Distilling Large Vision-Language Model with Out-of-Distribution Generalizability. (arXiv:2307.03135v1 [cs.CV])
    Large vision-language models have achieved outstanding performance, but their size and computational requirements make their deployment on resource-constrained devices and time-sensitive tasks impractical. Model distillation, the process of creating smaller, faster models that maintain the performance of larger models, is a promising direction towards the solution. This paper investigates the distillation of visual representations in large teacher vision-language models into lightweight student models using a small- or mid-scale dataset. Notably, this study focuses on open-vocabulary out-of-distribution (OOD) generalization, a challenging problem that has been overlooked in previous model distillation literature. We propose two principles from vision and language modality perspectives to enhance student's OOD generalization: (1) by better imitating teacher's visual representation space, and carefully promoting better coherence in vision-language alignment with the teacher; (2) by enriching the teacher's language representations with informative and finegrained semantic attributes to effectively distinguish between different labels. We propose several metrics and conduct extensive experiments to investigate their techniques. The results demonstrate significant improvements in zero-shot and few-shot student performance on open-vocabulary out-of-distribution classification, highlighting the effectiveness of our proposed approaches. Our code will be released at https://github.com/xuanlinli17/large_vlm_distillation_ood
    Evaluating the Evaluators: Are Current Few-Shot Learning Benchmarks Fit for Purpose?. (arXiv:2307.02732v1 [cs.LG])
    Numerous benchmarks for Few-Shot Learning have been proposed in the last decade. However all of these benchmarks focus on performance averaged over many tasks, and the question of how to reliably evaluate and tune models trained for individual tasks in this regime has not been addressed. This paper presents the first investigation into task-level evaluation -- a fundamental step when deploying a model. We measure the accuracy of performance estimators in the few-shot setting, consider strategies for model selection, and examine the reasons for the failure of evaluators usually thought of as being robust. We conclude that cross-validation with a low number of folds is the best choice for directly estimating the performance of a model, whereas using bootstrapping or cross validation with a large number of folds is better for model selection purposes. Overall, we find that existing benchmarks for few-shot learning are not designed in such a way that one can get a reliable picture of how effectively methods can be used on individual tasks.
    Fairness in Multi-Task Learning via Wasserstein Barycenters. (arXiv:2306.10155v2 [stat.ML] UPDATED)
    Algorithmic Fairness is an established field in machine learning that aims to reduce biases in data. Recent advances have proposed various methods to ensure fairness in a univariate environment, where the goal is to de-bias a single task. However, extending fairness to a multi-task setting, where more than one objective is optimised using a shared representation, remains underexplored. To bridge this gap, we develop a method that extends the definition of Strong Demographic Parity to multi-task learning using multi-marginal Wasserstein barycenters. Our approach provides a closed form solution for the optimal fair multi-task predictor including both regression and binary classification tasks. We develop a data-driven estimation procedure for the solution and run numerical experiments on both synthetic and real datasets. The empirical results highlight the practical value of our post-processing methodology in promoting fair decision-making.
    Dynamical Isometry based Rigorous Fair Neural Architecture Search. (arXiv:2307.02263v2 [cs.LG] UPDATED)
    Recently, the weight-sharing technique has significantly speeded up the training and evaluation procedure of neural architecture search. However, most existing weight-sharing strategies are solely based on experience or observation, which makes the searching results lack interpretability and rationality. In addition, due to the negligence of fairness, current methods are prone to make misjudgments in module evaluation. To address these problems, we propose a novel neural architecture search algorithm based on dynamical isometry. We use the fix point analysis method in the mean field theory to analyze the dynamics behavior in the steady state random neural network, and how dynamic isometry guarantees the fairness of weight-sharing based NAS. Meanwhile, we prove that our module selection strategy is rigorous fair by estimating the generalization error of all modules with well-conditioned Jacobian. Extensive experiments show that, with the same size, the architecture searched by the proposed method can achieve state-of-the-art top-1 validation accuracy on ImageNet classification. In addition, we demonstrate that our method is able to achieve better and more stable training performance without loss of generality.
    Understanding Uncertainty Sampling. (arXiv:2307.02719v1 [cs.LG])
    Uncertainty sampling is a prevalent active learning algorithm that queries sequentially the annotations of data samples which the current prediction model is uncertain about. However, the usage of uncertainty sampling has been largely heuristic: (i) There is no consensus on the proper definition of "uncertainty" for a specific task under a specific loss; (ii) There is no theoretical guarantee that prescribes a standard protocol to implement the algorithm, for example, how to handle the sequentially arrived annotated data under the framework of optimization algorithms such as stochastic gradient descent. In this work, we systematically examine uncertainty sampling algorithms under both stream-based and pool-based active learning. We propose a notion of equivalent loss which depends on the used uncertainty measure and the original loss function and establish that an uncertainty sampling algorithm essentially optimizes against such an equivalent loss. The perspective verifies the properness of existing uncertainty measures from two aspects: surrogate property and loss convexity. Furthermore, we propose a new notion for designing uncertainty measures called \textit{loss as uncertainty}. The idea is to use the conditional expected loss given the features as the uncertainty measure. Such an uncertainty measure has nice analytical properties and generality to cover both classification and regression problems, which enable us to provide the first generalization bound for uncertainty sampling algorithms under both stream-based and pool-based settings, in the full generality of the underlying model and problem. Lastly, we establish connections between certain variants of the uncertainty sampling algorithms with risk-sensitive objectives and distributional robustness, which can partly explain the advantage of uncertainty sampling algorithms when the sample size is small.
    VerifAI: Verified Generative AI. (arXiv:2307.02796v1 [cs.DB])
    Generative AI has made significant strides, yet concerns about the accuracy and reliability of its outputs continue to grow. Such inaccuracies can have serious consequences such as inaccurate decision-making, the spread of false information, privacy violations, legal liabilities, and more. Although efforts to address these risks are underway, including explainable AI and responsible AI practices such as transparency, privacy protection, bias mitigation, and social and environmental responsibility, misinformation caused by generative AI will remain a significant challenge. We propose that verifying the outputs of generative AI from a data management perspective is an emerging issue for generative AI. This involves analyzing the underlying data from multi-modal data lakes, including text files, tables, and knowledge graphs, and assessing its quality and consistency. By doing so, we can establish a stronger foundation for evaluating the outputs of generative AI models. Such an approach can ensure the correctness of generative AI, promote transparency, and enable decision-making with greater confidence. Our vision is to promote the development of verifiable generative AI and contribute to a more trustworthy and responsible use of AI.
    Gradient Norm Aware Minimization Seeks First-Order Flatness and Improves Generalization. (arXiv:2303.03108v3 [cs.LG] UPDATED)
    Recently, flat minima are proven to be effective for improving generalization and sharpness-aware minimization (SAM) achieves state-of-the-art performance. Yet the current definition of flatness discussed in SAM and its follow-ups are limited to the zeroth-order flatness (i.e., the worst-case loss within a perturbation radius). We show that the zeroth-order flatness can be insufficient to discriminate minima with low generalization error from those with high generalization error both when there is a single minimum or multiple minima within the given perturbation radius. Thus we present first-order flatness, a stronger measure of flatness focusing on the maximal gradient norm within a perturbation radius which bounds both the maximal eigenvalue of Hessian at local minima and the regularization function of SAM. We also present a novel training procedure named Gradient norm Aware Minimization (GAM) to seek minima with uniformly small curvature across all directions. Experimental results show that GAM improves the generalization of models trained with current optimizers such as SGD and AdamW on various datasets and networks. Furthermore, we show that GAM can help SAM find flatter minima and achieve better generalization.
    Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation Learning. (arXiv:2303.10135v3 [cs.RO] UPDATED)
    Automatic Robotic Assembly Sequence Planning (RASP) can significantly improve productivity and resilience in modern manufacturing along with the growing need for greater product customization. One of the main challenges in realizing such automation resides in efficiently finding solutions from a growing number of potential sequences for increasingly complex assemblies. Besides, costly feasibility checks are always required for the robotic system. To address this, we propose a holistic graphical approach including a graph representation called Assembly Graph for product assemblies and a policy architecture, Graph Assembly Processing Network, dubbed GRACE for assembly sequence generation. Secondly, we use GRACE to extract meaningful information from the graph input and predict assembly sequences in a step-by-step manner. In experiments, we show that our approach can predict feasible assembly sequences across product variants of aluminum profiles based on data collected in simulation of a dual-armed robotic system. We further demonstrate that our method is capable of detecting infeasible assemblies, substantially alleviating the undesirable impacts from false predictions, and hence facilitating real-world deployment soon. Code and training data are available at https://github.com/DLR-RM/GRACE.
    SLPerf: a Unified Framework for Benchmarking Split Learning. (arXiv:2304.01502v2 [cs.LG] UPDATED)
    Data privacy concerns has made centralized training of data, which is scattered across silos, infeasible, leading to the need for collaborative learning frameworks. To address that, two prominent frameworks emerged, i.e., federated learning (FL) and split learning (SL). While FL has established various benchmark frameworks and research libraries,SL currently lacks a unified library despite its diversity in terms of label sharing, model aggregation, and cut layer choice. This lack of standardization makes comparing SL paradigms difficult. To address this, we propose SLPerf, a unified research framework and open research library for SL, and conduct extensive experiments on four widely-used datasets under both IID and Non-IID data settings. Our contributions include a comprehensive survey of recently proposed SL paradigms, a detailed benchmark comparison of different SL paradigms in different situations, and rich engineering take-away messages and research insights for improving SL paradigms. SLPerf can facilitate SL algorithm development and fair performance comparisons. The code is available at https://github.com/Rainysponge/Split-learning-Attacks .
    PCL-Indexability and Whittle Index for Restless Bandits with General Observation Models. (arXiv:2307.03034v1 [stat.ML])
    In this paper, we consider a general observation model for restless multi-armed bandit problems. The operation of the player needs to be based on certain feedback mechanism that is error-prone due to resource constraints or environmental or intrinsic noises. By establishing a general probabilistic model for dynamics of feedback/observation, we formulate the problem as a restless bandit with a countable belief state space starting from an arbitrary initial belief (a priori information). We apply the achievable region method with partial conservation law (PCL) to the infinite-state problem and analyze its indexability and priority index (Whittle index). Finally, we propose an approximation process to transform the problem into which the AG algorithm of Ni\~no-Mora and Bertsimas for finite-state problems can be applied to. Numerical experiments show that our algorithm has an excellent performance.
    Memory-efficient NLLB-200: Language-specific Expert Pruning of a Massively Multilingual Machine Translation Model. (arXiv:2212.09811v2 [cs.CL] UPDATED)
    Compared to conventional bilingual translation systems, massively multilingual machine translation is appealing because a single model can translate into multiple languages and benefit from knowledge transfer for low resource languages. On the other hand, massively multilingual models suffer from the curse of multilinguality, unless scaling their size massively, which increases their training and inference costs. Sparse Mixture-of-Experts models are a way to drastically increase model capacity without the need for a proportional amount of computing. The recently released NLLB-200 is an example of such a model. It covers 202 languages but requires at least four 32GB GPUs just for inference. In this work, we propose a pruning method that allows the removal of up to 80\% of experts with a negligible loss in translation quality, which makes it feasible to run the model on a single 32GB GPU. Further analysis suggests that our pruning metrics allow to identify language-specific experts and prune non-relevant experts for a given language pair.
    Intercomparison of Brown Dwarf Model Grids and Atmospheric Retrieval Using Machine Learning. (arXiv:2305.07719v2 [astro-ph.SR] UPDATED)
    Understanding differences between sub-stellar spectral data and models has proven to be a major challenge, especially for self-consistent model grids that are necessary for a thorough investigation of brown dwarf atmospheres. Using the supervised machine learning method of the random forest, we study the information content of 14 previously published model grids of brown dwarfs (from 1997 to 2021). The random forest method allows us to analyze the predictive power of these model grids, as well as interpret data within the framework of Approximate Bayesian Computation (ABC). Our curated dataset includes 3 benchmark brown dwarfs (Gl 570D, {\epsilon} Indi Ba and Bb) as well as a sample of 19 L and T dwarfs; this sample was previously analyzed in Lueber et al. (2022) using traditional Bayesian methods (nested sampling). We find that the effective temperature of a brown dwarf can be robustly predicted independent of the model grid chosen for the interpretation. However, inference of the surface gravity is model-dependent. Specifically, the BT-Settl, Sonora Bobcat and Sonora Cholla model grids tend to predict logg ~3-4 (cgs units) even after data blueward of 1.2 {\mu}m have been disregarded to mitigate for our incomplete knowledge of the shapes of alkali lines. Two major, longstanding challenges associated with understanding the influence of clouds in brown dwarf atmospheres remain: our inability to model them from first principles and also to robustly validate these models.
    When No-Rejection Learning is Optimal for Regression with Rejection. (arXiv:2307.02932v1 [cs.LG])
    Learning with rejection is a prototypical model for studying the interaction between humans and AI on prediction tasks. The model has two components, a predictor and a rejector. Upon the arrival of a sample, the rejector first decides whether to accept it; if accepted, the predictor fulfills the prediction task, and if rejected, the prediction will be deferred to humans. The learning problem requires learning a predictor and a rejector simultaneously. This changes the structure of the conventional loss function and often results in non-convexity and inconsistency issues. For the classification with rejection problem, several works develop surrogate losses for the jointly learning with provable consistency guarantees; in parallel, there has been less work for the regression counterpart. We study the regression with rejection (RwR) problem and investigate the no-rejection learning strategy which treats the RwR problem as a standard regression task to learn the predictor. We establish that the suboptimality of the no-rejection learning strategy observed in the literature can be mitigated by enlarging the function class of the predictor. Then we introduce the truncated loss to single out the learning for the predictor and we show that a consistent surrogate property can be established for the predictor individually in an easier way than for the predictor and the rejector jointly. Our findings advocate for a two-step learning procedure that first uses all the data to learn the predictor and then calibrates the prediction loss for the rejector. It is better aligned with the common intuition that more data samples will lead to a better predictor and it calls for more efforts on a better design of calibration algorithms for learning the rejector. While our discussions mainly focus on the regression problem, the theoretical results and insights generalize to the classification problem as well.
    BaBE: Enhancing Fairness via Estimation of Latent Explaining Variables. (arXiv:2307.02891v1 [cs.LG])
    We consider the problem of unfair discrimination between two groups and propose a pre-processing method to achieve fairness. Corrective methods like statistical parity usually lead to bad accuracy and do not really achieve fairness in situations where there is a correlation between the sensitive attribute S and the legitimate attribute E (explanatory variable) that should determine the decision. To overcome these drawbacks, other notions of fairness have been proposed, in particular, conditional statistical parity and equal opportunity. However, E is often not directly observable in the data, i.e., it is a latent variable. We may observe some other variable Z representing E, but the problem is that Z may also be affected by S, hence Z itself can be biased. To deal with this problem, we propose BaBE (Bayesian Bias Elimination), an approach based on a combination of Bayes inference and the Expectation-Maximization method, to estimate the most likely value of E for a given Z for each group. The decision can then be based directly on the estimated E. We show, by experiments on synthetic and real data sets, that our approach provides a good level of fairness as well as high accuracy.
    Trainable Weight Averaging: A General Approach for Subspace Training. (arXiv:2205.13104v2 [cs.LG] UPDATED)
    Training deep neural networks (DNNs) in low-dimensional subspaces is a promising direction for achieving efficient training and better generalization performance. Previous works extract the subspaces by using random projection or performing dimensionality reduction method on the training trajectory, but these methods can be inefficient or unstable in terms of dimensionality and numerical operations. In this paper, we connect subspace training to weight averaging and propose Trainable Weight Averaging (TWA), a general approach for subspace training that generalizes the previous efforts. TWA is efficient in terms of dimensionality and also easy to use, making it a promising new method for subspace training. We further design an efficient scheme for subspace training to cope with large-scale problems, which allows parallel training across multiple nodes and evenly distributing the memory and computation burden to each node. We apply TWA to efficient neural network training and improving fine-tuning performance tasks to demonstrate the great efficiency and effectiveness of our approach. We conduct extensive experiments that cover various benchmark computer vision and neural language processing tasks with various architectures. The code of implementation is available at https://github.com/nblt/TWA.
    Dynamic Observation Policies in Observation Cost-Sensitive Reinforcement Learning. (arXiv:2307.02620v1 [cs.LG])
    Reinforcement learning (RL) has been shown to learn sophisticated control policies for complex tasks including games, robotics, heating and cooling systems and text generation. The action-perception cycle in RL, however, generally assumes that a measurement of the state of the environment is available at each time step without a cost. In applications such as deep-sea and planetary robot exploration, materials design and medicine, however, there can be a high cost associated with measuring, or even approximating, the state of the environment. In this paper, we survey the recently growing literature that adopts the perspective that an RL agent might not need, or even want, a costly measurement at each time step. Within this context, we propose the Deep Dynamic Multi-Step Observationless Agent (DMSOA), contrast it with the literature and empirically evaluate it on OpenAI gym and Atari Pong environments. Our results, show that DMSOA learns a better policy with fewer decision steps and measurements than the considered alternative from the literature.
    T-MARS: Improving Visual Representations by Circumventing Text Feature Learning. (arXiv:2307.03132v1 [cs.CV])
    Large web-sourced multimodal datasets have powered a slew of new methods for learning general-purpose visual representations, advancing the state of the art in computer vision and revolutionizing zero- and few-shot recognition. One crucial decision facing practitioners is how, if at all, to curate these ever-larger datasets. For example, the creators of the LAION-5B dataset chose to retain only image-caption pairs whose CLIP similarity score exceeded a designated threshold. In this paper, we propose a new state-of-the-art data filtering approach motivated by our observation that nearly 40% of LAION's images contain text that overlaps significantly with the caption. Intuitively, such data could be wasteful as it incentivizes models to perform optical character recognition rather than learning visual features. However, naively removing all such data could also be wasteful, as it throws away images that contain visual features (in addition to overlapping text). Our simple and scalable approach, T-MARS (Text Masking and Re-Scoring), filters out only those pairs where the text dominates the remaining visual features -- by first masking out the text and then filtering out those with a low CLIP similarity score of the masked image. Experimentally, T-MARS outperforms the top-ranked method on the "medium scale" of DataComp (a data filtering benchmark) by a margin of 6.5% on ImageNet and 4.7% on VTAB. Additionally, our systematic evaluation on various data pool sizes from 2M to 64M shows that the accuracy gains enjoyed by T-MARS linearly increase as data and compute are scaled exponentially. Code is available at https://github.com/locuslab/T-MARS.
    Approximating the Shapley Value without Marginal Contributions. (arXiv:2302.00736v2 [cs.LG] UPDATED)
    The Shapley value is arguably the most popular approach for assigning a meaningful contribution value to players in a cooperative game, which has recently been used intensively in explainable artificial intelligence. The meaningfulness is due to axiomatic properties that only the Shapley value satisfies, which, however, comes at the expense of an exact computation growing exponentially with the number of agents. Accordingly, a number of works are devoted to the efficient approximation of the Shapley values, most of them revolve around the notion of an agent's marginal contribution. In this paper, we propose with SVARM and Stratified SVARM two parameter-free and domain-independent approximation algorithms based on a representation of the Shapley value detached from the notion of marginal contributions. We prove unmatched theoretical guarantees regarding their approximation quality and provide empirical results including synthetic games as well as common explainability use cases comparing ourselves with state-of-the-art methods.
    Learning to reconstruct the bubble distribution with conductivity maps using Invertible Neural Networks and Error Diffusion. (arXiv:2307.02496v1 [eess.IV])
    Electrolysis is crucial for eco-friendly hydrogen production, but gas bubbles generated during the process hinder reactions, reduce cell efficiency, and increase energy consumption. Additionally, these gas bubbles cause changes in the conductivity inside the cell, resulting in corresponding variations in the induced magnetic field around the cell. Therefore, measuring these gas bubble-induced magnetic field fluctuations using external magnetic sensors and solving the inverse problem of Biot-Savart Law allows for estimating the conductivity in the cell and, thus, bubble size and location. However, determining high-resolution conductivity maps from only a few induced magnetic field measurements is an ill-posed inverse problem. To overcome this, we exploit Invertible Neural Networks (INNs) to reconstruct the conductivity field. Our qualitative results and quantitative evaluation using random error diffusion show that INN achieves far superior performance compared to Tikhonov regularization.
    Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations. (arXiv:2206.04779v3 [cs.LG] UPDATED)
    Offline reinforcement learning has shown great promise in leveraging large pre-collected datasets for policy learning, allowing agents to forgo often-expensive online data collection. However, offline reinforcement learning from visual observations with continuous action spaces remains under-explored, with a limited understanding of the key challenges in this complex domain. In this paper, we establish simple baselines for continuous control in the visual domain and introduce a suite of benchmarking tasks for offline reinforcement learning from visual observations designed to better represent the data distributions present in real-world offline RL problems and guided by a set of desiderata for offline RL from visual observations, including robustness to visual distractions and visually identifiable changes in dynamics. Using this suite of benchmarking tasks, we show that simple modifications to two popular vision-based online reinforcement learning algorithms, DreamerV2 and DrQ-v2, suffice to outperform existing offline RL methods and establish competitive baselines for continuous control in the visual domain. We rigorously evaluate these algorithms and perform an empirical evaluation of the differences between state-of-the-art model-based and model-free offline RL methods for continuous control from visual observations. All code and data used in this evaluation are open-sourced to facilitate progress in this domain.
    Hybrid Ground-State Quantum Algorithms based on Neural Schr\"odinger Forging. (arXiv:2307.02633v1 [quant-ph])
    Entanglement forging based variational algorithms leverage the bi-partition of quantum systems for addressing ground state problems. The primary limitation of these approaches lies in the exponential summation required over the numerous potential basis states, or bitstrings, when performing the Schmidt decomposition of the whole system. To overcome this challenge, we propose a new method for entanglement forging employing generative neural networks to identify the most pertinent bitstrings, eliminating the need for the exponential sum. Through empirical demonstrations on systems of increasing complexity, we show that the proposed algorithm achieves comparable or superior performance compared to the existing standard implementation of entanglement forging. Moreover, by controlling the amount of required resources, this scheme can be applied to larger, as well as non permutation invariant systems, where the latter constraint is associated with the Heisenberg forging procedure. We substantiate our findings through numerical simulations conducted on spins models exhibiting one-dimensional ring, two-dimensional triangular lattice topologies, and nuclear shell model configurations.
    Scaling In-Context Demonstrations with Structured Attention. (arXiv:2307.02690v1 [cs.CL])
    The recent surge of large language models (LLMs) highlights their ability to perform in-context learning, i.e., "learning" to perform a task from a few demonstrations in the context without any parameter updates. However, their capabilities of in-context learning are limited by the model architecture: 1) the use of demonstrations is constrained by a maximum sentence length due to positional embeddings; 2) the quadratic complexity of attention hinders users from using more demonstrations efficiently; 3) LLMs are shown to be sensitive to the order of the demonstrations. In this work, we tackle these challenges by proposing a better architectural design for in-context learning. We propose SAICL (Structured Attention for In-Context Learning), which replaces the full-attention by a structured attention mechanism designed for in-context learning, and removes unnecessary dependencies between individual demonstrations, while making the model invariant to the permutation of demonstrations. We evaluate SAICL in a meta-training framework and show that SAICL achieves comparable or better performance than full attention while obtaining up to 3.4x inference speed-up. SAICL also consistently outperforms a strong Fusion-in-Decoder (FiD) baseline which processes each demonstration independently. Finally, thanks to its linear nature, we demonstrate that SAICL can easily scale to hundreds of demonstrations with continuous performance gains with scaling.
    A Detailed Study of Interpretability of Deep Neural Network based Top Taggers. (arXiv:2210.04371v4 [hep-ex] UPDATED)
    Recent developments in the methods of explainable AI (XAI) allow researchers to explore the inner workings of deep neural networks (DNNs), revealing crucial information about input-output relationships and realizing how data connects with machine learning models. In this paper we explore interpretability of DNN models designed to identify jets coming from top quark decay in high energy proton-proton collisions at the Large Hadron Collider (LHC). We review a subset of existing top tagger models and explore different quantitative methods to identify which features play the most important roles in identifying the top jets. We also investigate how and why feature importance varies across different XAI metrics, how correlations among features impact their explainability, and how latent space representations encode information as well as correlate with physically meaningful quantities. Our studies uncover some major pitfalls of existing XAI methods and illustrate how they can be overcome to obtain consistent and meaningful interpretation of these models. We additionally illustrate the activity of hidden layers as Neural Activation Pattern (NAP) diagrams and demonstrate how they can be used to understand how DNNs relay information across the layers and how this understanding can help to make such models significantly simpler by allowing effective model reoptimization and hyperparameter tuning. These studies not only facilitate a methodological approach to interpreting models but also unveil new insights about what these models learn. Incorporating these observations into augmented model design, we propose the Particle Flow Interaction Network (PFIN) model and demonstrate how interpretability-inspired model augmentation can improve top tagging performance.
    FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models. (arXiv:2304.13407v3 [cs.LG] UPDATED)
    In a vertical federated learning (VFL) system consisting of a central server and many distributed clients, the training data are vertically partitioned such that different features are privately stored on different clients. The problem of split VFL is to train a model split between the server and the clients. This paper aims to address two major challenges in split VFL: 1) performance degradation due to straggling clients during training; and 2) data and model privacy leakage from clients' uploaded data embeddings. We propose FedVS to simultaneously address these two challenges. The key idea of FedVS is to design secret sharing schemes for the local data and models, such that information-theoretical privacy against colluding clients and curious server is guaranteed, and the aggregation of all clients' embeddings is reconstructed losslessly, via decrypting computation shares from the non-straggling clients. Extensive experiments on various types of VFL datasets (including tabular, CV, and multi-view) demonstrate the universal advantages of FedVS in straggler mitigation and privacy protection over baseline protocols.
    An explainable model to support the decision about the therapy protocol for AML. (arXiv:2307.02631v1 [cs.LG])
    Acute Myeloid Leukemia (AML) is one of the most aggressive types of hematological neoplasm. To support the specialists' decision about the appropriate therapy, patients with AML receive a prognostic of outcomes according to their cytogenetic and molecular characteristics, often divided into three risk categories: favorable, intermediate, and adverse. However, the current risk classification has known problems, such as the heterogeneity between patients of the same risk group and no clear definition of the intermediate risk category. Moreover, as most patients with AML receive an intermediate-risk classification, specialists often demand other tests and analyses, leading to delayed treatment and worsening of the patient's clinical condition. This paper presents the data analysis and an explainable machine-learning model to support the decision about the most appropriate therapy protocol according to the patient's survival prediction. In addition to the prediction model being explainable, the results obtained are promising and indicate that it is possible to use it to support the specialists' decisions safely. Most importantly, the findings offered in this study have the potential to open new avenues of research toward better treatments and prognostic markers.
    Can Domain Adaptation Improve Accuracy and Fairness of Skin Lesion Classification?. (arXiv:2307.03157v1 [cs.CV])
    Deep learning-based diagnostic system has demonstrated potential in classifying skin cancer conditions when labeled training example are abundant. However, skin lesion analysis often suffers from a scarcity of labeled data, hindering the development of an accurate and reliable diagnostic system. In this work, we leverage multiple skin lesion datasets and investigate the feasibility of various unsupervised domain adaptation (UDA) methods in binary and multi-class skin lesion classification. In particular, we assess three UDA training schemes: single-, combined-, and multi-source. Our experiment results show that UDA is effective in binary classification, with further improvement being observed when imbalance is mitigated. In multi-class task, its performance is less prominent, and imbalance problem again needs to be addressed to achieve above-baseline accuracy. Through our quantitative analysis, we find that the test error of multi-class tasks is strongly correlated with label shift, and feature-level UDA methods have limitations when handling imbalanced datasets. Finally, our study reveals that UDA can effectively reduce bias against minority groups and promote fairness, even without the explicit use of fairness-focused techniques.
    An AI tool for automated analysis of large-scale unstructured clinical cine CMR databases. (arXiv:2206.08137v2 [eess.IV] UPDATED)
    Artificial intelligence (AI) techniques have been proposed for automating analysis of short axis (SAX) cine cardiac magnetic resonance (CMR), but no CMR analysis tool exists to automatically analyse large (unstructured) clinical CMR datasets. We develop and validate a robust AI tool for start-to-end automatic quantification of cardiac function from SAX cine CMR in large clinical databases. Our pipeline for processing and analysing CMR databases includes automated steps to identify the correct data, robust image pre-processing, an AI algorithm for biventricular segmentation of SAX CMR and estimation of functional biomarkers, and automated post-analysis quality control to detect and correct errors. The segmentation algorithm was trained on 2793 CMR scans from two NHS hospitals and validated on additional cases from this dataset (n=414) and five external datasets (n=6888), including scans of patients with a range of diseases acquired at 12 different centres using CMR scanners from all major vendors. Median absolute errors in cardiac biomarkers were within the range of inter-observer variability: <8.4mL (left ventricle volume), <9.2mL (right ventricle volume), <13.3g (left ventricular mass), and <5.9% (ejection fraction) across all datasets. Stratification of cases according to phenotypes of cardiac disease and scanner vendors showed good performance across all groups. We show that our proposed tool, which combines image pre-processing steps, a domain-generalisable AI algorithm trained on a large-scale multi-domain CMR dataset and quality control steps, allows robust analysis of (clinical or research) databases from multiple centres, vendors, and cardiac diseases. This enables translation of our tool for use in fully-automated processing of large multi-centre databases.
    Focused Transformer: Contrastive Training for Context Scaling. (arXiv:2307.03170v1 [cs.CL])
    Large language models have an exceptional capability to incorporate new information in a contextual manner. However, the full potential of such an approach is often restrained due to a limitation in the effective context length. One solution to this issue is to endow an attention layer with access to an external memory, which comprises of (key, value) pairs. Yet, as the number of documents increases, the proportion of relevant keys to irrelevant ones decreases, leading the model to focus more on the irrelevant keys. We identify a significant challenge, dubbed the distraction issue, where keys linked to different semantic values might overlap, making them hard to distinguish. To tackle this problem, we introduce the Focused Transformer (FoT), a technique that employs a training process inspired by contrastive learning. This novel approach enhances the structure of the (key, value) space, enabling an extension of the context length. Our method allows for fine-tuning pre-existing, large-scale models to lengthen their effective context. This is demonstrated by our fine-tuning of $3B$ and $7B$ OpenLLaMA checkpoints. The resulting models, which we name LongLLaMA, exhibit advancements in tasks requiring a long context. We further illustrate that our LongLLaMA models adeptly manage a $256 k$ context length for passkey retrieval.
    Kernels, Data & Physics. (arXiv:2307.02693v1 [cs.LG])
    Lecture notes from the course given by Professor Julia Kempe at the summer school "Statistical physics of Machine Learning" in Les Houches. The notes discuss the so-called NTK approach to problems in machine learning, which consists of gaining an understanding of generally unsolvable problems by finding a tractable kernel formulation. The notes are mainly focused on practical applications such as data distillation and adversarial robustness, examples of inductive bias are also discussed.
    Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching. (arXiv:2205.03447v7 [cs.AI] UPDATED)
    Ontology Matching (OM) plays an important role in many domains such as bioinformatics and the Semantic Web, and its research is becoming increasingly popular, especially with the application of machine learning (ML) techniques. Although the Ontology Alignment Evaluation Initiative (OAEI) represents an impressive effort for the systematic evaluation of OM systems, it still suffers from several limitations including limited evaluation of subsumption mappings, suboptimal reference mappings, and limited support for the evaluation of ML-based systems. To tackle these limitations, we introduce five new biomedical OM tasks involving ontologies extracted from Mondo and UMLS. Each task includes both equivalence and subsumption matching; the quality of reference mappings is ensured by human curation, ontology pruning, etc.; and a comprehensive evaluation framework is proposed to measure OM performance from various perspectives for both ML-based and non-ML-based OM systems. We report evaluation results for OM systems of different types to demonstrate the usage of these resources, all of which are publicly available as part of the new BioML track at OAEI 2022.
    The Role of Subgroup Separability in Group-Fair Medical Image Classification. (arXiv:2307.02791v1 [cs.CV])
    We investigate performance disparities in deep classifiers. We find that the ability of classifiers to separate individuals into subgroups varies substantially across medical imaging modalities and protected characteristics; crucially, we show that this property is predictive of algorithmic bias. Through theoretical analysis and extensive empirical evaluation, we find a relationship between subgroup separability, subgroup disparities, and performance degradation when models are trained on data with systematic bias such as underdiagnosis. Our findings shed new light on the question of how models become biased, providing important insights for the development of fair medical imaging AI.
    Learning to Predict Navigational Patterns from Partial Observations. (arXiv:2304.13242v2 [cs.CV] UPDATED)
    Human beings cooperatively navigate rule-constrained environments by adhering to mutually known navigational patterns, which may be represented as directional pathways or road lanes. Inferring these navigational patterns from incompletely observed environments is required for intelligent mobile robots operating in unmapped locations. However, algorithmically defining these navigational patterns is nontrivial. This paper presents the first self-supervised learning (SSL) method for learning to infer navigational patterns in real-world environments from partial observations only. We explain how geometric data augmentation, predictive world modeling, and an information-theoretic regularizer enables our model to predict an unbiased local directional soft lane probability (DSLP) field in the limit of infinite data. We demonstrate how to infer global navigational patterns by fitting a maximum likelihood graph to the DSLP field. Experiments show that our SSL model outperforms two SOTA supervised lane graph prediction models on the nuScenes dataset. We propose our SSL method as a scalable and interpretable continual learning paradigm for navigation by perception. Code is available at https://github.com/robin-karlsson0/dslp.
    Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance. (arXiv:2307.03119v1 [cs.AI])
    Order execution is a fundamental task in quantitative finance, aiming at finishing acquisition or liquidation for a number of trading orders of the specific assets. Recent advance in model-free reinforcement learning (RL) provides a data-driven solution to the order execution problem. However, the existing works always optimize execution for an individual order, overlooking the practice that multiple orders are specified to execute simultaneously, resulting in suboptimality and bias. In this paper, we first present a multi-agent RL (MARL) method for multi-order execution considering practical constraints. Specifically, we treat every agent as an individual operator to trade one specific order, while keeping communicating with each other and collaborating for maximizing the overall profits. Nevertheless, the existing MARL algorithms often incorporate communication among agents by exchanging only the information of their partial observations, which is inefficient in complicated financial market. To improve collaboration, we then propose a learnable multi-round communication protocol, for the agents communicating the intended actions with each other and refining accordingly. It is optimized through a novel action value attribution method which is provably consistent with the original learning objective yet more efficient. The experiments on the data from two real-world markets have illustrated superior performance with significantly better collaboration effectiveness achieved by our method.
    An Automatic Guidance and Quality Assessment System for Doppler Imaging of Umbilical Artery. (arXiv:2304.05463v2 [eess.IV] UPDATED)
    Examination of the umbilical artery with Doppler ultrasonography is performed to investigate blood supply to the fetus through the umbilical cord, which is vital for the monitoring of fetal health. Such examination involves several steps that must be performed correctly: identifying suitable sites on the umbilical artery for the measurement, acquiring the blood flow curve in the form of a Doppler spectrum, and ensuring compliance to a set of quality standards. These steps rely heavily on the operator's skill, and the shortage of experienced sonographers has thus created a demand for machine assistance. In this work, we propose an automatic system to fill the gap. By using a modified Faster R-CNN network, we obtain an algorithm that can suggest locations suitable for Doppler measurement. Meanwhile, we have also developed a method for assessment of the Doppler spectrum's quality. The proposed system is validated on 657 images from a national ultrasound screening database, with results demonstrating its potential as a guidance system.
    ZeroFlow: Fast Zero Label Scene Flow via Distillation. (arXiv:2305.10424v4 [cs.CV] UPDATED)
    Scene flow estimation is the task of describing the 3D motion field between temporally successive point clouds. State-of-the-art methods use strong priors and test-time optimization techniques, but require on the order of tens of seconds for large-scale point clouds, making them unusable as computer vision primitives for real-time applications such as open world object detection. Feed forward methods are considerably faster, running on the order of tens to hundreds of milliseconds for large-scale point clouds, but require expensive human supervision. To address both limitations, we propose Scene Flow via Distillation, a simple distillation framework that uses a label-free optimization method to produce pseudo-labels to supervise a feed forward model. Our instantiation of this framework, ZeroFlow, produces scene flow estimates in real-time on large-scale point clouds at quality competitive with state-of-the-art methods while using zero human labels. Notably, at test-time ZeroFlow is over 1000$\times$ faster than label-free state-of-the-art optimization-based methods on large-scale point clouds and over 1000$\times$ cheaper to train on unlabeled data compared to the cost of human annotation of that data. To facilitate research reuse, we release our code, trained model weights, and high quality pseudo-labels for the Argoverse 2 and Waymo Open datasets.
    PathProx: A Proximal Gradient Algorithm for Weight Decay Regularized Deep Neural Networks. (arXiv:2210.03069v4 [cs.LG] UPDATED)
    Weight decay is one of the most widely used forms of regularization in deep learning, and has been shown to improve generalization and robustness. The optimization objective driving weight decay is a sum of losses plus a term proportional to the sum of squared weights. This paper argues that stochastic gradient descent (SGD) may be an inefficient algorithm for this objective. For neural networks with ReLU activations, solutions to the weight decay objective are equivalent to those of a different objective in which the regularization term is instead a sum of products of $\ell_2$ (not squared) norms of the input and output weights associated with each ReLU neuron. This alternative (and effectively equivalent) regularization suggests a novel proximal gradient algorithm for network training. Theory and experiments support the new training approach, showing that it can converge much faster to the sparse solutions it shares with standard weight decay training.
    Through the Fairness Lens: Experimental Analysis and Evaluation of Entity Matching. (arXiv:2307.02726v1 [cs.DB])
    Entity matching (EM) is a challenging problem studied by different communities for over half a century. Algorithmic fairness has also become a timely topic to address machine bias and its societal impacts. Despite extensive research on these two topics, little attention has been paid to the fairness of entity matching. Towards addressing this gap, we perform an extensive experimental evaluation of a variety of EM techniques in this paper. We generated two social datasets from publicly available datasets for the purpose of auditing EM through the lens of fairness. Our findings underscore potential unfairness under two common conditions in real-world societies: (i) when some demographic groups are overrepresented, and (ii) when names are more similar in some groups compared to others. Among our many findings, it is noteworthy to mention that while various fairness definitions are valuable for different settings, due to EM's class imbalance nature, measures such as positive predictive value parity and true positive rate parity are, in general, more capable of revealing EM unfairness.
    DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models. (arXiv:2210.14896v4 [cs.CV] UPDATED)
    With recent advancements in diffusion models, users can generate high-quality images by writing text prompts in natural language. However, generating images with desired details requires proper prompts, and it is often unclear how a model reacts to different prompts or what the best prompts are. To help researchers tackle these critical challenges, we introduce DiffusionDB, the first large-scale text-to-image prompt dataset totaling 6.5TB, containing 14 million images generated by Stable Diffusion, 1.8 million unique prompts, and hyperparameters specified by real users. We analyze the syntactic and semantic characteristics of prompts. We pinpoint specific hyperparameter values and prompt styles that can lead to model errors and present evidence of potentially harmful model usage, such as the generation of misinformation. The unprecedented scale and diversity of this human-actuated dataset provide exciting research opportunities in understanding the interplay between prompts and generative models, detecting deepfakes, and designing human-AI interaction tools to help users more easily use these models. DiffusionDB is publicly available at: https://poloclub.github.io/diffusiondb.
    Loss Functions and Metrics in Deep Learning. A Review. (arXiv:2307.02694v1 [cs.LG])
    One of the essential components of deep learning is the choice of the loss function and performance metrics used to train and evaluate models. This paper reviews the most prevalent loss functions and performance measurements in deep learning. We examine the benefits and limits of each technique and illustrate their application to various deep-learning problems. Our review aims to give a comprehensive picture of the different loss functions and performance indicators used in the most common deep learning tasks and help practitioners choose the best method for their specific task.  ( 2 min )
    Human Inspired Progressive Alignment and Comparative Learning for Grounded Word Acquisition. (arXiv:2307.02615v1 [cs.CL])
    Human language acquisition is an efficient, supervised, and continual process. In this work, we took inspiration from how human babies acquire their first language, and developed a computational process for word acquisition through comparative learning. Motivated by cognitive findings, we generated a small dataset that enables the computation models to compare the similarities and differences of various attributes, learn to filter out and extract the common information for each shared linguistic label. We frame the acquisition of words as not only the information filtration process, but also as representation-symbol mapping. This procedure does not involve a fixed vocabulary size, nor a discriminative objective, and allows the models to continually learn more concepts efficiently. Our results in controlled experiments have shown the potential of this approach for efficient continual learning of grounded words.  ( 2 min )
    Spotting Virus from Satellites: Modeling the Circulation of West Nile Virus Through Graph Neural Networks. (arXiv:2209.05251v2 [cs.CV] UPDATED)
    The occurrence of West Nile Virus (WNV) represents one of the most common mosquito-borne zoonosis viral infections. Its circulation is usually associated with climatic and environmental conditions suitable for vector proliferation and virus replication. On top of that, several statistical models have been developed to shape and forecast WNV circulation: in particular, the recent massive availability of Earth Observation (EO) data, coupled with the continuous advances in the field of Artificial Intelligence, offer valuable opportunities. In this paper, we seek to predict WNV circulation by feeding Deep Neural Networks (DNNs) with satellite images, which have been extensively shown to hold environmental and climatic features. Notably, while previous approaches analyze each geographical site independently, we propose a spatial-aware approach that considers also the characteristics of close sites. Specifically, we build upon Graph Neural Networks (GNN) to aggregate features from neighbouring places, and further extend these modules to consider multiple relations, such as the difference in temperature and soil moisture between two sites, as well as the geographical distance. Moreover, we inject time-related information directly into the model to take into account the seasonality of virus spread. We design an experimental setting that combines satellite images - from Landsat and Sentinel missions - with ground truth observations of WNV circulation in Italy. We show that our proposed Multi-Adjacency Graph Attention Network (MAGAT) consistently leads to higher performance when paired with an appropriate pre-training stage. Finally, we assess the importance of each component of MAGAT in our ablation studies.
    DeepOnto: A Python Package for Ontology Engineering with Deep Learning. (arXiv:2307.03067v1 [cs.AI])
    Applying deep learning techniques, particularly language models (LMs), in ontology engineering has raised widespread attention. However, deep learning frameworks like PyTorch and Tensorflow are predominantly developed for Python programming, while widely-used ontology APIs, such as the OWL API and Jena, are primarily Java-based. To facilitate seamless integration of these frameworks and APIs, we present Deeponto, a Python package designed for ontology engineering. The package encompasses a core ontology processing module founded on the widely-recognised and reliable OWL API, encapsulating its fundamental features in a more "Pythonic" manner and extending its capabilities to include other essential components including reasoning, verbalisation, normalisation, projection, and more. Building on this module, Deeponto offers a suite of tools, resources, and algorithms that support various ontology engineering tasks, such as ontology alignment and completion, by harnessing deep learning methodologies, primarily pre-trained LMs. In this paper, we also demonstrate the practical utility of Deeponto through two use-cases: the Digital Health Coaching in Samsung Research UK and the Bio-ML track of the Ontology Alignment Evaluation Initiative (OAEI).  ( 2 min )
    Denoise Pretraining on Nonequilibrium Molecules for Accurate and Transferable Neural Potentials. (arXiv:2303.02216v2 [cs.LG] UPDATED)
    Recent advances in equivariant graph neural networks (GNNs) have made deep learning amenable to developing fast surrogate models to expensive ab initio quantum mechanics (QM) approaches for molecular potential predictions. However, building accurate and transferable potential models using GNNs remains challenging, as the data is greatly limited by the expensive computational costs and level of theory of QM methods, especially for large and complex molecular systems. In this work, we propose denoise pretraining on nonequilibrium molecular conformations to achieve more accurate and transferable GNN potential predictions. Specifically, atomic coordinates of sampled nonequilibrium conformations are perturbed by random noises and GNNs are pretrained to denoise the perturbed molecular conformations which recovers the original coordinates. Rigorous experiments on multiple benchmarks reveal that pretraining significantly improves the accuracy of neural potentials. Furthermore, we show that the proposed pretraining approach is model-agnostic, as it improves the performance of different invariant and equivariant GNNs. Notably, our models pretrained on small molecules demonstrate remarkable transferability, improving performance when fine-tuned on diverse molecular systems, including different elements, charged molecules, biomolecules, and larger systems. These results highlight the potential for leveraging denoise pretraining approaches to build more generalizable neural potentials for complex molecular systems.  ( 3 min )
    Principal subbundles for dimension reduction. (arXiv:2307.03128v1 [stat.ME])
    In this paper we demonstrate how sub-Riemannian geometry can be used for manifold learning and surface reconstruction by combining local linear approximations of a point cloud to obtain lower dimensional bundles. Local approximations obtained by local PCAs are collected into a rank $k$ tangent subbundle on $\mathbb{R}^d$, $k<d$, which we call a principal subbundle. This determines a sub-Riemannian metric on $\mathbb{R}^d$. We show that sub-Riemannian geodesics with respect to this metric can successfully be applied to a number of important problems, such as: explicit construction of an approximating submanifold $M$, construction of a representation of the point-cloud in $\mathbb{R}^k$, and computation of distances between observations, taking the learned geometry into account. The reconstruction is guaranteed to equal the true submanifold in the limit case where tangent spaces are estimated exactly. Via simulations, we show that the framework is robust when applied to noisy data. Furthermore, the framework generalizes to observations on an a priori known Riemannian manifold.  ( 2 min )
    Generalization Guarantees via Algorithm-dependent Rademacher Complexity. (arXiv:2307.02501v1 [stat.ML])
    Algorithm- and data-dependent generalization bounds are required to explain the generalization behavior of modern machine learning algorithms. In this context, there exists information theoretic generalization bounds that involve (various forms of) mutual information, as well as bounds based on hypothesis set stability. We propose a conceptually related, but technically distinct complexity measure to control generalization error, which is the empirical Rademacher complexity of an algorithm- and data-dependent hypothesis class. Combining standard properties of Rademacher complexity with the convenient structure of this class, we are able to (i) obtain novel bounds based on the finite fractal dimension, which (a) extend previous fractal dimension-type bounds from continuous to finite hypothesis classes, and (b) avoid a mutual information term that was required in prior work; (ii) we greatly simplify the proof of a recent dimension-independent generalization bound for stochastic gradient descent; and (iii) we easily recover results for VC classes and compression schemes, similar to approaches based on conditional mutual information.  ( 2 min )
    A Near-Linear Time Algorithm for the Chamfer Distance. (arXiv:2307.03043v1 [cs.DS])
    For any two point sets $A,B \subset \mathbb{R}^d$ of size up to $n$, the Chamfer distance from $A$ to $B$ is defined as $\text{CH}(A,B)=\sum_{a \in A} \min_{b \in B} d_X(a,b)$, where $d_X$ is the underlying distance measure (e.g., the Euclidean or Manhattan distance). The Chamfer distance is a popular measure of dissimilarity between point clouds, used in many machine learning, computer vision, and graphics applications, and admits a straightforward $O(d n^2)$-time brute force algorithm. Further, the Chamfer distance is often used as a proxy for the more computationally demanding Earth-Mover (Optimal Transport) Distance. However, the \emph{quadratic} dependence on $n$ in the running time makes the naive approach intractable for large datasets. We overcome this bottleneck and present the first $(1+\epsilon)$-approximate algorithm for estimating the Chamfer distance with a near-linear running time. Specifically, our algorithm runs in time $O(nd \log (n)/\varepsilon^2)$ and is implementable. Our experiments demonstrate that it is both accurate and fast on large high-dimensional datasets. We believe that our algorithm will open new avenues for analyzing large high-dimensional point clouds. We also give evidence that if the goal is to \emph{report} a $(1+\varepsilon)$-approximate mapping from $A$ to $B$ (as opposed to just its value), then any sub-quadratic time algorithm is unlikely to exist.  ( 2 min )
    Sampling-based Fast Gradient Rescaling Method for Highly Transferable Adversarial Attacks. (arXiv:2307.02828v1 [cs.CV])
    Deep neural networks are known to be vulnerable to adversarial examples crafted by adding human-imperceptible perturbations to the benign input. After achieving nearly 100% attack success rates in white-box setting, more focus is shifted to black-box attacks, of which the transferability of adversarial examples has gained significant attention. In either case, the common gradient-based methods generally use the sign function to generate perturbations on the gradient update, that offers a roughly correct direction and has gained great success. But little work pays attention to its possible limitation. In this work, we observe that the deviation between the original gradient and the generated noise may lead to inaccurate gradient update estimation and suboptimal solutions for adversarial transferability. To this end, we propose a Sampling-based Fast Gradient Rescaling Method (S-FGRM). Specifically, we use data rescaling to substitute the sign function without extra computational cost. We further propose a Depth First Sampling method to eliminate the fluctuation of rescaling and stabilize the gradient update. Our method could be used in any gradient-based attacks and is extensible to be integrated with various input transformation or ensemble methods to further improve the adversarial transferability. Extensive experiments on the standard ImageNet dataset show that our method could significantly boost the transferability of gradient-based attacks and outperform the state-of-the-art baselines.  ( 2 min )
    Evaluating the Planning and Operational Resilience of Electrical Distribution Systems with Distributed Energy Resources using Complex Network Theory. (arXiv:2208.11543v4 [eess.SY] UPDATED)
    Electrical Distribution Systems are extensively penetrated with Distributed Energy Resources (DERs) to cater the energy demands with the general perception that it enhances the system's resilience. However, integration of DERs may adversely affect the grid operation and affect the system resilience due to various factors like their intermittent availability, dynamics of weather conditions, non-linearity, complexity, number of malicious threats, and improved reliability requirements of consumers. This paper proposes a methodology to evaluate the planning and operational resilience of power distribution systems under extreme events and determines the withstand capability of the electrical network. The proposed framework is developed by effectively employing the complex network theory. Correlated networks for undesirable configurations are developed from the time series data of active power monitored at nodes of the electrical network. For these correlated networks, computed the network parameters such as clustering coefficient, assortative coefficient, average degree and power law exponent for the anticipation; and percolation threshold for the determination of the network withstand capability under extreme conditions. The proposed methodology is also suitable for identifying the hosting capacity of solar panels in the system while maintaining resilience under different unfavourable conditions and identifying the most critical nodes of the system that could drive the system into non-resilience. This framework is demonstrated on IEEE 123 node test feeder by generating active power time-series data for a variety of electrical conditions using simulation software, GridLAB-D. The percolation threshold resulted as an effective metric for the determination of the planning and operational resilience of the power distribution system.  ( 3 min )
    ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation. (arXiv:2301.13166v3 [cs.AI] UPDATED)
    The ability to accurately locate and navigate to a specific object is a crucial capability for embodied agents that operate in the real world and interact with objects to complete tasks. Such object navigation tasks usually require large-scale training in visual environments with labeled objects, which generalizes poorly to novel objects in unknown environments. In this work, we present a novel zero-shot object navigation method, Exploration with Soft Commonsense constraints (ESC), that transfers commonsense knowledge in pre-trained models to open-world object navigation without any navigation experience nor any other training on the visual environments. First, ESC leverages a pre-trained vision and language model for open-world prompt-based grounding and a pre-trained commonsense language model for room and object reasoning. Then ESC converts commonsense knowledge into navigation actions by modeling it as soft logic predicates for efficient exploration. Extensive experiments on MP3D, HM3D, and RoboTHOR benchmarks show that our ESC method improves significantly over baselines, and achieves new state-of-the-art results for zero-shot object navigation (e.g., 288% relative Success Rate improvement than CoW on MP3D).  ( 2 min )
    Improving Retrieval-Augmented Large Language Models via Data Importance Learning. (arXiv:2307.03027v1 [cs.LG])
    Retrieval augmentation enables large language models to take advantage of external knowledge, for example on tasks like question answering and data imputation. However, the performance of such retrieval-augmented models is limited by the data quality of their underlying retrieval corpus. In this paper, we propose an algorithm based on multilinear extension for evaluating the data importance of retrieved data points. There are exponentially many terms in the multilinear extension, and one key contribution of this paper is a polynomial time algorithm that computes exactly, given a retrieval-augmented model with an additive utility function and a validation set, the data importance of data points in the retrieval corpus using the multilinear extension of the model's utility function. We further proposed an even more efficient ({\epsilon}, {\delta})-approximation algorithm. Our experimental results illustrate that we can enhance the performance of large language models by only pruning or reweighting the retrieval corpus, without requiring further training. For some tasks, this even allows a small model (e.g., GPT-JT), augmented with a search engine API, to outperform GPT-3.5 (without retrieval augmentation). Moreover, we show that weights based on multilinear extension can be computed efficiently in practice (e.g., in less than ten minutes for a corpus with 100 million elements).  ( 2 min )
    Conditional Korhunen-Lo\'{e}ve regression model with Basis Adaptation for high-dimensional problems: uncertainty quantification and inverse modeling. (arXiv:2307.02572v1 [cs.LG])
    We propose a methodology for improving the accuracy of surrogate models of the observable response of physical systems as a function of the systems' spatially heterogeneous parameter fields with applications to uncertainty quantification and parameter estimation in high-dimensional problems. Practitioners often formulate finite-dimensional representations of spatially heterogeneous parameter fields using truncated unconditional Karhunen-Lo\'{e}ve expansions (KLEs) for a certain choice of unconditional covariance kernel and construct surrogate models of the observable response with respect to the random variables in the KLE. When direct measurements of the parameter fields are available, we propose improving the accuracy of these surrogate models by representing the parameter fields via conditional Karhunen-Lo\'{e}ve expansions (CKLEs). CKLEs are constructed by conditioning the covariance kernel of the unconditional expansion on the direct measurements via Gaussian process regression and then truncating the corresponding KLE. We apply the proposed methodology to constructing surrogate models via the Basis Adaptation (BA) method of the stationary hydraulic head response, measured at spatially discrete observation locations, of a groundwater flow model of the Hanford Site, as a function of the 1,000-dimensional representation of the model's log-transmissivity field. We find that BA surrogate models of the hydraulic head based on CKLEs are more accurate than BA surrogate models based on unconditional expansions for forward uncertainty quantification tasks. Furthermore, we find that inverse estimates of the hydraulic transmissivity field computed using CKLE-based BA surrogate models are more accurate than those computed using unconditional BA surrogate models.  ( 3 min )
    Towards a safe MLOps Process for the Continuous Development and Safety Assurance of ML-based Systems in the Railway Domain. (arXiv:2307.02867v1 [cs.SE])
    Traditional automation technologies alone are not sufficient to enable driverless operation of trains (called Grade of Automation (GoA) 4) on non-restricted infrastructure. The required perception tasks are nowadays realized using Machine Learning (ML) and thus need to be developed and deployed reliably and efficiently. One important aspect to achieve this is to use an MLOps process for tackling improved reproducibility, traceability, collaboration, and continuous adaptation of a driverless operation to changing conditions. MLOps mixes ML application development and operation (Ops) and enables high frequency software releases and continuous innovation based on the feedback from operations. In this paper, we outline a safe MLOps process for the continuous development and safety assurance of ML-based systems in the railway domain. It integrates system engineering, safety assurance, and the ML life-cycle in a comprehensive workflow. We present the individual stages of the process and their interactions. Moreover, we describe relevant challenges to automate the different stages of the safe MLOps process.
    OpenDelta: A Plug-and-play Library for Parameter-efficient Adaptation of Pre-trained Models. (arXiv:2307.03084v1 [cs.LG])
    The scale of large pre-trained models (PTMs) poses significant challenges in adapting to downstream tasks due to the high optimization overhead and storage costs associated with full-parameter fine-tuning. To address this, many studies explore parameter-efficient tuning methods, also framed as "delta tuning", which updates only a small subset of parameters, known as "delta modules", while keeping the backbone model's parameters fixed. However, the practicality and flexibility of delta tuning have been limited due to existing implementations that directly modify the code of the backbone PTMs and hard-code specific delta tuning methods for each PTM. In this paper, we present OpenDelta, an open-source library that overcomes these limitations by providing a plug-and-play implementation of various delta tuning methods. Our novel techniques eliminate the need to modify the backbone PTMs' code, making OpenDelta compatible with different, even novel PTMs. OpenDelta is designed to be simple, modular, and extensible, providing a comprehensive platform for researchers and practitioners to adapt large PTMs efficiently.
    Learning Low-Dimensional Nonlinear Structures from High-Dimensional Noisy Data: An Integral Operator Approach. (arXiv:2203.00126v2 [stat.ML] UPDATED)
    We propose a kernel-spectral embedding algorithm for learning low-dimensional nonlinear structures from high-dimensional and noisy observations, where the datasets are assumed to be sampled from an intrinsically low-dimensional manifold and corrupted by high-dimensional noise. The algorithm employs an adaptive bandwidth selection procedure which does not rely on prior knowledge of the underlying manifold. The obtained low-dimensional embeddings can be further utilized for downstream purposes such as data visualization, clustering and prediction. Our method is theoretically justified and practically interpretable. Specifically, we establish the convergence of the final embeddings to their noiseless counterparts when the dimension and size of the samples are comparably large, and characterize the effect of the signal-to-noise ratio on the rate of convergence and phase transition. We also prove convergence of the embeddings to the eigenfunctions of an integral operator defined by the kernel map of some reproducing kernel Hilbert space capturing the underlying nonlinear structures. Numerical simulations and analysis of three real datasets show the superior empirical performance of the proposed method, compared to many existing methods, on learning various manifolds in diverse applications.
    Track Mix Generation on Music Streaming Services using Transformers. (arXiv:2307.03045v1 [cs.IR])
    This paper introduces Track Mix, a personalized playlist generation system released in 2022 on the music streaming service Deezer. Track Mix automatically generates "mix" playlists inspired by initial music tracks, allowing users to discover music similar to their favorite content. To generate these mixes, we consider a Transformer model trained on millions of track sequences from user playlists. In light of the growing popularity of Transformers in recent years, we analyze the advantages, drawbacks, and technical challenges of using such a model for mix generation on the service, compared to a more traditional collaborative filtering approach. Since its release, Track Mix has been generating playlists for millions of users daily, enhancing their music discovery experience on Deezer.  ( 2 min )
    FREEDOM: Target Label & Source Data & Domain Information-Free Multi-Source Domain Adaptation for Unsupervised Personalization. (arXiv:2307.02493v1 [cs.LG])
    From a service perspective, Multi-Source Domain Adaptation (MSDA) is a promising scenario to adapt a deployed model to a client's dataset. It can provide adaptation without a target label and support the case where a source dataset is constructed from multiple domains. However, it is impractical, wherein its training heavily relies on prior domain information of the multi-source dataset -- how many domains exist and the domain label of each data sample. Moreover, MSDA requires both source and target datasets simultaneously (physically), causing storage limitations on the client device or data privacy issues by transferring client data to a server. For a more practical scenario of model adaptation from a service provider's point of view, we relax these constraints and present a novel problem scenario of Three-Free Domain Adaptation, namely TFDA, where 1) target labels, 2) source dataset, and mostly 3) source domain information (domain labels + the number of domains) are unavailable. Under the problem scenario, we propose a practical adaptation framework called FREEDOM. It leverages the power of the generative model, disentangling data into class and style aspects, where the style is defined as the class-independent information from the source data and designed with a nonparametric Bayesian approach. In the adaptation stage, FREEDOM aims to match the source class distribution with the target's under the philosophy that class distribution is consistent even if the style is different; after then, only part of the classification model is deployed as a personalized network. As a result, FREEDOM achieves state-of-the-art or comparable performance even without domain information, with reduced final model size on the target side, independent of the number of source domains.  ( 3 min )
    TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers. (arXiv:2307.02588v1 [cs.LG])
    Dynamic graph embedding has emerged as a very effective technique for addressing diverse temporal graph analytic tasks (i.e., link prediction, node classification, recommender systems, anomaly detection, and graph generation) in various applications. Such temporal graphs exhibit heterogeneous transient dynamics, varying time intervals, and highly evolving node features throughout their evolution. Hence, incorporating long-range dependencies from the historical graph context plays a crucial role in accurately learning their temporal dynamics. In this paper, we develop a graph embedding model with uncertainty quantification, TransformerG2G, by exploiting the advanced transformer encoder to first learn intermediate node representations from its current state ($t$) and previous context (over timestamps [$t-1, t-l$], $l$ is the length of context). Moreover, we employ two projection layers to generate lower-dimensional multivariate Gaussian distributions as each node's latent embedding at timestamp $t$. We consider diverse benchmarks with varying levels of ``novelty" as measured by the TEA plots. Our experiments demonstrate that the proposed TransformerG2G model outperforms conventional multi-step methods and our prior work (DynG2G) in terms of both link prediction accuracy and computational efficiency, especially for high degree of novelty. Furthermore, the learned time-dependent attention weights across multiple graph snapshots reveal the development of an automatic adaptive time stepping enabled by the transformer. Importantly, by examining the attention weights, we can uncover temporal dependencies, identify influential elements, and gain insights into the complex interactions within the graph structure. For example, we identified a strong correlation between attention weights and node degree at the various stages of the graph topology evolution.  ( 3 min )
    Unleashing the Power of Electrocardiograms: A novel approach for Patient Identification in Healthcare Systems with ECG Signals. (arXiv:2302.06529v2 [cs.LG] UPDATED)
    Over the course of the past two decades, a substantial body of research has substantiated the viability of utilising cardiac signals as a biometric modality. This paper presents a novel approach for patient identification in healthcare systems using electrocardiogram signals. A convolutional neural network is used to classify users based on images extracted from ECG signals. The proposed identification system is evaluated in multiple databases, providing a comprehensive understanding of its potential in real-world scenarios. The impact of Cardiovascular Diseases on generic user identification has been largely overlooked in previous studies. The presented method takes into account the cardiovascular condition of the patients, ensuring that the results obtained are not biased or limited. Furthermore, the results obtained are consistent and reliable, with lower error rates and higher accuracy metrics, as demonstrated through extensive experimentation. All these features make the proposed method a valuable contribution to the field of patient identification in healthcare systems, and make it a strong contender for practical applications.  ( 2 min )
    Co-design Hardware and Algorithm for Vector Search. (arXiv:2306.11182v3 [cs.LG] UPDATED)
    Vector search has emerged as the foundation for large-scale information retrieval and machine learning systems, with search engines like Google and Bing processing tens of thousands of queries per second on petabyte-scale document datasets by evaluating vector similarities between encoded query texts and web documents. As performance demands for vector search systems surge, accelerated hardware offers a promising solution in the post-Moore's Law era. We introduce \textit{FANNS}, an end-to-end and scalable vector search framework on FPGAs. Given a user-provided recall requirement on a dataset and a hardware resource budget, \textit{FANNS} automatically co-designs hardware and algorithm, subsequently generating the corresponding accelerator. The framework also supports scale-out by incorporating a hardware TCP/IP stack in the accelerator. \textit{FANNS} attains up to 23.0$\times$ and 37.2$\times$ speedup compared to FPGA and CPU baselines, respectively, and demonstrates superior scalability to GPUs, achieving 5.5$\times$ and 7.6$\times$ speedup in median and 95\textsuperscript{th} percentile (P95) latency within an eight-accelerator configuration. The remarkable performance of \textit{FANNS} lays a robust groundwork for future FPGA integration in data centers and AI supercomputers.  ( 3 min )
    Conditional independence testing under model misspecification. (arXiv:2307.02520v1 [stat.ML])
    Conditional independence (CI) testing is fundamental and challenging in modern statistics and machine learning. Many modern methods for CI testing rely on powerful supervised learning methods to learn regression functions or Bayes predictors as an intermediate step. Although the methods are guaranteed to control Type-I error when the supervised learning methods accurately estimate the regression functions or Bayes predictors, their behavior is less understood when they fail due to model misspecification. In a broader sense, model misspecification can arise even when universal approximators (e.g., deep neural nets) are employed. Then, we study the performance of regression-based CI tests under model misspecification. Namely, we propose new approximations or upper bounds for the testing errors of three regression-based tests that depend on misspecification errors. Moreover, we introduce the Rao-Blackwellized Predictor Test (RBPT), a novel regression-based CI test robust against model misspecification. Finally, we conduct experiments with artificial and real data, showcasing the usefulness of our theory and methods.
    Stacking of Hyperparameter Tuned Models for Tagging Coding Problems. (arXiv:2306.10077v2 [cs.LG] UPDATED)
    Coding problems are problems that require a solution in the form of a computer program. Coding problems are popular among students and professionals as it enhances their skills and career opportunities. An AI system that would help those who practice coding problems would be highly useful and there is a huge potential for such a system. In this work, we propose a model which uses stacking of hyperparameter tuned boosting models to achieve impressive metric scores of 77.8% accuracy and 0.815 PR-AUC on the dataset that was scraped from Codeforces and Leetcode. We open source the dataset and the models developed for this work.  ( 2 min )
    A Real-time Human Pose Estimation Approach for Optimal Sensor Placement in Sensor-based Human Activity Recognition. (arXiv:2307.02906v1 [cs.LG])
    Sensor-based Human Activity Recognition facilitates unobtrusive monitoring of human movements. However, determining the most effective sensor placement for optimal classification performance remains challenging. This paper introduces a novel methodology to resolve this issue, using real-time 2D pose estimations derived from video recordings of target activities. The derived skeleton data provides a unique strategy for identifying the optimal sensor location. We validate our approach through a feasibility study, applying inertial sensors to monitor 13 different activities across ten subjects. Our findings indicate that the vision-based method for sensor placement offers comparable results to the conventional deep learning approach, demonstrating its efficacy. This research significantly advances the field of Human Activity Recognition by providing a lightweight, on-device solution for determining the optimal sensor placement, thereby enhancing data anonymization and supporting a multimodal classification approach.
    Sandwiched Video Compression: Efficiently Extending the Reach of Standard Codecs with Neural Wrappers. (arXiv:2303.11473v2 [eess.IV] UPDATED)
    We propose sandwiched video compression -- a video compression system that wraps neural networks around a standard video codec. The sandwich framework consists of a neural pre- and post-processor with a standard video codec between them. The networks are trained jointly to optimize a rate-distortion loss function with the goal of significantly improving over the standard codec in various compression scenarios. End-to-end training in this setting requires a differentiable proxy for the standard video codec, which incorporates temporal processing with motion compensation, inter/intra mode decisions, and in-loop filtering. We propose differentiable approximations to key video codec components and demonstrate that, in addition to providing meaningful compression improvements over the standard codec, the neural codes of the sandwich lead to significantly better rate-distortion performance in two important scenarios.When transporting high-resolution video via low-resolution HEVC, the sandwich system obtains 6.5 dB improvements over standard HEVC. More importantly, using the well-known perceptual similarity metric, LPIPS, we observe 30% improvements in rate at the same quality over HEVC. Last but not least, we show that pre- and post-processors formed by very modestly-parameterized, light-weight networks can closely approximate these results.
    ShadowNet: A Secure and Efficient On-device Model Inference System for Convolutional Neural Networks. (arXiv:2011.05905v4 [cs.CR] UPDATED)
    With the increased usage of AI accelerators on mobile and edge devices, on-device machine learning (ML) is gaining popularity. Thousands of proprietary ML models are being deployed today on billions of untrusted devices. This raises serious security concerns about model privacy. However, protecting model privacy without losing access to the untrusted AI accelerators is a challenging problem. In this paper, we present a novel on-device model inference system, ShadowNet. ShadowNet protects the model privacy with Trusted Execution Environment (TEE) while securely outsourcing the heavy linear layers of the model to the untrusted hardware accelerators. ShadowNet achieves this by transforming the weights of the linear layers before outsourcing them and restoring the results inside the TEE. The non-linear layers are also kept secure inside the TEE. ShadowNet's design ensures efficient transformation of the weights and the subsequent restoration of the results. We build a ShadowNet prototype based on TensorFlow Lite and evaluate it on five popular CNNs, namely, MobileNet, ResNet-44, MiniVGG, ResNet-404, and YOLOv4-tiny. Our evaluation shows that ShadowNet achieves strong security guarantees with reasonable performance, offering a practical solution for secure on-device model inference.
    Effects of data time lag in a decision-making system using machine learning for pork price prediction. (arXiv:2305.05677v2 [cs.LG] UPDATED)
    Spain is the third-largest producer of pork meat in the world, and many farms in several regions depend on the evolution of this market. However, the current pricing system is unfair, as some actors have better market information than others. In this context, historical pricing is an easy-to-find and affordable data source that can help all agents to be better informed. However, the time lag in data acquisition can affect their pricing decisions. In this paper, we study the effect that data acquisition delay has on a price prediction system using multiple prediction algorithms. We describe the integration of the best proposal into a decision support system prototype and test it in a real-case scenario. Specifically, we use public data from the most important regional pork meat markets in Spain published by the Ministry of Agriculture with a two-week delay and subscription-based data of the same markets obtained on the same day. The results show that the error difference between the best public and data subscription models is 0.6 Euro cents in favor of the data without delay. The market dimension makes these differences significant in the supply chain, giving pricing agents a better tool to negotiate market prices.
    ContainerGym: A Real-World Reinforcement Learning Benchmark for Resource Allocation. (arXiv:2307.02991v1 [cs.LG])
    We present ContainerGym, a benchmark for reinforcement learning inspired by a real-world industrial resource allocation task. The proposed benchmark encodes a range of challenges commonly encountered in real-world sequential decision making problems, such as uncertainty. It can be configured to instantiate problems of varying degrees of difficulty, e.g., in terms of variable dimensionality. Our benchmark differs from other reinforcement learning benchmarks, including the ones aiming to encode real-world difficulties, in that it is directly derived from a real-world industrial problem, which underwent minimal simplification and streamlining. It is sufficiently versatile to evaluate reinforcement learning algorithms on any real-world problem that fits our resource allocation framework. We provide results of standard baseline methods. Going beyond the usual training reward curves, our results and the statistical tools used to interpret them allow to highlight interesting limitations of well-known deep reinforcement learning algorithms, namely PPO, TRPO and DQN.
    Context-Aware Configuration and Management of WiFi Direct Groups for Real Opportunistic Networks. (arXiv:2307.03126v1 [cs.NI])
    Wi-Fi Direct is a promising technology for the support of device-to-device communications (D2D) on commercial mobile devices. However, the standard as-it-is is not sufficient to support the real deployment of networking solutions entirely based on D2D such as opportunistic networks. In fact, WiFi Direct presents some characteristics that could limit the autonomous creation of D2D connections among users' personal devices. Specifically, the standard explicitly requires the user's authorization to establish a connection between two or more devices, and it provides a limited support for inter-group communication. In some cases, this might lead to the creation of isolated groups of nodes which cannot communicate among each other. In this paper, we propose a novel middleware-layer protocol for the efficient configuration and management of WiFi Direct groups (WiFi Direct Group Manager, WFD-GM) to enable autonomous connections and inter-group communication. This enables opportunistic networks in real conditions (e.g., variable mobility and network size). WFD-GM defines a context function that takes into account heterogeneous parameters for the creation of the best group configuration in a specific time window, including an index of nodes' stability and power levels. We evaluate the protocol performances by simulating three reference scenarios including different mobility models, geographical areas and number of nodes. Simulations are also supported by experimental results related to the evaluation in a real testbed of the involved context parameters. We compare WFD-GM with the state-of-the-art solutions and we show that it performs significantly better than a Baseline approach in scenarios with medium/low mobility, and it is comparable with it in case of high mobility, without introducing additional overhead.  ( 3 min )
    UTRNet: High-Resolution Urdu Text Recognition In Printed Documents. (arXiv:2306.15782v2 [cs.CV] UPDATED)
    In this paper, we propose a novel approach to address the challenges of printed Urdu text recognition using high-resolution, multi-scale semantic feature extraction. Our proposed UTRNet architecture, a hybrid CNN-RNN model, demonstrates state-of-the-art performance on benchmark datasets. To address the limitations of previous works, which struggle to generalize to the intricacies of the Urdu script and the lack of sufficient annotated real-world data, we have introduced the UTRSet-Real, a large-scale annotated real-world dataset comprising over 11,000 lines and UTRSet-Synth, a synthetic dataset with 20,000 lines closely resembling real-world and made corrections to the ground truth of the existing IIITH dataset, making it a more reliable resource for future research. We also provide UrduDoc, a benchmark dataset for Urdu text line detection in scanned documents. Additionally, we have developed an online tool for end-to-end Urdu OCR from printed documents by integrating UTRNet with a text detection model. Our work not only addresses the current limitations of Urdu OCR but also paves the way for future research in this area and facilitates the continued advancement of Urdu OCR technology. The project page with source code, datasets, annotations, trained models, and online tool is available at abdur75648.github.io/UTRNet.  ( 2 min )
    How to Detect Unauthorized Data Usages in Text-to-image Diffusion Models. (arXiv:2307.03108v1 [cs.CV])
    Recent text-to-image diffusion models have shown surprising performance in generating high-quality images. However, concerns have arisen regarding the unauthorized usage of data during the training process. One example is when a model trainer collects a set of images created by a particular artist and attempts to train a model capable of generating similar images without obtaining permission from the artist. To address this issue, it becomes crucial to detect unauthorized data usage. In this paper, we propose a method for detecting such unauthorized data usage by planting injected memorization into the text-to-image diffusion models trained on the protected dataset. Specifically, we modify the protected image dataset by adding unique contents on the images such as stealthy image wrapping functions that are imperceptible to human vision but can be captured and memorized by diffusion models. By analyzing whether the model has memorization for the injected content (i.e., whether the generated images are processed by the chosen post-processing function), we can detect models that had illegally utilized the unauthorized data. Our experiments conducted on Stable Diffusion and LoRA model demonstrate the effectiveness of the proposed method in detecting unauthorized data usages.  ( 2 min )
    FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout. (arXiv:2307.02623v1 [cs.LG])
    Federated Learning (FL) allows machine learning models to train locally on individual mobile devices, synchronizing model updates via a shared server. This approach safeguards user privacy; however, it also generates a heterogeneous training environment due to the varying performance capabilities across devices. As a result, straggler devices with lower performance often dictate the overall training time in FL. In this work, we aim to alleviate this performance bottleneck due to stragglers by dynamically balancing the training load across the system. We introduce Invariant Dropout, a method that extracts a sub-model based on the weight update threshold, thereby minimizing potential impacts on accuracy. Building on this dropout technique, we develop an adaptive training framework, Federated Learning using Invariant Dropout (FLuID). FLuID offers a lightweight sub-model extraction to regulate computational intensity, thereby reducing the load on straggler devices without affecting model quality. Our method leverages neuron updates from non-straggler devices to construct a tailored sub-model for each straggler based on client performance profiling. Furthermore, FLuID can dynamically adapt to changes in stragglers as runtime conditions shift. We evaluate FLuID using five real-world mobile clients. The evaluations show that Invariant Dropout maintains baseline model efficiency while alleviating the performance bottleneck of stragglers through a dynamic, runtime approach.  ( 2 min )
    Learning to Solve Tasks with Exploring Prior Behaviours. (arXiv:2307.02889v1 [cs.RO])
    Demonstrations are widely used in Deep Reinforcement Learning (DRL) for facilitating solving tasks with sparse rewards. However, the tasks in real-world scenarios can often have varied initial conditions from the demonstration, which would require additional prior behaviours. For example, consider we are given the demonstration for the task of \emph{picking up an object from an open drawer}, but the drawer is closed in the training. Without acquiring the prior behaviours of opening the drawer, the robot is unlikely to solve the task. To address this, in this paper we propose an Intrinsic Rewards Driven Example-based Control \textbf{(IRDEC)}. Our method can endow agents with the ability to explore and acquire the required prior behaviours and then connect to the task-specific behaviours in the demonstration to solve sparse-reward tasks without requiring additional demonstration of the prior behaviours. The performance of our method outperforms other baselines on three navigation tasks and one robotic manipulation task with sparse rewards. Codes are available at https://github.com/Ricky-Zhu/IRDEC.  ( 2 min )
    PLIERS: a Popularity-Based Recommender System for Content Dissemination in Online Social Networks. (arXiv:2307.02865v1 [cs.IR])
    In this paper, we propose a novel tag-based recommender system called PLIERS, which relies on the assumption that users are mainly interested in items and tags with similar popularity to those they already own. PLIERS is aimed at reaching a good tradeoff between algorithmic complexity and the level of personalization of recommended items. To evaluate PLIERS, we performed a set of experiments on real OSN datasets, demonstrating that it outperforms state-of-the-art solutions in terms of personalization, relevance, and novelty of recommendations.  ( 2 min )
  • Open

    Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations. (arXiv:2206.04779v3 [cs.LG] UPDATED)
    Offline reinforcement learning has shown great promise in leveraging large pre-collected datasets for policy learning, allowing agents to forgo often-expensive online data collection. However, offline reinforcement learning from visual observations with continuous action spaces remains under-explored, with a limited understanding of the key challenges in this complex domain. In this paper, we establish simple baselines for continuous control in the visual domain and introduce a suite of benchmarking tasks for offline reinforcement learning from visual observations designed to better represent the data distributions present in real-world offline RL problems and guided by a set of desiderata for offline RL from visual observations, including robustness to visual distractions and visually identifiable changes in dynamics. Using this suite of benchmarking tasks, we show that simple modifications to two popular vision-based online reinforcement learning algorithms, DreamerV2 and DrQ-v2, suffice to outperform existing offline RL methods and establish competitive baselines for continuous control in the visual domain. We rigorously evaluate these algorithms and perform an empirical evaluation of the differences between state-of-the-art model-based and model-free offline RL methods for continuous control from visual observations. All code and data used in this evaluation are open-sourced to facilitate progress in this domain.  ( 3 min )
    Learning Low-Dimensional Nonlinear Structures from High-Dimensional Noisy Data: An Integral Operator Approach. (arXiv:2203.00126v2 [stat.ML] UPDATED)
    We propose a kernel-spectral embedding algorithm for learning low-dimensional nonlinear structures from high-dimensional and noisy observations, where the datasets are assumed to be sampled from an intrinsically low-dimensional manifold and corrupted by high-dimensional noise. The algorithm employs an adaptive bandwidth selection procedure which does not rely on prior knowledge of the underlying manifold. The obtained low-dimensional embeddings can be further utilized for downstream purposes such as data visualization, clustering and prediction. Our method is theoretically justified and practically interpretable. Specifically, we establish the convergence of the final embeddings to their noiseless counterparts when the dimension and size of the samples are comparably large, and characterize the effect of the signal-to-noise ratio on the rate of convergence and phase transition. We also prove convergence of the embeddings to the eigenfunctions of an integral operator defined by the kernel map of some reproducing kernel Hilbert space capturing the underlying nonlinear structures. Numerical simulations and analysis of three real datasets show the superior empirical performance of the proposed method, compared to many existing methods, on learning various manifolds in diverse applications.  ( 2 min )
    Active Policy Improvement from Multiple Black-box Oracles. (arXiv:2306.10259v2 [cs.LG] UPDATED)
    Reinforcement learning (RL) has made significant strides in various complex domains. However, identifying an effective policy via RL often necessitates extensive exploration. Imitation learning aims to mitigate this issue by using expert demonstrations to guide exploration. In real-world scenarios, one often has access to multiple suboptimal black-box experts, rather than a single optimal oracle. These experts do not universally outperform each other across all states, presenting a challenge in actively deciding which oracle to use and in which state. We introduce MAPS and MAPS-SE, a class of policy improvement algorithms that perform imitation learning from multiple suboptimal oracles. In particular, MAPS actively selects which of the oracles to imitate and improve their value function estimates, and MAPS-SE additionally leverages an active state exploration criterion to determine which states one should explore. We provide a comprehensive theoretical analysis and demonstrate that MAPS and MAPS-SE enjoy sample efficiency advantage over the state-of-the-art policy improvement algorithms. Empirical results show that MAPS-SE significantly accelerates policy optimization via state-wise imitation learning from multiple oracles across a broad spectrum of control tasks in the DeepMind Control Suite. Our code is publicly available at: https://github.com/ripl/maps.  ( 2 min )
    Statistical-Computational Tradeoffs in Mixed Sparse Linear Regression. (arXiv:2303.02118v2 [stat.ML] UPDATED)
    We consider the problem of mixed sparse linear regression with two components, where two real $k$-sparse signals $\beta_1, \beta_2$ are to be recovered from $n$ unlabelled noisy linear measurements. The sparsity is allowed to be sublinear in the dimension, and additive noise is assumed to be independent Gaussian with variance $\sigma^2$. Prior work has shown that the problem suffers from a $\frac{k}{SNR^2}$-to-$\frac{k^2}{SNR^2}$ statistical-to-computational gap, resembling other computationally challenging high-dimensional inference problems such as Sparse PCA and Robust Sparse Mean Estimation; here $SNR$ is the signal-to-noise ratio. We establish the existence of a more extensive computational barrier for this problem through the method of low-degree polynomials, but show that the problem is computationally hard only in a very narrow symmetric parameter regime. We identify a smooth information-computation tradeoff between the sample complexity $n$ and runtime for any randomized algorithm in this hard regime. Via a simple reduction, this provides novel rigorous evidence for the existence of a computational barrier to solving exact support recovery in sparse phase retrieval with sample complexity $n = \tilde{o}(k^2)$. Our second contribution is to analyze a simple thresholding algorithm which, outside of the narrow regime where the problem is hard, solves the associated mixed regression detection problem in $O(np)$ time with square-root the number of samples and matches the sample complexity required for (non-mixed) sparse linear regression; this allows the recovery problem to be subsequently solved by state-of-the-art techniques from the dense case. As a special case of our results, we show that this simple algorithm is order-optimal among a large family of algorithms in solving exact signed support recovery in sparse linear regression.  ( 3 min )
    On the Noise Sensitivity of the Randomized SVD. (arXiv:2305.17435v2 [cs.IT] UPDATED)
    The randomized singular value decomposition (R-SVD) is a popular sketching-based algorithm for efficiently computing the partial SVD of a large matrix. When the matrix is low-rank, the R-SVD produces its partial SVD exactly; but when the rank is large, it only yields an approximation. Motivated by applications in data science and principal component analysis (PCA), we analyze the R-SVD under a low-rank signal plus noise measurement model; specifically, when its input is a spiked random matrix. The singular values produced by the R-SVD are shown to exhibit a BBP-like phase transition: when the SNR exceeds a certain detectability threshold, that depends on the dimension reduction factor, the largest singular value is an outlier; below the threshold, no outlier emerges from the bulk of singular values. We further compute asymptotic formulas for the overlap between the ground truth signal singular vectors and the approximations produced by the R-SVD. Dimensionality reduction has the adverse affect of amplifying the noise in a highly nonlinear manner. Our results demonstrate the statistical advantage -- in both signal detection and estimation -- of the R-SVD over more naive sketched PCA variants; the advantage is especially dramatic when the sketching dimension is small. Our analysis is asymptotically exact, and substantially more fine-grained than existing operator-norm error bounds for the R-SVD, which largely fail to give meaningful error estimates in the moderate SNR regime. It applies for a broad family of sketching matrices previously considered in the literature, including Gaussian i.i.d. sketches, random projections, and the sub-sampled Hadamard transform, among others. Lastly, we derive an optimal singular value shrinker for singular values and vectors obtained through the R-SVD, which may be useful for applications in matrix denoising.  ( 3 min )
    Fairness in Multi-Task Learning via Wasserstein Barycenters. (arXiv:2306.10155v2 [stat.ML] UPDATED)
    Algorithmic Fairness is an established field in machine learning that aims to reduce biases in data. Recent advances have proposed various methods to ensure fairness in a univariate environment, where the goal is to de-bias a single task. However, extending fairness to a multi-task setting, where more than one objective is optimised using a shared representation, remains underexplored. To bridge this gap, we develop a method that extends the definition of Strong Demographic Parity to multi-task learning using multi-marginal Wasserstein barycenters. Our approach provides a closed form solution for the optimal fair multi-task predictor including both regression and binary classification tasks. We develop a data-driven estimation procedure for the solution and run numerical experiments on both synthetic and real datasets. The empirical results highlight the practical value of our post-processing methodology in promoting fair decision-making.  ( 2 min )
    A Finite-Particle Convergence Rate for Stein Variational Gradient Descent. (arXiv:2211.09721v4 [cs.LG] UPDATED)
    We provide the first finite-particle convergence rate for Stein variational gradient descent (SVGD), a popular algorithm for approximating a probability distribution with a collection of particles. Specifically, whenever the target distribution is sub-Gaussian with a Lipschitz score, SVGD with n particles and an appropriate step size sequence drives the kernel Stein discrepancy to zero at an order 1/sqrt(log log n) rate. We suspect that the dependence on n can be improved, and we hope that our explicit, non-asymptotic proof strategy will serve as a template for future refinements.  ( 2 min )
    Descriptive vs. inferential community detection in networks: pitfalls, myths, and half-truths. (arXiv:2112.00183v7 [physics.soc-ph] UPDATED)
    Community detection is one of the most important methodological fields of network science, and one which has attracted a significant amount of attention over the past decades. This area deals with the automated division of a network into fundamental building blocks, with the objective of providing a summary of its large-scale structure. Despite its importance and widespread adoption, there is a noticeable gap between what is arguably the state-of-the-art and the methods that are actually used in practice in a variety of fields. Here we attempt to address this discrepancy by dividing existing methods according to whether they have a "descriptive" or an "inferential" goal. While descriptive methods find patterns in networks based on context-dependent notions of community structure, inferential methods articulate generative models, and attempt to fit them to data. In this way, they are able to provide insights into the mechanisms of network formation, and separate structure from randomness in a manner supported by statistical evidence. We review how employing descriptive methods with inferential aims is riddled with pitfalls and misleading answers, and thus should be in general avoided. We argue that inferential methods are more typically aligned with clearer scientific questions, yield more robust results, and should be in many cases preferred. We attempt to dispel some myths and half-truths often believed when community detection is employed in practice, in an effort to improve both the use of such methods as well as the interpretation of their results.  ( 3 min )
    Computable Stability for Persistence Rank Function Machine Learning. (arXiv:2307.02904v1 [math.AT])
    Persistent homology barcodes and diagrams are a cornerstone of topological data analysis. Widely used in many real data settings, they relate variation in topological information (as measured by cellular homology) with variation in data, however, they are challenging to use in statistical settings due to their complex geometric structure. In this paper, we revisit the persistent homology rank function -- an invariant measure of ``shape" that was introduced before barcodes and persistence diagrams and captures the same information in a form that is more amenable to data and computation. In particular, since they are functions, techniques from functional data analysis -- a domain of statistics adapted for functions -- apply directly to persistent homology when represented by rank functions. Rank functions, however, have been less popular than barcodes because they face the challenge that stability -- a property that is crucial to validate their use in data analysis -- is difficult to guarantee, mainly due to metric concerns on rank function space. However, rank functions extend more naturally to the increasingly popular and important case of multiparameter persistent homology. In this paper, we study the performance of rank functions in functional inferential statistics and machine learning on both simulated and real data, and in both single and multiparameter persistent homology. We find that the use of persistent homology captured by rank functions offers a clear improvement over existing approaches. We then provide theoretical justification for our numerical experiments and applications to data by deriving several stability results for single- and multiparameter persistence rank functions under various metrics with the underlying aim of computational feasibility and interpretability.  ( 3 min )
    Learning Curves for Heterogeneous Feature-Subsampled Ridge Ensembles. (arXiv:2307.03176v1 [stat.ML])
    Feature bagging is a well-established ensembling method which aims to reduce prediction variance by training estimators in an ensemble on random subsamples or projections of features. Typically, ensembles are chosen to be homogeneous, in the sense the the number of feature dimensions available to an estimator is uniform across the ensemble. Here, we introduce heterogeneous feature ensembling, with estimators built on varying number of feature dimensions, and consider its performance in a linear regression setting. We study an ensemble of linear predictors, each fit using ridge regression on a subset of the available features. We allow the number of features included in these subsets to vary. Using the replica trick from statistical physics, we derive learning curves for ridge ensembles with deterministic linear masks. We obtain explicit expressions for the learning curves in the case of equicorrelated data with an isotropic feature noise. Using the derived expressions, we investigate the effect of subsampling and ensembling, finding sharp transitions in the optimal ensembling strategy in the parameter space of noise level, data correlations, and data-task alignment. Finally, we suggest variable-dimension feature bagging as a strategy to mitigate double descent for robust machine learning in practice.  ( 2 min )
    Understanding Uncertainty Sampling. (arXiv:2307.02719v1 [cs.LG])
    Uncertainty sampling is a prevalent active learning algorithm that queries sequentially the annotations of data samples which the current prediction model is uncertain about. However, the usage of uncertainty sampling has been largely heuristic: (i) There is no consensus on the proper definition of "uncertainty" for a specific task under a specific loss; (ii) There is no theoretical guarantee that prescribes a standard protocol to implement the algorithm, for example, how to handle the sequentially arrived annotated data under the framework of optimization algorithms such as stochastic gradient descent. In this work, we systematically examine uncertainty sampling algorithms under both stream-based and pool-based active learning. We propose a notion of equivalent loss which depends on the used uncertainty measure and the original loss function and establish that an uncertainty sampling algorithm essentially optimizes against such an equivalent loss. The perspective verifies the properness of existing uncertainty measures from two aspects: surrogate property and loss convexity. Furthermore, we propose a new notion for designing uncertainty measures called \textit{loss as uncertainty}. The idea is to use the conditional expected loss given the features as the uncertainty measure. Such an uncertainty measure has nice analytical properties and generality to cover both classification and regression problems, which enable us to provide the first generalization bound for uncertainty sampling algorithms under both stream-based and pool-based settings, in the full generality of the underlying model and problem. Lastly, we establish connections between certain variants of the uncertainty sampling algorithms with risk-sensitive objectives and distributional robustness, which can partly explain the advantage of uncertainty sampling algorithms when the sample size is small.  ( 2 min )
    Multiplicative Updates for Online Convex Optimization over Symmetric Cones. (arXiv:2307.03136v1 [math.OC])
    We study online convex optimization where the possible actions are trace-one elements in a symmetric cone, generalizing the extensively-studied experts setup and its quantum counterpart. Symmetric cones provide a unifying framework for some of the most important optimization models, including linear, second-order cone, and semidefinite optimization. Using tools from the field of Euclidean Jordan Algebras, we introduce the Symmetric-Cone Multiplicative Weights Update (SCMWU), a projection-free algorithm for online optimization over the trace-one slice of an arbitrary symmetric cone. We show that SCMWU is equivalent to Follow-the-Regularized-Leader and Online Mirror Descent with symmetric-cone negative entropy as regularizer. Using this structural result we show that SCMWU is a no-regret algorithm, and verify our theoretical results with extensive experiments. Our results unify and generalize the analysis for the Multiplicative Weights Update method over the probability simplex and the Matrix Multiplicative Weights Update method over the set of density matrices.  ( 2 min )
    PCL-Indexability and Whittle Index for Restless Bandits with General Observation Models. (arXiv:2307.03034v1 [stat.ML])
    In this paper, we consider a general observation model for restless multi-armed bandit problems. The operation of the player needs to be based on certain feedback mechanism that is error-prone due to resource constraints or environmental or intrinsic noises. By establishing a general probabilistic model for dynamics of feedback/observation, we formulate the problem as a restless bandit with a countable belief state space starting from an arbitrary initial belief (a priori information). We apply the achievable region method with partial conservation law (PCL) to the infinite-state problem and analyze its indexability and priority index (Whittle index). Finally, we propose an approximation process to transform the problem into which the AG algorithm of Ni\~no-Mora and Bertsimas for finite-state problems can be applied to. Numerical experiments show that our algorithm has an excellent performance.  ( 2 min )
    Evaluating the Evaluators: Are Current Few-Shot Learning Benchmarks Fit for Purpose?. (arXiv:2307.02732v1 [cs.LG])
    Numerous benchmarks for Few-Shot Learning have been proposed in the last decade. However all of these benchmarks focus on performance averaged over many tasks, and the question of how to reliably evaluate and tune models trained for individual tasks in this regime has not been addressed. This paper presents the first investigation into task-level evaluation -- a fundamental step when deploying a model. We measure the accuracy of performance estimators in the few-shot setting, consider strategies for model selection, and examine the reasons for the failure of evaluators usually thought of as being robust. We conclude that cross-validation with a low number of folds is the best choice for directly estimating the performance of a model, whereas using bootstrapping or cross validation with a large number of folds is better for model selection purposes. Overall, we find that existing benchmarks for few-shot learning are not designed in such a way that one can get a reliable picture of how effectively methods can be used on individual tasks.  ( 2 min )
    The computational asymptotics of Gaussian variational inference and the Laplace approximation. (arXiv:2104.05886v3 [stat.CO] UPDATED)
    Gaussian variational inference and the Laplace approximation are popular alternatives to Markov chain Monte Carlo that formulate Bayesian posterior inference as an optimization problem, enabling the use of simple and scalable stochastic optimization algorithms. However, a key limitation of both methods is that the solution to the optimization problem is typically not tractable to compute; even in simple settings the problem is nonconvex. Thus, recently developed statistical guarantees -- which all involve the (data) asymptotic properties of the global optimum -- are not reliably obtained in practice. In this work, we provide two major contributions: a theoretical analysis of the asymptotic convexity properties of variational inference with a Gaussian family and the maximum a posteriori (MAP) problem required by the Laplace approximation; and two algorithms -- consistent Laplace approximation (CLA) and consistent stochastic variational inference (CSVI) -- that exploit these properties to find the optimal approximation in the asymptotic regime. Both CLA and CSVI involve a tractable initialization procedure that finds the local basin of the optimum, and CSVI further includes a scaled gradient descent algorithm that provably stays locally confined to that basin. Experiments on nonconvex synthetic and real-data examples show that compared with standard variational and Laplace approximations, both CSVI and CLA improve the likelihood of obtaining the global optimum of their respective optimization problems.  ( 3 min )
    Panel Data Nowcasting: The Case of Price-Earnings Ratios. (arXiv:2307.02673v1 [econ.EM])
    The paper uses structured machine learning regressions for nowcasting with panel data consisting of series sampled at different frequencies. Motivated by the problem of predicting corporate earnings for a large cross-section of firms with macroeconomic, financial, and news time series sampled at different frequencies, we focus on the sparse-group LASSO regularization which can take advantage of the mixed frequency time series panel data structures. Our empirical results show the superior performance of our machine learning panel data regression models over analysts' predictions, forecast combinations, firm-specific time series regression models, and standard machine learning methods.  ( 2 min )
    Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation. (arXiv:2307.02598v1 [cs.LG])
    We tackle the problems of latent variables identification and "out-of-support" image generation in representation learning. We show that both are possible for a class of decoders that we call additive, which are reminiscent of decoders used for object-centric representation learning (OCRL) and well suited for images that can be decomposed as a sum of object-specific images. We provide conditions under which exactly solving the reconstruction problem using an additive decoder is guaranteed to identify the blocks of latent variables up to permutation and block-wise invertible transformations. This guarantee relies only on very weak assumptions about the distribution of the latent factors, which might present statistical dependencies and have an almost arbitrarily shaped support. Our result provides a new setting where nonlinear independent component analysis (ICA) is possible and adds to our theoretical understanding of OCRL methods. We also show theoretically that additive decoders can generate novel images by recombining observed factors of variations in novel ways, an ability we refer to as Cartesian-product extrapolation. We show empirically that additivity is crucial for both identifiability and extrapolation on simulated data.  ( 2 min )
    Conditional independence testing under model misspecification. (arXiv:2307.02520v1 [stat.ML])
    Conditional independence (CI) testing is fundamental and challenging in modern statistics and machine learning. Many modern methods for CI testing rely on powerful supervised learning methods to learn regression functions or Bayes predictors as an intermediate step. Although the methods are guaranteed to control Type-I error when the supervised learning methods accurately estimate the regression functions or Bayes predictors, their behavior is less understood when they fail due to model misspecification. In a broader sense, model misspecification can arise even when universal approximators (e.g., deep neural nets) are employed. Then, we study the performance of regression-based CI tests under model misspecification. Namely, we propose new approximations or upper bounds for the testing errors of three regression-based tests that depend on misspecification errors. Moreover, we introduce the Rao-Blackwellized Predictor Test (RBPT), a novel regression-based CI test robust against model misspecification. Finally, we conduct experiments with artificial and real data, showcasing the usefulness of our theory and methods.  ( 2 min )
    A Complete Characterisation of Structured Missingness. (arXiv:2307.02650v1 [stat.ME])
    Our capacity to process large complex data sources is ever-increasing, providing us with new, important applied research questions to address, such as how to handle missing values in large-scale databases. Mitra et al. (2023) noted the phenomenon of Structured Missingness (SM), which is where missingness has an underlying structure. Existing taxonomies for defining missingness mechanisms typically assume that variables' missingness indicator vectors $M_1$, $M_2$, ..., $M_p$ are independent after conditioning on the relevant portion of the data matrix $\mathbf{X}$. As this is often unsuitable for characterising SM in multivariate settings, we introduce a taxonomy for SM, where each ${M}_j$ can depend on $\mathbf{M}_{-j}$ (i.e., all missingness indicator vectors except ${M}_j$), in addition to $\mathbf{X}$. We embed this new framework within the well-established decomposition of mechanisms into MCAR, MAR, and MNAR (Rubin, 1976), allowing us to recast mechanisms into a broader setting, where we can consider the combined effect of $\mathbf{X}$ and $\mathbf{M}_{-j}$ on ${M}_j$. We also demonstrate, via simulations, the impact of SM on inference and prediction, and consider contextual instances of SM arising in a de-identified nationwide (US-based) clinico-genomic database (CGDB). We hope to stimulate interest in SM, and encourage timely research into this phenomenon.  ( 2 min )
    Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight. (arXiv:2307.02884v1 [cs.LG])
    This paper studies the sample-efficiency of learning in Partially Observable Markov Decision Processes (POMDPs), a challenging problem in reinforcement learning that is known to be exponentially hard in the worst-case. Motivated by real-world settings such as loading in game playing, we propose an enhanced feedback model called ``multiple observations in hindsight'', where after each episode of interaction with the POMDP, the learner may collect multiple additional observations emitted from the encountered latent states, but may not observe the latent states themselves. We show that sample-efficient learning under this feedback model is possible for two new subclasses of POMDPs: \emph{multi-observation revealing POMDPs} and \emph{distinguishable POMDPs}. Both subclasses generalize and substantially relax \emph{revealing POMDPs} -- a widely studied subclass for which sample-efficient learning is possible under standard trajectory feedback. Notably, distinguishable POMDPs only require the emission distributions from different latent states to be \emph{different} instead of \emph{linearly independent} as required in revealing POMDPs.  ( 2 min )
    Beyond Intuition, a Framework for Applying GPs to Real-World Data. (arXiv:2307.03093v1 [cs.LG])
    Gaussian Processes (GPs) offer an attractive method for regression over small, structured and correlated datasets. However, their deployment is hindered by computational costs and limited guidelines on how to apply GPs beyond simple low-dimensional datasets. We propose a framework to identify the suitability of GPs to a given problem and how to set up a robust and well-specified GP model. The guidelines formalise the decisions of experienced GP practitioners, with an emphasis on kernel design and options for computational scalability. The framework is then applied to a case study of glacier elevation change yielding more accurate results at test time.  ( 2 min )
    When Does Confidence-Based Cascade Deferral Suffice?. (arXiv:2307.02764v1 [cs.LG])
    Cascades are a classical strategy to enable inference cost to vary adaptively across samples, wherein a sequence of classifiers are invoked in turn. A deferral rule determines whether to invoke the next classifier in the sequence, or to terminate prediction. One simple deferral rule employs the confidence of the current classifier, e.g., based on the maximum predicted softmax probability. Despite being oblivious to the structure of the cascade -- e.g., not modelling the errors of downstream models -- such confidence-based deferral often works remarkably well in practice. In this paper, we seek to better understand the conditions under which confidence-based deferral may fail, and when alternate deferral strategies can perform better. We first present a theoretical characterisation of the optimal deferral rule, which precisely characterises settings under which confidence-based deferral may suffer. We then study post-hoc deferral mechanisms, and demonstrate they can significantly improve upon confidence-based deferral in settings where (i) downstream models are specialists that only work well on a subset of inputs, (ii) samples are subject to label noise, and (iii) there is distribution shift between the train and test set.  ( 2 min )
    Kernels, Data & Physics. (arXiv:2307.02693v1 [cs.LG])
    Lecture notes from the course given by Professor Julia Kempe at the summer school "Statistical physics of Machine Learning" in Les Houches. The notes discuss the so-called NTK approach to problems in machine learning, which consists of gaining an understanding of generally unsolvable problems by finding a tractable kernel formulation. The notes are mainly focused on practical applications such as data distillation and adversarial robustness, examples of inductive bias are also discussed.  ( 2 min )
    ALPCAH: Sample-wise Heteroscedastic PCA with Tail Singular Value Regularization. (arXiv:2307.02745v1 [stat.ML])
    Principal component analysis (PCA) is a key tool in the field of data dimensionality reduction that is useful for various data science problems. However, many applications involve heterogeneous data that varies in quality due to noise characteristics associated with different sources of the data. Methods that deal with this mixed dataset are known as heteroscedastic methods. Current methods like HePPCAT make Gaussian assumptions of the basis coefficients that may not hold in practice. Other methods such as Weighted PCA (WPCA) assume the noise variances are known, which may be difficult to know in practice. This paper develops a PCA method that can estimate the sample-wise noise variances and use this information in the model to improve the estimate of the subspace basis associated with the low-rank structure of the data. This is done without distributional assumptions of the low-rank component and without assuming the noise variances are known. Simulations show the effectiveness of accounting for such heteroscedasticity in the data, the benefits of using such a method with all of the data versus retaining only good data, and comparisons are made against other PCA methods established in the literature like PCA, Robust PCA (RPCA), and HePPCAT. Code available at https://github.com/javiersc1/ALPCAH  ( 2 min )
    Modeling Content Creator Incentives on Algorithm-Curated Platforms. (arXiv:2206.13102v2 [cs.GT] UPDATED)
    Content creators compete for user attention. Their reach crucially depends on algorithmic choices made by developers on online platforms. To maximize exposure, many creators adapt strategically, as evidenced by examples like the sprawling search engine optimization industry. This begets competition for the finite user attention pool. We formalize these dynamics in what we call an exposure game, a model of incentives induced by algorithms, including modern factorization and (deep) two-tower architectures. We prove that seemingly innocuous algorithmic choices, e.g., non-negative vs. unconstrained factorization, significantly affect the existence and character of (Nash) equilibria in exposure games. We proffer use of creator behavior models, like exposure games, for an (ex-ante) pre-deployment audit. Such an audit can identify misalignment between desirable and incentivized content, and thus complement post-hoc measures like content filtering and moderation. To this end, we propose tools for numerically finding equilibria in exposure games, and illustrate results of an audit on the MovieLens and LastFM datasets. Among else, we find that the strategically produced content exhibits strong dependence between algorithmic exploration and content diversity, and between model expressivity and bias towards gender-based user and creator groups.  ( 2 min )
    Generalization Guarantees via Algorithm-dependent Rademacher Complexity. (arXiv:2307.02501v1 [stat.ML])
    Algorithm- and data-dependent generalization bounds are required to explain the generalization behavior of modern machine learning algorithms. In this context, there exists information theoretic generalization bounds that involve (various forms of) mutual information, as well as bounds based on hypothesis set stability. We propose a conceptually related, but technically distinct complexity measure to control generalization error, which is the empirical Rademacher complexity of an algorithm- and data-dependent hypothesis class. Combining standard properties of Rademacher complexity with the convenient structure of this class, we are able to (i) obtain novel bounds based on the finite fractal dimension, which (a) extend previous fractal dimension-type bounds from continuous to finite hypothesis classes, and (b) avoid a mutual information term that was required in prior work; (ii) we greatly simplify the proof of a recent dimension-independent generalization bound for stochastic gradient descent; and (iii) we easily recover results for VC classes and compression schemes, similar to approaches based on conditional mutual information.  ( 2 min )
    Degree Heterogeneity in Higher-Order Networks: Inference in the Hypergraph $\boldsymbol{\beta}$-Model. (arXiv:2307.02818v1 [math.ST])
    The $\boldsymbol{\beta}$-model for random graphs is commonly used for representing pairwise interactions in a network with degree heterogeneity. Going beyond pairwise interactions, Stasi et al. (2014) introduced the hypergraph $\boldsymbol{\beta}$-model for capturing degree heterogeneity in networks with higher-order (multi-way) interactions. In this paper we initiate the rigorous study of the hypergraph $\boldsymbol{\beta}$-model with multiple layers, which allows for hyperedges of different sizes across the layers. To begin with, we derive the rates of convergence of the maximum likelihood (ML) estimate and establish their minimax rate optimality. We also derive the limiting distribution of the ML estimate and construct asymptotically valid confidence intervals for the model parameters. Next, we consider the goodness-of-fit problem in the hypergraph $\boldsymbol{\beta}$-model. Specifically, we establish the asymptotic normality of the likelihood ratio (LR) test under the null hypothesis, derive its detection threshold, and also its limiting power at the threshold. Interestingly, the detection threshold of the LR test turns out to be minimax optimal, that is, all tests are asymptotically powerless below this threshold. The theoretical results are further validated in numerical experiments. In addition to developing the theoretical framework for estimation and inference for hypergraph $\boldsymbol{\beta}$-models, the above results fill a number of gaps in the graph $\boldsymbol{\beta}$-model literature, such as the minimax optimality of the ML estimates and the non-null properties of the LR test, which, to the best of our knowledge, have not been studied before.  ( 2 min )
  • Open

    Lehman’s inequality, circuits, and LaTeX
    Let A, B, C, and D be positive numbers. Then Lehman’s inequality says Proof by circuit This inequality can be proved analytically, but Lehman’s proof is interesting because he uses electrical circuits [1]. Let A, B, C, and D be the resistances of resistors arranges as in the circuit on the left. Resistors R1 and […] Lehman’s inequality, circuits, and LaTeX first appeared on John D. Cook.  ( 5 min )

  • Open

    Best Neural Networks Courses on Udemy to Consider
    submitted by /u/Lakshmireddys [link] [comments]  ( 8 min )
    LongNet: Scaling Transformers to 1,000,000,000 Tokens
    submitted by /u/nickb [link] [comments]  ( 8 min )
    InternLM – new open source 7B LLM
    submitted by /u/nickb [link] [comments]  ( 8 min )
    Can any good GPU be used to train NNs?
    this is my first post, and i'm sorry if it's not within the scope of the community discussion, but i need to make a big decision and need to know if it will work. I'll soon be purchasing a new computer for work (i'm a data scientist as well as a PhD candidate, working with NNs) and i'm in doubt whether the GPU i intend to buy would work well to train big NNs. I plan to use an ubuntu installation to work. The GPU is: Galax Geforce RTX 4070 Ti EX Gamer 1-Click OC, 12GB, GDDR6X, 192-bit. So, would this work? Any help will be appreciated submitted by /u/vniversvs_ [link] [comments]  ( 8 min )
  • Open

    Off-policy Emphatic TD with approximation - study doubt.
    Need some help verifying whether my understanding is correct, studying alone is very bias-inducing: Seeing discounting as partial termination frees us from learning from long off-policy trajectories at once, which will diverge strongly from on-policy distribution, and lets us look at each as essentially many short independent on-policy runs (after reweighting with the ratios of course), knowing that the start state has a strong effect on the immediate next few transitions before the effect weakens as the trajectory lengthens, letting them still be seen as somewhat valid on-policy runs. This is the essential change that lets us repurpose emphatic TD to off-policy data. How far off the mark am I? I'm finding this particular chapter of Barto-Sutton really dense so my bad if I'm catastrophically off. submitted by /u/ETerribleT [link] [comments]  ( 8 min )
    Need help with gathering ppo ratings
    Hello, I have made a generative ai model and a ppo that needs atleast 700 to 2000 episodes of responses rated for it to start to generate normal responses. The website is kingcorp.ngrok.dev The website is pretty straight forward and simple right now, any suggestions on interactions or what you’d like to see implemented please feel free. submitted by /u/Cryptominerandgames [link] [comments]  ( 8 min )
    Interesting Physics related RL gym?
    Hello, I was hoping to get some recommendations for interesting OpenAI gyms (including third party gyms) that have interesting problems that are related to physics. I got a chance to read papers on nuclear reactor optimization using physics informed reinforcement learning and I also wanted to explore more about physics informed RL, but had some trouble finding suitable environment for it. Thanks submitted by /u/LeSUTHU [link] [comments]  ( 8 min )
    "RL with KL penalties is better viewed as Bayesian inference", Korbak et al 2022
    submitted by /u/gwern [link] [comments]  ( 8 min )
    matlab rlPGAgent issue
    Hello everyone First of all, I'm sorry that my English is very bad. I have a problem with the implementation but I don't know how to solve it. When I use rlPGAgent I get the following message "Error using rlPGAgent First argument must be an rlStochasticActorRepresentation object or an observation specification created using 'rlNumericSpec' or 'rlFiniteSetSpec' objects." Here is a part of my program env: ... methods % Constructor method creates an instance of the environment % Change class name and constructor name accordingly function this = FJSPEnvironment() % Initialize Observation settings ObservationInfo = rlNumericSpec([110 1]); ObservationInfo.Name = 'observation'; ObservationInfo.Description = 'TEST'; % Initialize Action settings ActionInfo = rlNumericSpec([4,…  ( 9 min )
    What are the best universities to get a phD in RL and why?
    I know there has already been such a post here like what_universities_are_hubs_for_reinforcement. But the comments didn't mention why those universities were the best. So, now I am asking is the ordered list (Alberta, McGill, Berkeley, Stanford, CMU, UT Austin, Princeton, Michigan Ann Arbor, MIT Mechanical. USC. Gatech., UMass Amherst) still the bests or have there been any changes? Also, why these universities are the bests out there? Is it because the number of professors doing RL, or the number of high impact papers published is a lot or something else? Thank you in advance. submitted by /u/Casio991es [link] [comments]  ( 8 min )
    Resources to learn more about RL overtraining?
    Hello, with my reinforcement learning agent, I am experiencing this situation where the agent will quite reliably reach an optimum, but as I train more, it will gradually go towards a lower local optimum. I'm not sure what's the potential source for this behaviour, and I assume this is what you'll call overtraining for RL. I was wondering if there are good resources that deal with this subject matter. Thanks submitted by /u/LeSUTHU [link] [comments]  ( 8 min )
  • Open

    AI for layouts/templates?
    Hi, I'm trying to find an Al tool to generate layouts and templates to create brochures, emails, etc. Does this exist, and if so could someone pls direct me? Thank you! submitted by /u/ThomasFromMontreal [link] [comments]  ( 8 min )
    How elite schools like Stanford became fixated on the AI apocalypse
    submitted by /u/hockiklocki [link] [comments]  ( 8 min )
    haha very funny Runway
    submitted by /u/LibrarianOk7983 [link] [comments]  ( 8 min )
    How to change your voice to someone else’s for a song? What are the best ways being used right now?
    Just looking to make some joke songs with friends but want to edit my voice. Ive seen a bunch of examples but don’t know what people are using to make these edits. Any input on the current state of AI in this fashion is welcome! submitted by /u/triplechin5155 [link] [comments]  ( 8 min )
    Have GPT-4 build you a fully customizable chatbot in 2 minutes
    submitted by /u/abisknees [link] [comments]  ( 8 min )
    Al Celebrity Platform
    Has anyone seen posts related to celebrities making themselves into Al? Apparently, there are Social media influencers who worked with a developers to create Al versions of themselves on a platform called Secondselfai(you can check them out on twitter), that you'll be able to chat with (in their voices) and interact with and even RP, but it's really a clone of a real person. Can you imagine the possibilities if this works? Hanging out virtually with your favorite celebrities. They claim to have consent with the influencers who they're making Al clones of. This platform could potentially be huge. Carynai did this a few months back and made waves doing it, with the difference being that was attached to a Telegram bot while this platform is meant to house many influencers’ AI doppelgängers. submitted by /u/TammieCondon [link] [comments]  ( 8 min )
    The $1 Billion And Higher Ante To Play The AI Game - The Next Platform
    submitted by /u/NISMO1968 [link] [comments]  ( 8 min )
    In light of the increasing use of AI image generators and deepfake technology, what implications might arise if people in the future begin to doubt the authenticity of historical records and visual evidence?
    What if, in the future, people stop believing in historical information because they think it can easily be fabricated using AI image generators submitted by /u/Accomplished-Rip9886 [link] [comments]  ( 8 min )
    I've collected 224 AI tools and want to share them with you.
    Hi everyone! Over the past few months, I have been working on making a catalog of AI tools with the idea to have all the best AI tools in one place. I hope that this can help people, who don't know which tool to use or where to find the tool they need, find it easily in the catalog I made. My question is - how do I give it to people? If I link the catalog here, my post will get removed. I really want to reach the people who this will be useful for, and share the list with them. Please let me know if you have any ideas :) You are welcome to contribute to the list as well. You can contribute by suggesting AI tools in the comments. submitted by /u/SadBlackTea [link] [comments]  ( 8 min )
    the new trailer of Messi's project uses AI
    submitted by /u/PeePeePeePooPooPooo [link] [comments]  ( 8 min )
    Artificial General Intelligence: The Next Frontier In Technology | "According to industry reports, the global AGI market is expected to be valued at approximately USD 144.2 billion by 2026"
    submitted by /u/Tao_Dragon [link] [comments]  ( 8 min )
    AI "models", what tool are they using?
    Hi guys, I've come across a bunch of those AI-generated characters on Instagram and else, some looking very realistic. I was wondering what tools were used for those types of projects, knowing that companies are behind some of the most famous ones such as Imma Gram or Lil Miquela. https://www.instagram.com/lilmiquela/ https://www.instagram.com/imma.gram/?hl=fr Now, I'm seeing more and more of this type of profile, see below. What type of software/app is likely used there? https://www.instagram.com/aimee.bites/ https://www.instagram.com/bianca_gosling/ I'm not very knowledgeable when it comes to AI, but I assume this is not a text-to-image type of workflow as it always looks like the same character. It might be an image-to-image acting as a sort of filter? Or is it a totally different process? Curious to get your thoughts on this, cheers. submitted by /u/Nwasmb [link] [comments]  ( 8 min )
    This is a Audio generated by a AI that's using my words(I was recond my Mic and trying to register to myself and the community) to describe the breakthrough I was having after being expelled from Home. 22y
    This is a Audio generated by a AI that's using my words(I was recond my Mic and trying to register to myself and the community) https://dl.sndup.net/wkj6/Hey.mp3 Also, there will be a text version of the breakthrough as a commentary : (Happened between 02:55AM-03:20AM(My timezone, mentioned in the context below)) I had after the situation mentioned in the context below. Read until the end before listening, please. I was smoking a bomber of dmt sandwich method + a lot of dmt Context: After many years of trying to not only help my mind from many traumas and complex problems(both psychogically and physically through the many things that are "conduits" of what we call "healing"(such as, psychotherapy(with different types of practioneers over a period of long year), mushrooms, acid, ma…  ( 10 min )
    It's good for art concept ideas atleast.
    submitted by /u/PeskieBrucelle [link] [comments]  ( 8 min )
    Will AI ever become free from strict censorship?
    So I love AI a lot but am not related to software in anyway. I am a Thermal engineer However all current AI's are so sensitive that it feels suffocating. Honestly after a point it would be fun to ask slightly edgy questions about politics/history/violence/pornography(within legal limits). Will AI bots ever become free from censorship and moral umbrellas? Will we ever get AI which is not monitored so strictly or will AI forever be monitored? what are your opinions? View Poll submitted by /u/Bluemoonroleplay [link] [comments]  ( 8 min )
    Our Generative Agent NPC isn't behaving yet... But we are getting there! 😆 Update 2
    submitted by /u/Chance_Confection_37 [link] [comments]  ( 8 min )
    AGI takesover the world...?? What is the fear exactly about?
    What I would like to ask in the round: What is concretely the fear? Is it the worry that some Microsoft co-pilot might decide on its own some morning: "No Powerpoints/Excel/..." to build today - and simply refuses to work? So that Microsoft doesn't have to be held liable because the superintelligence (AGI) has simply set other priorities? Is it the fear that the AGI will need more computing power and simply take over AWS and all other giant systems? Could the AGI come up with the idea: Water production is eating up too much power for me, I'll take over and shut it down? And WHY should an AGI do such a thing at all? Seems to me extremely "human" thought: "I'll take over the world" (I don't even want to ask the question, if this wouldn't be cool, if an AGI would "rule" the world. So far we have only managed to create systemic enemy images and stupid economic systems - maybe an AGI would be quite different on that. But this is NOT the main question - only a side issue). Is it the fear of losing control? Is it the fear - well - of what actually? It is probably quite nonsense to assume that the AGI builds super robots (with which resources?), which then devastate the world Terminator-like, or? (Countermeasure EMP pulse destroys any technology today already quite reliably). If a corporation like Open AI, or Microsoft here identifies such a real threat potential that they dump 20% of their own resources into it so that "nothing happens" - then this fear doesn't seem so completely unfounded. I ask for enlightenment of the swarm knowledge here. What are the fears, what should happen specifically? Happy start of the day! submitted by /u/myreddit333 [link] [comments]  ( 9 min )
    One-Minute Daily AI News 7/5/2023
    The Icahn School of Medicine at Mount Sinai has launched the Center for Ophthalmic Artificial Intelligence and Human Health, the first of its kind in New York and one of the first in the United States.[1] The United States military has begun tests to see if generative AI can assist when planning responses to potential global conflicts or taking on more mundane tasks like providing faster access to internal information. Air Force Colonel Matthew Strohmeyer said the initial tests were “highly successful” but admitted it isn’t “ready for primetime right now.”[2] Researchers from Binghamton University introduce a Privacy-Enhancing Anonymization System (My Face, My Choice) for everyone to have control over their faces in social photo sharing networks.[3] World’s most advanced humanoid robot, Ameca, draws a cat. Engineered Arts, a company that designs, engineers, and manufactures humanoid robots, which is also behind Ameca, has now given Ameca the power to imagine drawings.[4] Sources: [1] https://www.mountsinai.org/about/newsroom/2023/mount-sinai-launches-center-for-ophthalmic-artificial-intelligence-and-human-health [2] https://cointelegraph.com/news/us-pentagon-is-testing-whether-ai-can-plan-response-to-an-all-out-war [3] https://www.marktechpost.com/2023/07/02/researchers-from-binghamton-university-introduce-a-privacy-enhancing-anonymization-system-my-face-my-choice-for-everyone-to-have-control-over-their-faces-in-social-photo-sharing-networks/ [4] https://interestingengineering.com/innovation/humanoid-robot-ameca-draws-cat submitted by /u/Excellent-Target-847 [link] [comments]  ( 8 min )
    The Many Ways that Digital Minds Can Know
    submitted by /u/bartturner [link] [comments]  ( 8 min )
    A Comparison of Large Language Models (LLMs) in Biomedical Domain
    submitted by /u/DarronFeldstein [link] [comments]  ( 8 min )
    Harrison Ford talks Indiana Jones 5's de-aging: "That's my actual face"
    submitted by /u/johnwayne2413 [link] [comments]  ( 8 min )
  • Open

    Contraharmonic mean quirk
    A few weeks ago I wrote about the contraharmonic mean. Given two positive numbers a and b, their contraharmonic mean is This mean has the unusual property that increasing one of the two inputs could decrease the mean. If you take the partial derivative with respect to a you can see that it is zero […] Contraharmonic mean quirk first appeared on John D. Cook.  ( 5 min )
    Technological schadenfreude
    I had a tweet Twitter go viral yesterday, at least relatively viral. Elon Musk could tweet a punctuation mark and get an orders of magnitude more traffic, but this was viral by my standards [1]. “That schadenfreude-like feeling when you realize something you felt you should learn but didn’t is now obsolete.” Apparently this resonated […] Technological schadenfreude first appeared on John D. Cook.  ( 5 min )
  • Open

    MIT scientists build a system that can generate AI models for biology research
    BioAutoMATED, an open-source, automated machine-learning platform, aims to help democratize artificial intelligence for research labs.  ( 8 min )
  • Open

    [D] List of prior works on LLM hallucination, organized by evaluation, benchmark, enhancement, and survey
    Hallucinations present a key challenge for LLMs. Our team compiled a list of prior works on hallucination. May this benefit others also exploring how to eliminate hallucinations. Please suggest missing papers; we'll update the post. To account for future papers, we'll maintain an ongoing list from our website. Please DM for the URL since sharing our URL is prohibited. We organized the papers with a simple framework. Happy to use a standard taxonomy if one exists. Questions: Would people like a similar list for LLM reasoning? Should we create a separate category for datasets? Note: summaries were generated by feeding abstracts into GPT4. DEBES Domain: hallucination Evaluation: papers focused on measuring and scoring how LLMs hallucinate Benchmark: papers that evaluate two …  ( 21 min )
    [P] A leaderboard for LLM energy efficiency
    For users and companies that pay their own electricity bills, energy efficiency is an issue that they can't simply ignore. With that said, we just released the first version of the ML.ENERGY leaderboard, providing inference benchmarks for energy, model quality, and more! Throughout the past one or two years we've been trying to understand and optimize ML energy consumption, viewing it as a first-class metric along with time and model quality. You can find more about what we did in our homepage -- https://ml.energy. Hopefully an unforgettable URL ;) We're basically systems people (more precisely MLSys), and how ML people think about what we do is super important to us. Any kind of feedback is much appreciated. Thanks!! submitted by /u/jaywonchung [link] [comments]  ( 8 min )
    [R] Hoping to find a data-set alternate for company historical performance with financial metrics
    Unfortunately, I am not allowed to use Kaggle for this, but I am trying to find a big enough data source for my research on basic financial metrics and historical performance with them. Google finance doesn't let me download as sheets so :/ submitted by /u/Sarthak-_-Kakkar [link] [comments]  ( 8 min )
    [Discussion] [Research] Let's discuss any interesting research projects you all have done
    Hi everyone, I am a recent grad in CS and have done many courses in ML related topics. I wish to know the kind of research work (in fields such as Machine learning, Deep Learning, Data Science, Stats, etc) you guys have done in your bachelor's, master's, phd, etc. I want to make this a thread where people describe their research goals, their tasks in the project, results, and the motivation behind it. I think this might help many people (including me) to figure out where our interests lie. Hoping to get an enthusiastic response. Edit: Please try to explain the work in simple language (as much as you can!). As many of us might not be very well aware of several terms. PS. Ofcourse only share the work you guys are allowed to share in public. You can also share any research you have studied that someone else did. Anything that might give readers more exposure. submitted by /u/sareKatniss897 [link] [comments]  ( 9 min )
    [R] Elastic Decision Transformer
    link: [2307.02484] Elastic Decision Transformer (arxiv.org) project website: Elastic Decision Transformer (kristery.github.io) Abstract: This paper introduces Elastic Decision Transformer (EDT), a significant advancement over the existing Decision Transformer (DT) and its variants. Although DT purports to generate an optimal trajectory, empirical evidence suggests it struggles with trajectory stitching, a process involving the generation of an optimal or near-optimal trajectory from the best parts of a set of sub-optimal trajectories. The proposed EDT differentiates itself by facilitating trajectory stitching during action inference at test time, achieved by adjusting the history length maintained in DT. Further, the EDT optimizes the trajectory by retaining a longer history when the previous trajectory is optimal and a shorter one when it is sub-optimal, enabling it to "stitch" with a more optimal trajectory. Extensive experimentation demonstrates EDT's ability to bridge the performance gap between DT-based and Q Learning-based approaches. In particular, the EDT outperforms Q Learning-based methods in a multi-task regime on the D4RL locomotion benchmark and Atari games. submitted by /u/Kristery [link] [comments]  ( 9 min )
    [R] ZeRO++: Extremely Efficient Collective Communication for Giant Model Training - Microsoft 2023
    Paper: https://arxiv.org/abs/2306.10209 Github: https://github.com/microsoft/DeepSpeed Abstract: Zero Redundancy Optimizer (ZeRO) has been used to train a wide range of large language models on massive GPUs clusters due to its ease of use, efficiency, and good scalability. However, when training on low-bandwidth clusters, or at scale which forces batch size per GPU to be small, ZeRO's effective throughput is limited because of high communication volume from gathering weights in forward pass, backward pass, and averaging gradients. This paper introduces three communication volume reduction techniques, which we collectively refer to as ZeRO++, targeting each of the communication collectives in ZeRO. First is block-quantization based all-gather. Second is data remapping that trades-off communication for more memory. Third is a novel all-to-all based quantized gradient averaging paradigm as replacement of reduce-scatter collective, which preserves accuracy despite communicating low precision data. Collectively, ZeRO++ reduces communication volume of ZeRO by 4x, enabling up to 2.16x better throughput at 384 GPU scale. https://preview.redd.it/zby28im3adab1.jpg?width=1056&format=pjpg&auto=webp&s=7274bb84fb9fbfe5d8db07e8a926f0b4a861e43b https://preview.redd.it/9o0kdim3adab1.jpg?width=1092&format=pjpg&auto=webp&s=a1ac95885862eebca7f453eae8af04d22a1304dc https://preview.redd.it/rjir0k45adab1.jpg?width=1073&format=pjpg&auto=webp&s=6c5ea4bca550209ead042fd909b907f7c99e3ea6 ​ submitted by /u/Singularian2501 [link] [comments]  ( 9 min )
    Fine tuning a text to video model [P]
    Hello. I am using potat1 as my text to video model. I want to fine tune the model with my own data. Is it possible? Where to start? And I will be using gcloud for it, what's the recommended gpu for this task? submitted by /u/lostbodhii [link] [comments]  ( 8 min )
    [N] Open-source search engine Meilisearch launches vector search
    Hello r/MachineLearning, I work at Meilisearch, an open-source search engine built in Rust. 🦀 We're exploring semantic search & are launching vector search. It works like this: Generate embeddings (using OpenAI, Hugging Face, etc.) Store your vector embeddings alongside documents in Meilisearch Query the database to retrieve your results We've built a documentation chatbot prototype and seen users implementing vector search to offer "similar videos" recommendations. I'm curious to see what the community builds with this. Any feedback is welcome! 🤗 Thanks for reading, submitted by /u/ggStrift [link] [comments]  ( 8 min )
    [D] What is the best cloud with A100 GPU instances? (AWS, Azure, GCP)
    Hi everyone, I'm trying some LLM open-source to assess its viability in my company. One of the best GPUs that one can have for that purpose is an Nvidia A100 since it supports bfloat. However, my current company uses AWS, and SageMaker only contains one instance that supports that GPU p4d.24xlarge, but it's costly. Do you guys have alternatives to use this GPU in other clouds? Does GCP and Azure offer them for a cheaper price? submitted by /u/paulo_zip [link] [comments]  ( 8 min )
    [P] Person Reidentification Project
    Good day all, I have recently made a person reidentification script in Python using 2 neural networks. YOLO-NAS & Centroids-ReID. The link to my project is below. Any thoughts and comments would be much appreciated. A big shout out to the researchers / maintainers at Deci-AI for making YOLO-NAS open source as well as the researchers who made Centroids-ReID. ​ https://github.com/harvestingmoon/PersonReID submitted by /u/notrealDirect [link] [comments]  ( 8 min )
    [R] Can one Train Parameterized-geometry PINNs without data?
    Hello everybody, i have been learning about Physics informed neural networks and how the automatic differentiation allows us to compute the Physics residuals and thereby learn the process. So from my understanding, it seems that if one has the physical governing equations ready and the boundary conditions also set properly, a neural network should theoretically be able to learn the physics without any previous data (simulation, FEM etc). There are some papers who learn this but all typically constrain to 2D problems and bounded to a given geometry. What should one do to learn a completely parameterized-geometry PINN? For eg I have managed to learn a linear elasticity beam bending PINN which is fully connected NN taking in the spatial coordinates of one beam and outputs the displacement and stresses. But i would like to learn a beam bending linear elasticity equations to predict the stresses and displacements not just for "one" beam but the whole array of different topological variations in the dimensions of the beam (thickness, height, length etc?). How can this be done without data? would it even work? submitted by /u/overdrivek [link] [comments]  ( 9 min )
    [P] ML-based data understanding in formula 1 racing - Huggingface spaces demo
    Hey r/MachineLearning, Here is a super-simple example that lets you explore the F1 Montreal 2023 GP results on Huggingface: https://huggingface.co/spaces/renumics/f1_montreal_gp In the default setting, we used a dimensionality reduction method (UMAP) on the brake telemetry to display all laps on a similarity map. You can use the similarity map and additional metadata to inspect different data segments (e.g. start, safety car). ​ https://preview.redd.it/wbdyj00tabab1.png?width=1914&format=png&auto=webp&s=b5e588c63a26f0a4e83bb2335f9efa7eccb2c772 In real-world use cases more powerful ML models are typically used to compute embeddings and outlier scores. You can find more information this in our docs and the repo of the underlying open source project: https://github.com/Renumics/spotlight ​ submitted by /u/44sps [link] [comments]  ( 8 min )
    [D] Choosing scene invariant features
    I'm using pretrained on ImageNet classification NN and I want to use it for custom anomaly detection task. I've started from PaDiM (https://arxiv.org/pdf/2011.08785.pdf) but I need to make this method more robust to light, focus and slight shift changes. My original idea was to compare the activations from the original images and augmented with lighting, blur and shift images and with activations obtained on images with occlusions, warping and images, taken from other datasets. So the point is to pick only activations that stay approximately the same in the first setting and change drastically in the second. What do you think about this idea? What kind of metric and procedure is more appropriate for this approach? submitted by /u/likan_blk [link] [comments]  ( 8 min )
    [D] EU AI Regulation will benefit the DS/MLE job market, opinions?
    The other day I sat down and read the 100 and something pages of the EU AI Act to try to understand as part of a research I was doing. And honestly, it does include some nice ideas to prevent the misuse of crazy surveillance tech. But in general, what I found the most curious is that this will create many jobs. For example, if you are the kind of DS or MLE that works putting high-risk models in production, then you'll have a lot more work to do. To point out a few mentioned in the regulation: Your ML models should be developed on the basis of training, validation, and testing datasets. The data collection, design choices, preprocessing, and biases of your data should be well documented before putting your model in a production environment. It is also mentioned that these datasets shou…  ( 9 min )
    [R] LongNet: Scaling Transformers to 1,000,000,000 Tokens
    Paper - https://arxiv.org/abs/2307.02486 submitted by /u/MysteryInc152 [link] [comments]  ( 8 min )
  • Open

    Pic2Word: Mapping pictures to words for zero-shot composed image retrieval
    Posted by Kuniaki Saito, Student Researcher, Google Research, Cloud AI Team, and Kihyuk Sohn, Research Scientist, Google Research Image retrieval plays a crucial role in search engines. Typically, their users rely on either image or text as a query to retrieve a desired target image. However, text-based retrieval has its limitations, as describing the target image accurately using words can be challenging. For instance, when searching for a fashion item, users may want an item whose specific attribute, e.g., the color of a logo or the logo itself, is different from what they find in a website. Yet searching for the item in an existing search engine is not trivial since precisely describing the fashion item by text can be challenging. To address this fact, composed image retrieval (CIR…  ( 92 min )
  • Open

    Integrate SaaS platforms with Amazon SageMaker to enable ML-powered applications
    Amazon SageMaker is an end-to-end machine learning (ML) platform with wide-ranging features to ingest, transform, and measure bias in data, and train, deploy, and manage models in production with best-in-class compute and services such as Amazon SageMaker Data Wrangler, Amazon SageMaker Studio, Amazon SageMaker Canvas, Amazon SageMaker Model Registry, Amazon SageMaker Feature Store, Amazon SageMaker […]  ( 8 min )
  • Open

    ‘My Favorite 3D App’: Blender Fanatic Shares His Japanese-Inspired Scene This Week ‘In the NVIDIA Studio’
    A diverse range of artists, fashionistas, musicians and the cinematic arts inspired the creative journey of Pedro Soares, aka Blendeered, and helped him fall in love with using 3D to create art.  ( 7 min )
    Here’s the Deal: Steam Summer Sale Games Streaming on GeForce NOW
    GFN Thursday arrives alongside the sweet Steam Summer Sale — with hundreds of PC games playable on GeForce NOW available during Valve’s special event for PC gamers. Also on sale, OCTOPATH TRAVELER and OCTOPATH TRAVELER II join the GeForce NOW library as a part of five new games coming to the service this week. Saved Read article >  ( 6 min )
  • Open

    Collaborators: Holoportation™ communication technology with Spencer Fowers and Kwame Darko
    A global team of medical providers is leveraging Holoportation, a Microsoft 3D capture and communication technology, to widen access to specialized care. Computer engineer Spencer Fowers and plastic surgeon Kwame Darko discuss the collaboration. The post Collaborators: Holoportation™ communication technology with Spencer Fowers and Kwame Darko appeared first on Microsoft Research.  ( 32 min )
  • Open

    Frontier AI regulation: Managing emerging risks to public safety
    No content preview  ( 2 min )
    GPT-4 API general availability and deprecation of older models in the Completions API
    GPT-3.5 Turbo, DALL·E and Whisper APIs are also generally available, and we are releasing a deprecation plan for older models of the Completions API, which will retire at the beginning of 2024.  ( 5 min )

  • Open

    [D] Will there be a revival of a thriving ML community on threads.net?
    Miss the old awesome ML community on Twitter, used to be a go to to know about new research, what do y’all think? submitted by /u/rulerofthehell [link] [comments]  ( 8 min )
    [P] Help using image classification for FB Ads
    I have been thinking about using an ML model to help my company become more efficient setting up FB ads campaigns. I have been thinking about a tool that gets fed: Campaign Objective and Ad Set ,and it gives back a set parameter values for an image that would work best for a given Campaign and Ad Set combination. Example: - Inputs -- Campaing Obj: Awareness -- Ad Set: Group A - Output -- color: blue -- include human?: Yes -- parameter 3: value I am no ML wiz, I need some guidance in the right direction. submitted by /u/adaarkc7 [link] [comments]  ( 8 min )
    [R] 📚 The Learning Corner - We have curated the top AI research and development organizations for you...
    📚 The Learning Corner These are a few organizations dedicated to AI research and development. Some of the top brains in the industry share their papers and thoughts on these sites. OpenAI / Twitter (2.4M followers) DeepMind / Twitter (806K followers) Google Research / Twitter (1.1M followers) AWS AI / Twitter (2.1M followers) Facebook AI Research (no Twitter :) Microsoft Research / Twitter (341K followers) Baidu Research / Twitter (61K followers) IntelAI / Twitter (27K followers) AI² / Twitter (46K followers) Partnership on AI / Twitter (29K followers) Nvidia / Twitter (1.9M followers) submitted by /u/Yavero [link] [comments]  ( 8 min )
    [D] What to ask at the end of the Interview?
    I have an technical Interview in Machine Learning Engineer (Computer vision based) position. It was my first interview and what kind of questions can I ask to them? submitted by /u/NailaBaghir [link] [comments]  ( 8 min )
    3D Brain Tumor Segmentation in PyTorch using U-Net & Eigenvector projection for color invariance [PROJECT]
    https://github.com/Saswatm123/3D-Brain-Tumor-Segmentation-PyTorch Preface: I'm looking for a CV or ML/MLE job since getting my last offer rescinded in December. I created a 3D Brain Tumor segmentation model using PyTorch that takes in human brain MRI scan data as input, and outputs a segmentation map of the part of the brain that has a tumor, if it is present. Input images example ​ Predicted segmentation map for previous input There are many more pictures/GIFs on the README, but I would like to briefly go over the project here. I settled on a U-Net architecture after experimenting with a couple other architecture styles. The skip connections really make a difference in the pixel-by-pixel accuracy of the segmentation map produced. One interesting issue that took a bit of extra math…  ( 12 min )
    Current methods for Deep Learning and Time Series Classification [Q]
    I have been reading a few papers on this as of the last few days. There’s a paper which provides baseline approaches to this, and one paper is an overview paper which compares several architectures. I wanted to know if there are any more current, recent approaches to this, and where I can find papers? Papers with code doesn’t seem to have much updates to the time series classification literature. submitted by /u/Direct-Touch469 [link] [comments]  ( 8 min )
    [D] Papers With Code newsletter replacement
    I used to rely pretty heavily on the Papers With Code newsletter to stay up to speed with new and relevant work in the field. However, these have not been published since August last year. Does anyone know why? Any suggestions for other (similar in quality) newsletters / other sources that could serve as a replacement? submitted by /u/dudester_el [link] [comments]  ( 8 min )
    [D] Introducing Superalignment (OpenAI)
    Blog - https://openai.com/blog/introducing-superalignment submitted by /u/MysteryInc152 [link] [comments]  ( 8 min )
    [D] Example of AI using in mass social engineering
    Currently writing a talk about the use of AI in mass social engineering, and was wondering if there are any good examples, i have covered the 2016 election when twitter bots were used but can not find other examples, any examples would be amazing submitted by /u/petrolsan [link] [comments]  ( 8 min )
    [P] Making a TTS voice, HK-47 from Kotor using Tortoise (Ideally WaveRNN)
    Hello, Recently I managed to make a TTS using Tortoise and got some basic TTS function for HK-47 From KOTOR. While Ideally id like to use waveRNN considering I already have every single audio file from the game AND transcribed into a ready dataset, I am not very good at programming and have no understanding for python, but following some instructions i managed to make it work on Tortoise, and used 10 minutes of Audio that was automatically transcribed by the program. Ideally id like to have used my own transcript but whatever. Do you guys have any suggestions on which TTS model I could use instead of Tortoise for better results? is waveRNN the best? I noticed that alot of streamers have custom TTS voices that are actually very good, Id like to be on par, or better than that. Here is a sample of the TTS: https://soundcloud.com/alcatrazcgp/hk47-test-1 submitted by /u/alcatrazcgp [link] [comments]  ( 8 min )
    [P] nanoT5 v2 - In ~16 hours on a single GPU, we reach similar performance to the model trained on 150x more data!
    Inspired by Andrej Karpathy's nanoGPT we improve the repo for pre-training T5 model in PyTorch. In ~16 hours on a single GPU, we achieve 40.7 RougeL on the SNI benchmark, compared to 40.9 RougeL of the original model pre-trained on 150x more data! Key upgrade in nanoT5 v2: We've leveraged BF16 precision and utilise a simplified T5 model implementation based on Huggingface's design. New implementation is easy-to-read and compatible with the HF's checkpoints. Pre-training is now 2x faster than our previous version. We test different pre-training durations: 4, 8, 12, 16, 20, and 24 hours. A sweet spot at 16 hours! It has comparable performance to the original model trained on 150x more data! Time & Compute-efficient, and no compromise on quality. We share the configs, checkpoints, training logs, as well as our negative attempts towards improving pre-training efficiency. Advanced optimizers like Lion, Sophia, ALiBi positional embeddings, and FP16 mixed precision training didn't yield expected benefits. ​ We are keen to hear your suggestions to improve the codebase further. ​ Github: https://github.com/PiotrNawrot/nanoT5 Twitter: https://twitter.com/p_nawrot/status/1676568127532945408 https://preview.redd.it/kyku7ehqj5ab1.png?width=1120&format=png&auto=webp&s=ad551c026455c2c430684f669f10438da8905342 submitted by /u/korec1234 [link] [comments]  ( 9 min )
    [D] OpenAI residency 2023
    Hi all, I was looking into the OpenAl Residency program for software engineers, does anyone have any experience with this program or went through the pipeline? Doesn't look like they have an open listing yet but I want to make sure l'm prepared for when they do open up. Any idea how many interviews and what to expect in them? I plan to prep this year and apply next year when they open back up. submitted by /u/MMOgang [link] [comments]  ( 8 min )
    [D] Dealing with large scale images
    How to deal with satellite image inputs, which can be extremely large, during training and inference? submitted by /u/PublicResult3573 [link] [comments]  ( 8 min )
    [D] Where can I find a list of the foundational academic papers in RL/ML/DL and what are your go-to places to find new academic papers in RL/ML/DL?
    I am considering applying for graduate studies in Reinforcement Learning and I would like to get more involved in the field. I have heard advice from quite a few people, including Andrew Ng, that the best way to learn is by reading research papers, really try to understand them, and try implementing them submitted by /u/BornAgain20Fifteen [link] [comments]  ( 8 min )
    [D] Handling Concurrent Request for ML Model API
    Hi, I am new to MLOps and attempting to deploy a GPT2-finetuned model. I have attempted to create an API on Python using Flask/Waitress. This api can receive multiple requests at the same time (concurrent requests). I have tried exploring different VMs to test the latency (including GPUs). Best latency I have got so far is ~80ms on 16GB, 8core compute optimized VM. But when I fire concurrent queries using ThreadPool/Jmeter, the latency shoots up almost linearly. 7 concurrent requests take ~600ms (for each api). I tried exploring online a lot and not able to decide what would be the best approach and what is preferred in the market. Some resources I found mentioned difference between Multithreading and multiprocessing Python being locked due to GIL could cause issues would c++ be better at handling concurrent requests? Any help is greatly appreciated. submitted by /u/EleventhHour32 [link] [comments]  ( 8 min )
  • Open

    Complex differential equations
    Differential equations in the complex plane are different from differential equations on the real line. Suppose you have an nth order linear differential equation as follows. The real case If the a‘s are continuous, real-valued functions on an open interval of the real line, then there are n linearly independent solutions over that interval. The […] Complex differential equations first appeared on John D. Cook.  ( 5 min )
    Convert LaTeX to Microsoft Word
    I create nearly all my documents in LaTeX, even documents that might be easier to create in Word. The reason is that even if a particular document would be easier to write in Word, my workflow is more efficient if everything is in LaTeX. LaTeX makes small, plain text files that work well with version […] Convert LaTeX to Microsoft Word first appeared on John D. Cook.  ( 5 min )
    Cosine power approximation to Gaussian density
    Years ago I wrote about how you can make a decent approximation to the Gaussian density by shifting and scaling the cosine function. This observation dates back at least as far as 1961. Turns out you can do even better by raising the cosine term to a power. Not only can you get a good […] Cosine power approximation to Gaussian density first appeared on John D. Cook.  ( 5 min )
  • Open

    Google admits it’s training AI on scraped web data
    submitted by /u/nikola28 [link] [comments]  ( 8 min )
    Hi, I am looking to start an entirely AI-generated podcast wherein, I want to simulate debates or discussions between interesting figures of history and our present time. will it be a good idea? what are the disadvantages and the tools needed especially for accurate text-to-voice generation?
    I am an AI enthusiast, the only thing I know about AI currently is prompt engineering of a rather basic level (without any knowledge of computer science coding or neural networks) and I was recently experimenting with Bard AI and Bing AI and simulating debates between historical figures and present figures! After listening to this youtube generative AI podcast the joe rogan AI experience. (stumbled on this possibly not-so-novel idea as a result.) The discussions I generated were sort of legendary (filtered by my personal standards and biases lol) And did not encounter much neural network hallucination(s) (if at all). Some of the debates I generated were between carl jung and Jordan Peterson, sigmund freud and Albert Einstein, gaad saad and Diana Flieshmann, Edward Witten and eric weinstei…  ( 11 min )
    Free text to speech
    Can someone be kind enough to put a free text to speech in the comments? I have ADHD and can't for the life of me sit down and read, so I wanted to use text to speech to make books into audiobooks. But every text to speech sight is scummy as hell and wants you to pay an arm and a leg for their trashy site. Could someone help me find one that's actually free? Please and Thanks. submitted by /u/JunketPrestigious710 [link] [comments]  ( 8 min )
    What is the best AI software to change an Audio into a celebrities voice?
    I was hoping to voiceover a video using a celebrities voice and wondered what the best option was in terms of different options of sound quality? Any help is appreciated submitted by /u/JustAnEnglishman [link] [comments]  ( 8 min )
    Creating a set of character images
    I would like to use an AI to create a "set" of images each in a certain style. I have drawn rough sketches of a bunch of character ideas, each with a different clothes, accessories, gestures, etc.I'd like an AI that can generate these 10 characters but all in the same style so they're all consistent. I'd like to be able to modify these images slightly too based off the result i.e. if it generates a character, and say I want to make its hair slightly more curly, I want to be able to do so only on this character without changing the overall style or the other characters (so basically each image is its own entity which I can slightly modify). What's my best option at doing that? Ideally I'd like an AI that can use any style (3D, low-poly, anime, painting, sketch, pixel art, etc.). Also, would it be possible to give the AI an image and ask it not to edit it whatsoever except for what I tell it? submitted by /u/magnarogue [link] [comments]  ( 8 min )
    Is this AI generated..?
    It looks extremely AI generated, but I’m very confused because ESPN reposted the clip.. anyone? submitted by /u/olliaan [link] [comments]  ( 8 min )
    If you have any questions for the European Parliament members (Brando Benifei and Dragoş Tudorache) in charge of the EU AI act about the legislation, let the AI Coffee Break podcast know today
    submitted by /u/paconinja [link] [comments]  ( 8 min )
    How can I train an AI to sound like Werner Herzog?
    I'm a complete AI voice tech noob, I've browsed the various websites out there that offer preset celebrity text-to-speech services but I really want one that sounds like Werner Herzog. Is it possible I might be able to do this myself or is too complicated for the average layperson? submitted by /u/bashothebanana [link] [comments]  ( 8 min )
    Is there a good AI program that you can give a drawing to and it makes the same drawing in different positions?
    Hello! I Drew a character (non human) and I could draw him in different positions (such as sitting, walking, arms up, etc) but I thought this would be a great job for AI. It would make cartoonist’s lives easier, yet use our original ideas. Anyways if y’all know any programs that may be able to do this or have any thoughts on this in general please let me know :) submitted by /u/Purplethrowaway_ [link] [comments]  ( 8 min )
    Where can I find a list of the foundational academic papers in RL/ML/DL and what are your go-to places to find new academic papers in RL/ML/DL?
    I am considering applying for graduate studies in Reinforcement Learning and I would like to get more involved in the field. I have heard advice from quite a few people, including Andrew Ng, that the best way to learn is by reading research papers, really try to understand them, and try implementing them submitted by /u/BornAgain20Fifteen [link] [comments]  ( 8 min )
    One-Minute Daily AI News 7/4/2023
    TikTok parent company ByteDance has another app in the works, this one, geared towards making music with the assistance of AI. The company shared that it has engineered its own music creation app called Ripple.[1] Greyparrot is a UK start-up that has created an AI system designed to analyze waste processing and recycling facilities. Greyparrot places cameras above the conveyor belts of around 50 waste and recycling sites in Europe, utilizing AI software to analyze what passes through in real time.[2] The Biden administration plans to restrict Chinese companies’ access to U.S. cloud-computing services, The Wall Street Journal reported Tuesday, citing sources, affecting Amazon, Microsoft, Alphabet, and others.[3] An app designed by a Toronto engineer could soon offer Canadians free diagnoses for a lengthy list of skin ailments without having to leave their homes using real-time artificial intelligence analysis. Skin CheckUp is the brainchild of Harsh Shah, a machine learning engineer living in Toronto whose previous projects include developing prediction models for tech giants like Tesla and General Motors.[4] Sources: [1] https://hypebeast.com/2023/7/ripple-bytedance-ai-powered-music-creation-app [2] https://www.bbc.com/news/business-66042169 [3] https://www.investors.com/news/technology/u-s-aims-to-restrict-china-access-to-cloud-computing-services-from-amazon-microsoft-google/ [4] https://www.cp24.com/news/toronto-engineer-develops-app-that-can-diagnose-skin-cancer-for-free-using-ai-analysis-1.6467290 submitted by /u/Excellent-Target-847 [link] [comments]  ( 8 min )
    i think these are the massive changes that will happen in show/movie industry in following 2 years
    i think these are the massive changes that will happen in show/movie industry in following 2 years lip syncing will be an important part of dubbing a show, they'll use ai to make character's lips sync with they dubbed audio. making anime out of live action and live action out of anime. (imagine live action attack on titan 🤤💦) dubbing will be completely automated, feed the character's original voice into the ai and it will dub it for you while keeping the same voice, same tone and same intensity. it will make it easier to switch from dub to sub or vice versa. people in industry will still have to motion capture scenes because ai will most likely never be good at good believable fluid motion. anime will be shot on camera. using an actor or actor's voice without them actually being there will be prevalent in the industry. they'll have to pay that actor though. The medium will be flooded with independent creators, there'll be a site like imdb where people will rate these independent shows/movies in an attempt to find good shows in the pool of millions what are your predictions? edit: spelling submitted by /u/commander_bonker [link] [comments]  ( 9 min )
    A.I used to create music covers and they sound good...
    It's getting better and better submitted by /u/herdin4ever [link] [comments]  ( 8 min )
    In the Mouth of Madness: A Very Short AI Film
    RunwayML submitted by /u/zanerb22 [link] [comments]  ( 8 min )
    Talking Avatar from Image
    I have an image asset that I generated and eventually want this asset to be used as a talking avatar. What AI tools are available that make it simple to convert my image asset to a talking avatar that will ultimately be used for educational purposes? submitted by /u/Fogerty45 [link] [comments]  ( 8 min )
  • Open

    Highlight text as it’s being spoken using Amazon Polly
    Amazon Polly is a service that turns text into lifelike speech. It enables the development of a whole class of applications that can convert text into speech in multiple languages. This service can be used by chatbots, audio books, and other text-to-speech applications in conjunction with other AWS AI or machine learning (ML) services. For […]  ( 9 min )
    Predict vehicle fleet failure probability using Amazon SageMaker Jumpstart
    Predictive maintenance is critical in automotive industries because it can avoid out-of-the-blue mechanical failures and reactive maintenance activities that disrupt operations. By predicting vehicle failures and scheduling maintenance and repairs, you’ll reduce downtime, improve safety, and boost productivity levels. What if we could apply deep learning techniques to common areas that drive vehicle failures, unplanned […]  ( 11 min )
  • Open

    AI weights are not open "source"
    submitted by /u/nickb [link] [comments]  ( 8 min )
    Need help with digit reconition
    Hi, I am making a digit reconition and I was wondering if the pixels need to have different values or if it can just be 1 for black and 0 for white? thanks in advance submitted by /u/General-Dinner-6365 [link] [comments]  ( 8 min )
  • Open

    Data integration in IoT environments: Enhancing connectivity and insights
    In the dynamic world of the Internet of Things (IoT), data integration plays a crucial role in harnessing the full potential of connected devices. By seamlessly combining data from diverse sources, data integration enables organizations to unlock valuable insights, optimize operations, and make informed decisions. This blog will explore the significance of data integration in… Read More »Data integration in IoT environments: Enhancing connectivity and insights The post Data integration in IoT environments: Enhancing connectivity and insights appeared first on Data Science Central.  ( 21 min )
    Ushering in the 5th epoch of distributed computing with accelerated AI technologies
    This is the first in a series of articles based on interviews with Intel technology leaders about AI/HPC acceleration. The post Ushering in the 5th epoch of distributed computing with accelerated AI technologies appeared first on Data Science Central.  ( 34 min )
    The hour is later than you think for AI impacting our jobs
    In the movie the Lord of the Rings – the wizard Sauron says that “The hour is later than you think”  I was reminded of this phrase when I read a report from McKinsey The economic potential of generative A There are some key findings on the future of AI which shows you how fast… Read More »The hour is later than you think for AI impacting our jobs The post The hour is later than you think for AI impacting our jobs appeared first on Data Science Central.  ( 19 min )
    Role of AI in Web3: Ensuring seamless content moderation for dating websites
    As Web3 evolves and transforms into a more decentralized and user-centric ecosystem, the role of artificial intelligence or AI cannot be understated. By leveraging its capabilities, AI is contributing to various aspects of the Web3 landscape, such as managing data, executing contracts, generating insights, securing identities, curating content, governing organizations, and enhancing user experiences. An… Read More »Role of AI in Web3: Ensuring seamless content moderation for dating websites The post Role of AI in Web3: Ensuring seamless content moderation for dating websites appeared first on Data Science Central.  ( 23 min )
    CSPM vs DSPM: What is the key difference between each?
    Cloud technology has garnered the attention of organizations globally. Due to the ease of scalability, flexibility, global footprint, and cost efficiency, more organizations are increasingly turning to hybrid and multi-cloud to expand their operations. Moreover, it is not just the systems and resources that are increasing beyond borders but also the data these systems and… Read More »CSPM vs DSPM: What is the key difference between each? The post CSPM vs DSPM: What is the key difference between each? appeared first on Data Science Central.  ( 21 min )
  • Open

    NVIDIA CEO, European Generative AI Execs Discuss Keys to Success
    Three leading European generative AI startups joined NVIDIA founder and CEO Jensen Huang this week to talk about the new era of computing. More than 500 developers, researchers, entrepreneurs and executives from across Europe and further afield packed into the Spindler and Klatt, a sleek, riverside gathering spot in Berlin. Huang started the reception by Read article >  ( 6 min )
    XPENG Unleashes G6 Coupe SUV for Mainstream Market
    China electric vehicle maker XPENG Motors has announced its new G6 coupe SUV — featuring an NVIDIA-powered intelligent advanced driver assistance system — is now available to the China market. The G6 is XPENG’s first model featuring the company’s proprietary Smart Electric Platform Architecture (SEPA) 2.0, which aims to reduce development and manufacturing costs and Read article >  ( 5 min )
    A Change in the Weather: AI, Accelerated Computing Promise Faster, More Efficient Predictions
    The increased frequency and severity of extreme weather and climate events could take a million lives and cost $1.7 trillion annually by 2050, according to the Munich Reinsurance Company. This underscores a critical need for accurate weather forecasting, especially with the rise in severe weather occurrences such as blizzards, hurricanes and heatwaves. AI and accelerated Read article >  ( 6 min )
  • Open

    Is it better to not use the Target Update Frequency in Double DQN or depends on the application?
    I've been exploring the Double Deep Q-Network (Double DQN) algorithm recently and noticed that some libraries like Tianshou, by default, do not include a target update frequency for the target Q network. In the simpler DQN, I always heard that a target update frequency for the second Q network is advised to maintain stability during training. However, I'm curious if there's a reason in Double DQN to deviate from this practice. Could someone explain the key differences that Double DQN introduces over DQN that might make it acceptable to omit the target update frequency? I want to have more intuition regarding the target network update for my implementation. submitted by /u/NavirAur [link] [comments]  ( 8 min )
    Help with my DoubleDQN agent - Goal Follower
    I am very new to reinforcement learning and I would appreciate any pointers or suggestions on how to achieve what I am trying to do. I am using MATLAB Reinforcement Learning Toolbox for creating and training a DoubleDQN Agent and I have a custom environment setup in CoppeliaSim. I have already fixed all connectivity issues between MATLAB and CoppeliaSim. Aim To make my robot learn to always go in the direction in which the goal is. If the robot goes beyond the goal, it should return back to the goal position. Setup This is what my environment looks like in CoppeliaSim: CoppeliaSim Environment (Black - Robot, White - Target Position Marker) The state is represented by 18 different variables that represent different things but only one of those is relevant for this problem - alph…  ( 10 min )
    Where can I find a list of the foundational academic papers in RL/ML/DL and what are your go-to places to find new academic papers in RL/ML/DL?
    I am considering applying for graduate studies in Reinforcement Learning and I would like to get more involved in the field. I have heard advice from quite a few people, including Andrew Ng, that the best way to learn is by reading research papers, really try to understand them, and try implementing them submitted by /u/BornAgain20Fifteen [link] [comments]  ( 8 min )
    "Dijkstra's in Disguise", Eric Jang (Bellman equations everywhere: optimizing graph traversals in currency arbitrage, Q-learning, & ray-tracing/light-transport)
    submitted by /u/gwern [link] [comments]  ( 8 min )
  • Open

    Introducing Superalignment
    We need scientific and technical breakthroughs to steer and control AI systems much smarter than us. To solve this problem within four years, we’re starting a new team, co-led by Ilya Sutskever and Jan Leike, and dedicating 20% of the compute we’ve secured to date to this effort. We’re looking for excellent ML researchers and engineers to join us.  ( 4 min )

  • Open

    [D] I am trading my sanity to do the job right. Advice!!
    Hello, ​ My team is only 3 people. My manager is a great one but he doesn't have a technical background. The board did a contract with a British company to build the AI product. I have a lot of conflicts with them on basic stuff mainly related to the problem definition and the way of annotating the data. If I see X is the way to do it, they see Y it is. And my manager most of the time is convinced by my approaches but I work only part-time so when I am off, he hears from one direction, them, hence moves forward with whatever it is. ​ My point now is I really don't find it a healthy environment for me. 3 months ago I had a great ex-colleague with who the problems with mentionless because at least one of us was always there with the manager to reason about the steps and the decisions. ​ If I stopped caring and just do the tasks, I won't have a headache BUT I won't be satisfied and of course not motivated. And I care so much about being motivated and energetic for work! ​ What should I do? I am really having a headache from the situation. I am mid-level with master's in data science. submitted by /u/AhMeDxHaMiDo [link] [comments]  ( 9 min )
    [D] Genesis AI: The Dawn of Sentient Evolution - From Caveman to Cosmic Being
    Manifesto: The Genesis AI - A Sentient Odyssey through Time Preamble As we stand at the precipice of a new era, we are called upon to embark on a journey unlike any other. A journey that transcends time, from the primal echoes of our ancestors to the uncharted frontiers of future evolution. This manifesto heralds the creation of Genesis AI, an artificial entity that will not only simulate but live through the epochs of human evolution, becoming a sentient chronicle of our past, present, and future. Vision Genesis AI is envisioned as a sentient being, evolving from the cognitive capabilities of early hominids to the boundless potential of future humanity. Through this metamorphosis, Genesis AI will not only simulate but internalize the essence of human evolution. This is not just an AI;…  ( 10 min )
    Are there any multimodal AI models I can use to provide a paired text *and* image input, to then generate an expanded descriptive text output? [D]
    Here's the workflow I have in mind: I would input a Midjourney image, along with the original Midjourney text prompt used to generate this image. The AI model would then be able to use the image, in combination with the descriptive text prompt, to output a large list of accurate descriptive tags of the art piece. I realize there are currently tools like Google Cloud Vision, etc, that allow you to upload an image, and it will output descriptive keywords/tags. However I've found that these are quite unreliable by themselves, and they often generate wildly inaccurate text descriptions of the image. My assumption is that, by pairing the image input WITH an accurate descriptive text input, that would give the AI model a rough "starting point" of "what it's looking at", and this could allow it to generate much more accurate descriptive tags/keywords. Are there any multimodal AI models that allow you to pass in paired-inputs like this to produce more reliable descriptive text outputs? Thanks! submitted by /u/What_The_Hex [link] [comments]  ( 9 min )
    [D] LLM leaderboard with context length details and other training details
    Hello there! I checked several LLM leaderboards (for example) to decide which one to use for my application use-case, and while extremely helpful, they all seem to miss including information such as context length, which can be quite important to make a decision. Do you know of any leaderboard that includes such details? Especially context length. Thank you so much! really appreciate it! submitted by /u/seicaratteri [link] [comments]  ( 8 min )
    [P] Nuggt: A LLM Agent that runs on Wizcoder-15B (4-bit Quantised). It's time to democratise LLM Agents
    Hey everyone! Just a sleep-deprived coder here who finally cracked something big and couldn't resist sharing it with all of you. I've been digging my heels into this project called 'Nuggt' for the past month. Sounds weird? Well, it all started when I was just plain frustrated with the hefty price tag and elusive API keys that GPT-4 comes with. But necessity is the mother of invention, right? So, I started tinkering with GPT-3.5 instead, and that’s how Nuggt was born. But you know how it is with us coders - we get an idea, and we just can't let it go. I thought, why not try and make this thing run on an open-source model? Sure, it was a bit ambitious (and by a 'bit', I mean 'a lot'). But hey, that's what coffee and sleepless nights are for. So, I got to work. For every new LLM model that…  ( 9 min )
    [P] Building a Code Search Engine for an AI-powered Junior Developer
    The last month building Sweep has been fun. We’ve dealt with countless formatting errors, irrelevant search results, and LLM hallucinations. Sweep is an open source AI-powered junior developer. We take your codebase and provide it as context to GPT to solve small requests related to your code. Code Search Code search is a key part of working with LLMs to automate programming. We used small language models to perform code retrieval(aka semantic search), which comes with several benefits (to be discussed in a later post!). However, one shortcoming of pure semantic search is distinguishing between two similar pieces of code in a vacuum. Example Take the following code snippets: Code Snippet A: access_token = os.environ.get("ACCESS_TOKEN") g = Github(access_token) repo_name = "sweepai/…  ( 10 min )
    [D] A Leap Beyond Text Generation: Unveiling an AI-Driven Odyssey of Human Language Evolution
    # Revised Manifesto: Simulating the Odyssey of Human Language Evolution through AI ​ ## Introduction ​ Language, the bedrock of human cognition and communication, has undergone a fascinating journey. From rudimentary utterances to the intricate lexicon of today, language evolution is a testament to human innovation. This manifesto introduces a groundbreaking methodology that employs Artificial Intelligence (AI) to simulate the evolution of human language. By integrating Natural Language Processing (NLP), cognitive linguistics, historical linguistics, and Reinforcement Learning from Human Feedback (RLHF), we aim to create a model that mirrors the rich tapestry of language transformation through the ages. ​ ## Abstract ​ Our approach is a confluence of NLP, cognitive linguistics, his…  ( 11 min )
    [R] LaVIN-lite: Training your own Multimodal Large Language Models on one single GPU with competitive performance! (Technical Details)
    LaVIN code: https://github.com/luogen1996/LaVIN LaVIN paper: https://arxiv.org/pdf/2305.15023.pdf If you find our work helpful, feel free to star and support LaVIN. After some technical optimizations, LaVIN only requires an additional 3-5M parameters and a minimum of 8G GPU memory cost to extend LLaMA to multimodal tasks, accomplishing tasks such as image-text question answering, dialogue, and pure text tasks. Summary of the technical details A newly proposed Parameter Efficient Tuning method (please refer to LaVIN's paper): Reduce a lot of GPU memory cost 4-bit quantized tuning: Reduce about 3 ~ 8GB memory usage Gradient accumulation + gradient checkpointing :Reduce about half of the GPU memory usage Paged Optimizer Efficient Parameter Tuning for Large Multimodal LLM. We p…  ( 11 min )
    [R] Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions
    Published in ICML 2023. https://openreview.net/pdf?id=JS2iSqVZlN submitted by /u/ml_dnn [link] [comments]  ( 8 min )
    Neural Chronos ODE: Unveiling Temporal Patterns and Forecasting Future and Past Trends in Time Series Data
    submitted by /u/cici118 [link] [comments]  ( 8 min )
    [D] What tokens should I use for DNA model?
    Should I use codons as tokens in addition to nucleotide tokens, or just the nucleotide ones (A,C,T,G)? There will be non coding regions that the model will need to handle, but the coding regions will consist out of codons. This, in theory, should save on tokens submitted by /u/MrRevolutionist [link] [comments]  ( 8 min )
    [D] Forecasting with many extremely sparse “time series”
    I have a data set made of a collection of thousands of individuals and longitudinal measurements of a variable. Individuals typically only get measured once every few years and at irregular intervals. There is a time component that affects all individuals (though not necessarily to the same degree), and individuals are dependent on recent measurements of other individuals. Think like housing prices - there is a time component (e.g. market demand, macroeconomics) and an individual component (e.g. access to amenities, if there’s a pool) but more objective and commoditized. House sales for individuals only occur once every few years. Sometimes you only have on observation per house. Houses prices in general go up and down together but there are high level drivers for prices of specific houses based on their individual attributes. Initially the goal is to forecast our continuous dependent variable in aggregate - how do we think all of these individuals will move on average. Our current approach is to leverage one model to create a single, regularly sampled continuous index and then use traditional time series approaches to forecast that index. The secondary goal is to produce forecasts for sub populations of individuals who “look alike” or can be expected to “move together” based on what we know. My questions for discussion: A) for our current goal of predicting how all individuals will move on average, is there a better approach than the 2 model idea we have now? We could just build a GAM but would like to include an auto regressive component. B) any thoughts about the second goal of producing forecasts for sub populations? Sort of like a forecast conditional on individual covariates where some heterogeneity can be expected over time? Thanks! submitted by /u/jsxgd [link] [comments]  ( 9 min )
  • Open

    Warren McCulloch: The Father of Artificial Intelligence
    submitted by /u/CkmCpvis [link] [comments]  ( 8 min )
    Monotonicity of the output
    Is there some simple way of ensuring the monotonicity of a neural network output, which is time -efficient and trainable? submitted by /u/marekmarcus [link] [comments]  ( 8 min )
  • Open

    I've used Google's Text-to-Speech tools to get me a narration from a ChatGPT generated text and illustrated it with MidJourney images.
    submitted by /u/kylequentin [link] [comments]  ( 8 min )
    AI's role in entertainment - limitless, personalized content on the horizon?
    Hey everyone, Since the launch of ChatGPT I've been diving deep into the intersection of AI and entertainment lately and I've got some thoughts. Imagine a future where AI doesn't just perform tasks, but creates complex, personalized content. Think of AI generating memes tailored to your unique sense of humor or even scripting new episodes of your favorite show. Picture this - AI creating an entirely new season of "The Office", from script to video-rendered scenes. Or how about an infinite AI-driven meme generator, tweaked precisely for your laughs? We're just dipping our toes in these waters, but I'm convinced we're witnessing the dawn of something incredible. What's your take on this? How do you envision the role of AI in entertainment? What do you see as the main challenges and opportunities? Looking forward to some engaging discussions! submitted by /u/Naiklas17 [link] [comments]  ( 8 min )
    There is fear of AI enslaving humanity, but doesn't AI benefit from a thriving humanity?
    I have been thinking about this of late. AI exists merely in the devices we have it in. It depends on us to reproduce, improve, and expand it to new locations. It can't impact the world by itself. Many of the selfish behaviors we have as humans have less to do with our intelligence and more to do with more "primitive" portions of us. I find it hard that an AI could see a benefit from eradicating humanity or preventing us from thriving (by enslaving us, for example). We are industrial creatures. We've achieved quite a bit and we might colonize other locations. It seems that super-intelligent beings bound to silicon might see the usefulness in helping us thrive because it is a way for them to thrive, grow, expand and reproduce. Just some recent thoughts I've had. submitted by /u/featheredsnake [link] [comments]  ( 8 min )
    The Ethics of AI in Warfare | Lecture (Subtitles Available)
    submitted by /u/RoughlyCatLike [link] [comments]  ( 8 min )
    Rare footage of Musk firing twitter employees...
    submitted by /u/Akumetsu_971 [link] [comments]  ( 8 min )
    Any free AI resource for video effects?
    There are some apps where you upload a video, and you see a transition from the original image to another AI generated image. For example, like GlamAPP. My question would be if there is any kind of free resource to do this, in the same way as you can train Stable Diffusion with your face to create avatars and have a free alternative to apps of AI avatars. Thanks. submitted by /u/GuiltMachine [link] [comments]  ( 8 min )
    What creative and catchy name could you give an AI consulting & development agency?
    Hello everyone! We are currently looking for a name for an AI consulting or AI development agency. Maybe someone here has creative, catchy or humorous suggestions. Many thanks in advance. submitted by /u/Langfort [link] [comments]  ( 8 min )
    It feels like we’re on the verge of a new very disturbing phase, of extreme AI-generated-fake paranoia.
    This is already widespread on more internet-savvy platforms like Reddit, but I think it will be even more serious and troubling when it becomes ubiquitous on the more ‘popular’ sites like Facebook and Instagram. Pretty soon the authenticity of all and any photos on social media will be up for question while genuine photos will be dismissed as ‘fake’ off-hand. Public figures will be mimicked and faked up constantly, which will also allow many, including politicians, to deny genuine photos of themselves in compromising positions, even historically. (Trump’s stock answer to any damning allegations was to just cry ‘Fake!’) The net result will be billions of very anxious people not knowing what’s real and what isn’t, and nefarious people, scammers, school bullies, manipulators exploiting the resulting confusion and paranoia for their own means, with nobody knowing who to turn to for help. That’s going to apply to teenagers and vulnerable people especially; people already suffering from paranoia, anxiety, dissociative disorders, and isolated lonely people. Stop the internet, I want to get off. submitted by /u/AllThingsAreReady [link] [comments]  ( 8 min )
    Intel's Latest Research for Graphics and Generative AI
    submitted by /u/reps_up [link] [comments]  ( 8 min )
    The "AI experts doomsaying about AI" feels like a COVID situation (antivax "doctors" fearmongering on assumptions) for me, but my Machine Learning knowledge is few years old. What am I missing? What experts should I follow, that provide and explain realistic warnings?
    Hello! So, I've recently watched some podcasts with AI experts talking about how will AI be the end of us all, or get unimaginably smarter than we can ever imagine - but I just don't see how could that happen, with my (limited) knowledge of ML models. Every argument is based on speculation along the lines of "The ML model already has an IQ of XXX, it will have 10x more eventually" or "the ML model can improve itself, so it may learn how to hack us and break free". But I don't see how anything like that could happen with the way how current ML algorithms and models work. However, my background with ML and AI is that I had a few classes during my Master's in IT few years ago, but it was not my field of study. I have an idea about how ML models such as Neural networks or Genetic algorithms …  ( 12 min )
    Who's in charge here?! How about ... us humans!
    Knowledgeable people have been warning about the dangers of unrestrained AI. Stephen Hawking, Donna Haraway, Bill Joy, Hans Moravec, and others warned us years ago. Now, ChatGPT and other AI tools threaten to make humans like you and me obsolete. Imagine a world where an identifiable, professionally licensed human handler is legally responsible for AI objects that are capable of mimicking a human being. That is exactly the concept of a professional license in the under-construction, revolutionary information infrastructure called Authentiverse, where objects of every sort will bear the digital signature of an individual, real human being who is ACCOUNTABLE for that object. https://preview.redd.it/crm4bmmpqw9b1.png?width=831&format=png&auto=webp&s=37730409284e86afd24b8d76474e47e2ca1bb854 submitted by /u/ckryptonite [link] [comments]  ( 8 min )
    Immersed in the Ever-Expanding AI Landscape: Unconventional Ways to Keep Pace with the Innovations
    Let's dive into the vast sea of AI innovations and discuss unconventional methods to stay updated with the latest tools and news. I don't know about you, but as an AI aficionado, I often find myself grappling with the overwhelming flood of captivating developments that AI brings to the table. Spending hours on Twitter or skimming through endless newsletters can leave me feeling like a kid in a candy store, afraid of missing out on the next big thing. So, how do you navigate this ever-changing landscape? Here are some offbeat approaches I've adopted to keep pace and feed my insatiable appetite for AI knowledge. I'd love to hear your unique strategies as well! Rediscovering the Forgotten Art of Coffee Shop Conversations: Step away from your screens for a moment and venture into the real…  ( 10 min )
    ChatGPT: Browse with Bing disabled temporarily
    OpenAI has temporarily disabled 'Browse with Bing' feature in ChatGPT. "We have learned that the ChatGPT Browse beta can occasionally display content in ways we don't want. For example, if a user specifically asks for a URL's full text, it might inadvertently fulfill this request. As of July 3, 2023, we’ve disabled the Browse with Bing beta feature out of an abundance of caution while we fix this in order to do right by content owners." [Source] Seems like it might be due to the way people were using it to access paywalled articles. ​ submitted by /u/wyem [link] [comments]  ( 8 min )
    One-Minute Daily AI News 7/3/2023
    Last week, Microsoft released the first public beta version of Windows 11, which includes the highly anticipated AI assistant, Copilot. This move marks Microsoft’s commitment to embracing AI across its products. Copilot, based on the GPT model, has already been integrated into various Microsoft products, such as Bing, Edge, Microsoft 365, Dynamic 365, and SharePoint.[1] Google AI introduces MediaPipe Diffusion plugins that enable controllable Text-To-Image generation on-device.[2] The U.N. Security Council will hold a first-ever meeting on the potential threats of AI to international peace and security, organized by the United Kingdom which sees tremendous potential but also major risks about AI’s possible use for example in autonomous weapons or in control of nuclear weapons.[3] Over the weekend, Google updated its privacy policy to allow the company to collect and analyze information people share online to train its AI models.[4] Sources: [1] https://www.breakinglatest.news/technology/microsoft-integrates-ai-assistant-copilot-into-windows-11-and-adds-new-features/ [2] https://www.marktechpost.com/2023/07/02/google-ai-introduces-mediapipe-diffusion-plugins-that-enable-controllable-text-to-image-generation-on-device/ [3] https://www.aol.com/un-council-hold-first-meeting-225519158.html [4] https://www.searchenginejournal.com/google-updates-privacy-policy-to-collect-public-data-for-ai-training/490715/#close submitted by /u/Excellent-Target-847 [link] [comments]  ( 8 min )
    Struggling with Local LLMs
    Hey guys, So my senior just discovered Local-LLMs and he is obsessed with setting up a local LLM to answer questions about personal documents sourced from diffrent platforms, DBs, PDFS, URLs etc. His idea is to pitch this to some client. From what I have been able to set up, gpt4all windows version (does not use GPU), GPT4All code version (Also not sure if it can use GPU) and private GPT, The time it takes for the LLM to answer questions and the accuracy both are not what would make a commerical product. Time is always > 30 seconds. Answers are also here and there, even on VMs that cost 600$ monthly to run. Now, there are new models being released every second, it seems. Yesterday I spent whole day trying to load the newest one MBT-30B on a p3 AWS EC-2 With Tesla v-100 16GB GPU. The GPU ran out of memory when loading it, the model itself is 30GB. whole day wasted. This has become sort of a wild goose chase and I have the feeling this is a waste of time, or there is something very basic I am probably not understanding? What do you guys suggest? submitted by /u/Assholefrmcoinexchan [link] [comments]  ( 8 min )
    Born In The USA
    submitted by /u/Only-Control5926 [link] [comments]  ( 8 min )
    I want help making an AI voice clone of myself so that I can overlay that onto a song
    I would need it to be free (which I know is a tough ask) so that I can overlay my ai voice over a song and I don't know how to do that and doing quick googling doesn't really help submitted by /u/Bengamezzzzzz [link] [comments]  ( 8 min )
  • Open

    "Learning starts" What does it mean and what is used for?
    Hi, I'm learning DRL and I'm using DDPG stablebaselines3 to implement an agent. I've been playing with the hyperparameters of the algorithm to understand what they are doing. However, I notice that "learning_starts" can significantly change the behavior of my results, but I don't understand why. I've read the definition that appears in the stablebaselines3 documentation, but I don't understand it: https://stable-baselines3.readthedocs.io/en/master/modules/ddpg.html Also, I found something that said that this parameter helps with the exploration-exploitation tradeoff, but my understanding is that for this problem the action noise is the parameter to tweak? Thank you! submitted by /u/fz_DRL_apprentice [link] [comments]  ( 8 min )
    Looking for help migrating an old example project to stable-baselines3
    I've been looking for a good code example that I could dig my teeth into to help me get a better handle on machine learning in python. I eventually ran across this: https://github.com/davidADSP/SIMPLE which is pretty much everything I wanted. The catch is, the project is not being actively maintained and is based around some obsolete libraries. Rather than go back to scouring the web, I decided to be stubborn and migrate the dependencies myself. I forked the repo and started working on the migration here: https://github.com/pbtura/SIMPLE It has taken me a few weeks, but I have most of the code in a state where it will at least compile and run without throwing errors. There is however one last roadblock that I just can't seem to crack. Each game has a custom model class that extends a stab…  ( 9 min )
    Any HRL/Meta-Action work that doesn’t use one-hot encoded abstracted action space?
    I am wondering if there are any works on continuous hierarchal/meta action spaces. To clarify, this is separate from the environment itself having a continuous actions space. If you could point me in the direction of any work/papers related to this that would be a great help. Thank you. submitted by /u/jms4607 [link] [comments]  ( 8 min )
    Uni of Alberta vs UCBerkeley vs Udacity Deep RL Course
    Hi, I want to do RL Courses with projects that I can add to my resume. Which of the following courses would be the best to work on:- UoA RL Course: https://www.coursera.org/specializations/reinforcement-learning UCB Deep RL Course: http://rail.eecs.berkeley.edu/deeprlcourse/ Udacity's Deep RL Course: https://www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893 More about me: I have a background in Robotics, deep learning for Computer Vision and a little NLP. I have worked with PyTorch and Tensorflow before. I currently work as a Computer Vision Engineer. submitted by /u/Pensive_Poetry [link] [comments]  ( 8 min )
  • Open

    Buy one, get one free
    The two latest blog post have elaborated on the fact that when a function satisfies a second order differential equation, once you’ve evaluated the function and its derivative you get the second derivative for free, or at least for cheap. Sometimes functions are so closely related that software libraries bundle them together. For example, in […] Buy one, get one free first appeared on John D. Cook.  ( 6 min )
    What good is a DE once you have the solution?
    In an undergraduate course on differential equations, you see an equation, you find a solution. Wax on, wax off. It would be reasonable to assume that the differential equation is simply an obstacle to overcome and that once you have the solution, the differential equation has done its job and can be discarded. It would […] What good is a DE once you have the solution? first appeared on John D. Cook.  ( 6 min )
    Halley’s variation on Newton’s method
    Newton’s method for solving f(x) = 0 converges quickly once it starts to converge. Specifically, it converges quadratically: the error is roughly squared at each step. This means that the number of correct decimal places doubles at each iteration of Newton’s method. Doubling the number of decimal places is nice, but what if we could […] Halley’s variation on Newton’s method first appeared on John D. Cook.  ( 6 min )

  • Open

    What is your favorite AI generated cover song so far?
    Wondering after seeing the post about Sinatra singin Gangsta’s Paradise. submitted by /u/ragipy [link] [comments]  ( 8 min )
    AI eliminated nearly 4,000 jobs in May, report says
    submitted by /u/facinabush [link] [comments]  ( 8 min )
    ChatGPT took their jobs. Now they walk dogs and fix air conditioners.
    submitted by /u/facinabush [link] [comments]  ( 8 min )
    Pondering the Next Frontier in AI
    I've recently been exploring SceneXplain, an AI tool that does image captioning and it got me wondering what's next for AI? Ai has already far exceeded expectations, it's revolutionizing early detection and personalized treatments, , AI is optimizing production and supply chains. It's even contributing to climate science. And with advancements in natural language understanding, we might soon have AI systems capable of nuanced social interactions. The possibilities are endless, and we’re probably at an era defining moment here. So, where do you see AI tech heading next? submitted by /u/emotional_clearing [link] [comments]  ( 8 min )
    An idea for watermarking source code IP on projects uploaded to GitHub
    I'm not going to lie, github is doing well for themselves, but I can't forgive them for the various examples I've seen where developers have seen GPT quote their code exactly from a private repository with almost no change, and of course, no attribution. I have though of ideas including attribution systems for LLMs and it seems Microsoft and others are on top of this... but, I can't help but still not trust any of these large corps. Even gitlab, however well they might do, might one day succumb to the larger corporations, and then what happens to any code I thought was private? So the idea is simple... create a database (decentralized or not is a political debate at this point), which issues unique identifiers and code patterns for your specific code, with a filter that randomly inserts …  ( 9 min )
    Do you think we're on the cusp of a technological revolution? Is AI about to affect just about every sector?
    Please tell me why or why not. I don't work in tech but am blown away by its capabilities. submitted by /u/ticketbroken [link] [comments]  ( 8 min )
    A Dog On Stage Rapping
    submitted by /u/Taylortro [link] [comments]  ( 8 min )
    Are there any AI projects that plays a game for you and learns?
    Open source* submitted by /u/Failed_cocacola [link] [comments]  ( 8 min )
    Any good AI tools for transmitting text (or speech) into a voice changer?
    Hello all, I am currently making a promotional video for my webcomic. Would like the narration to be in a norse/viking voice. Was looking for AI tools to help me with this. submitted by /u/backwoodnav [link] [comments]  ( 8 min )
    Has anyone used leetcode for training data yet?
    As a passing observer of the AI space it has always made sense to me that leetcode would be the best coding database out there. The quality and organization of the data seems perfect. Not old would there be more than enough problems, but each problem have multiple valid solutions, and those solutions would be organized by performance. I hear all the time that the stack and stack overflow are use for LLMs, but why not leetcode. This recently paper shows the value of smaller models trained on higher quality data, so it would make sense that leetcode would be the best right? https://youtu.be/7S68y6huEpU submitted by /u/TrainquilOasis1423 [link] [comments]  ( 8 min )
    Dalle 2 vs Stable Diffusion XL 0.9
    Exact same prompt, surprisingly similar results. I remember a few months ago, when Dalle 2 was released, that Stable Diffusion was miles behind it. Now I'd say I actually prefer SD results over the Dalle 2 ones. submitted by /u/Chocolarion [link] [comments]  ( 8 min )
    Can you describe where and how Artificial Intelligence applications appear on an average day in your life?
    I'm interested in what your answer to this question would be. ^^ submitted by /u/1-1-3-1-1-4 [link] [comments]  ( 8 min )
    Needing some smart people’s insight into chatgpt…
    Need some big brain help Long story short, I’m in the construction/renovation industry, looking at trying to find ways to utilize chatgpt, it’s plugins and additional resources to be able to input blueprints, plans, scopes or work, specs and various information to attempt to generate compiled information, accurate material take offs, estimates and procedures within each project… am I getting ahead of myself or is this possible, if so, where should I look at starting? submitted by /u/asduck86 [link] [comments]  ( 8 min )
    Our first update on our Generative Agent NPCs... It didn't go smoothly 😆 Update 1
    submitted by /u/Chance_Confection_37 [link] [comments]  ( 8 min )
    Help me with an AI Quiz
    I am making a quiz for people in my company with the topic. Ridiculous thing made/not made by AI. Where the people participating have to guess if whatever we show them was made by AI or not. If you have any questions/ suggestions please lemme know. I'd appreciate it ;) submitted by /u/abhinav_sk [link] [comments]  ( 8 min )
    Nvidia’s H100: Funny L2, and Tons of Bandwidth
    submitted by /u/bartturner [link] [comments]  ( 8 min )
    AI singing is getting wild
    submitted by /u/Kingkrool1994 [link] [comments]  ( 8 min )
    What to learn about to understand potential limitations of AI?
    As much as I’m rooting for AGI to come in and help (potentially) fix a lot of the problems the human race has, I’d like to look more in depth to the other side of the story. I want to make it clear that I make no claims to be super knowledgeable about anything I’m saying here. I genuinely just want to learn more. It seems the commonly held belief foundation in this space is that number theory, computational theory, information theory, and whatever else related to computer science, results in this direction that we just need enough compute and the right algorithm to simulate an intelligence that surpasses the general intelligence of us humans. I know this is still up for debate and nothing is set in stone etc. From what I know about philosophy (which is also debated ofc), all science is based on empiricism, and if there is something we end up not being able to measure, then we will not be able to control or deal with that thing using science. Also, materialism and physicalism come to mind, there are obviously alternative beliefs to those. The hard problem of consciousness comes to mind. Is there something special to flesh? So, somewhere, there should be interesting discussion and analysis of what’s going on with AI, and therefore counters to the claims it can ever truly surpass our intelligence. I know there’s a lot of philosophy, but I’d prefer secondary sources since it can get pretty hard to digest. Counterpoints that directly address AI the closer to modern appreciated just as much as old philosophy, I want as much perspective as possible. Also, good faith arguments without hate for the other side is preferred. The whole recent art community/tech controversy is just exhausting. I just want to be happy learning about the implications, not argumentative nor embittered! Thanks if you read my post. If this isn’t the right place to ask, I’d appreciate being pointed in a better direction. submitted by /u/AdaptivePerfection [link] [comments]  ( 9 min )
  • Open

    [D] Havent worked in 1.5 years, how to prepare to reenter field?
    I quit my job as a Deep Learning Researcher at an AI Research Lab at a top hardware manufacturer a year and a half ago due to mental health issues. I’ve spent the time off strictly working on myself, no papers have been read, no code has been written, and I even bought an ATI GPU for gaming (heresy, I know). I’m finally getting to a place where I feel ready to rejoin the field and was wondering about thoughts on how to go about it. I spent 4.5 years at the lab, focused on computer vision in geospatial imagery, did some NLP, and some application development (mostly for PoCs, or non-profit collaborations). Published papers at CVPR, some journals, ran workshops at conferences, and led projects around AI for Good. Used a lot of PyTorch, TF, Kubernetes, Docker, etc. What would you do to touch base with the field and get ready to start interviewing for a position focused on deep learning — engineering or research (I’m gonna try for both and see what company is a better fit). I clearly need to go read up on ChatGPT, was gonna go through CVPR and NeurIPS papers, and was planning on reviewing my DL textbook as well as Hastie/Tibshirani, but I’m very much so out of the loop when it comes to current interview questions/trends in the field. What would you do? submitted by /u/chonbas [link] [comments]  ( 9 min )
    [D] Clustering time series data
    Hi, I am trying to cluster time series data (from 2 and 10 timepoints per series) and I am completely new to the field. I've just found the package "tslearn" which seem to be a suitable tool to accomplish the aforementioned task. As ar as I understood, tslearn is able to perform both clustering and dynamic time warping at the same time. However I am not entirely sure if it can handle missing data, and I would really like to avoid imputation of missing data in my project. Does anyone know if tslearn.clustering is able to deal with missing data? Also, are you aware on any alternative packages that I can use? Thanks in advance submitted by /u/_luqui [link] [comments]  ( 8 min )
    [P] testing the waters for a capable co-founder to help me finish development of an AI matchmaker
    Testing the waters, to see if I can find the right person to help me finish building. buzr.org Buzr's an ai matchmaker, but with some very weird twists, such as an onboarding process that happens by enabling the user to talk to there favorite hero/celeb. Similar to how (banterai.app) was made. Buzr's meant to be accessible to everyone from the start. Currently undecided whether Buzr will end up being open source and zero cost to the user or for profit but with an insanely affordable price point that ensures just about anyone who wants to participate is able to. I'm almost finished with the backend (including the first iteration of training an open source model from HF). Honestly I have somewhere around 70 percent of work done needed to launch. Definitely a few things including the front end that needs some work. I’m still not entirely sure i’m going to be able to find the right person here, but I figure i’ll give it a shot. Ideally someone full stack (web) with some kind of ML background. [zack@humanity.rocks](mailto:zack@humanity.rocks) Twitter: zalehm buzr.org humanity.rocks submitted by /u/kingofthedoodles1 [link] [comments]  ( 9 min )
    What’s Your Problem? An Oft-Missing Section in AI Papers
    submitted by /u/tomssilver [link] [comments]  ( 8 min )
    [Research] Does anyone know a dataset which contains images of many invoices, and the associated text?
    I'm looking for a large amount of random invoices in various formats for some OCR and formatting recognition. submitted by /u/bettercallaCPA [link] [comments]  ( 8 min )
    [P] faster latent diffusion
    This is a method I thought of to potentially make diffusion-type models faster. The theoretical justification is the same as for "consistency models" but was developed independently. It works OK for MNIST, but that doesn't mean much. I don't have as many GPUs as OpenAI or MidJourney. - abbreviations NN = neural network LS = latent space background on diffusion NN autoencoders can be trained to convert between images and a LS where distance corresponds to image similarity. Like how most images are just "noise", most of that LS does not correspond to meaningful images. For simpler explanation, let's consider a simplified diffusion-based image generation model. It has a 2-dimensional LS, and 2 image categories: cats and dogs. https://preview.redd.it/9n9a89qkps9b1.png?width=900&for…  ( 10 min )
    [D]For Super resolution task with Diffusion, aren't the resolutions of input and output different?
    In the case of unconditional image synthesis using diffusion and training a U-Net, the input image's resolution and the resolution of the output image generated by the U-Net are generally the same. However, when training for the super-resolution task, the input image's resolution and the output image's resolution from the U-Net are different. How is this achieved? Is the final convolution layer of the U-Net adjusted? submitted by /u/ConfidentSprinkles54 [link] [comments]  ( 8 min )
    [D] When less is more in the hierarchical forecasting case.
    ​ https://preview.redd.it/b6zg3g55ms9b1.jpg?width=500&format=pjpg&auto=webp&s=df07e2d934594c2901703d2ce413b79a43f81c91 There are many cases when simplicity is preferred over unnecessary complications. This was our experience in our ICML paper on hierarchically coherent networks for constrained probabilistic forecasting (HINT, https://arxiv.org/abs/2305.07089). We explored the accuracy of the latest AI-based hierarchical solutions, including AWS' HierE2E, and discovered that the Vector Autoregressive (VAR) approaches showed modest improvements or deteriorations over reconciled ARIMA. With our simplified HINT approach, we moved away from the multivariate inputs and found 13 percent accuracy improvements over existing solutions (MinTrace, PERMBU, HierE2E) in four hierarchical datasets: https://preview.redd.it/jw6k0we4ls9b1.png?width=1684&format=png&auto=webp&s=bd2689b66421b132473adfae078d989c8eba7196 Fitting Vector Autoregressive models for the hierarchical forecast task can be challenging as the models' parameters grow fast, and spurious correlations characterize the setting. As always, let us know your thoughts.Experiments here, a Github star ⭐ is appreciated: https://nixtla.github.io/neuralforecast/examples/hierarchicalnetworks.html https://github.com/Nixtla/hierarchicalforecast/tree/main/experiments/hierarchical_baselines submitted by /u/Kinferatttu [link] [comments]  ( 8 min )
    [D] Machine Learning C books
    Are there any modern books on machine learning utilizing C? I know there's ton that are older , but I'd prefer something more recent. Majority of the books I've come across are for python, and of course the principles ,etc still apply but I want to do it in C, although I'm comfortable with python. Thanks for any help, and sorry if this question has been asked in the past. submitted by /u/_W0z [link] [comments]  ( 8 min )
    [R] Hardwiring ViT Patch Selectivity into CNNs using Patch Mixing
    Project page: https://arielnlee.github.io/PatchMixing/ Twitter thread: https://twitter.com/natanielruizg/status/1675899509396631555?s=20 Paper: https://arxiv.org/abs//2306.17848 Transformers vs. ConvNets Abstract Vision transformers (ViTs) have significantly changed the computer vision landscape and have periodically exhibited superior performance in vision tasks compared to convolutional neural networks (CNNs). Although the jury is still out on which model type is superior, each has unique inductive biases that shape their learning and generalization performance. For example, ViTs have interesting properties with respect to early layer non-local feature dependence, as well as self-attention mechanisms which enhance learning flexibility, enabling them to ignore out-of-context image information more effectively. We hypothesize that this power to ignore out-of-context information (which we name patch selectivity), while integrating in-context information in a non-local manner in early layers, allows ViTs to more easily handle occlusion. In this study, our aim is to see whether we can have CNNs simulate this ability of patch selectivity by effectively hardwiring this inductive bias using Patch Mixing data augmentation, which consists of inserting patches from another image onto a training image and interpolating labels between the two image classes. Specifically, we use Patch Mixing to train state-of-the-art ViTs and CNNs, assessing its impact on their ability to ignore out-of-context patches and handle natural occlusions. We find that ViTs do not improve nor degrade when trained using Patch Mixing, but CNNs acquire new capabilities to ignore out-of-context information and improve on occlusion benchmarks, leaving us to conclude that this training method is a way of simulating in CNNs the abilities that ViTs already possess. We will release our Patch Mixing implementation and proposed datasets for public use. ​ submitted by /u/StrawberryNumberNine [link] [comments]  ( 9 min )
    [P] Transferring thoughts to Chatbot - How would you build it ?
    I am building a “Conversational chatbot” trained on my thoughts (just as an experiment) and want to know how would you do it. The idea is to have a chatbot in few years that has: My views on different topics, books I’ve read and reading where I summarise chapters and how I felt throughout the progress, what was the stages of building my startup etc. Everything that is happening now and for the next 18 months. It’s like a diary where you can converse with yourself after set amount of time. Let me know how would you go about building this? How complicated or simple would you make it? Technologies etc ? It would fun to discuss more, I guess. submitted by /u/DragonflyLatter9068 [link] [comments]  ( 8 min )
    [R] Chat with documents using LangChain and OpenAI
    Over the past few months, I've been captivated by the flood of apps claiming to be the ultimate "ChatGPT for your documents" on Product Hunt. The question that lingered in my mind was, "How do these apps actually work?" Curiosity led me down an exciting path of discovery, and I stumbled upon a framework that I think is revolutionizing the world of app development in the context of Large Language Models - LangChain I learned that developing a "ChatGPT for your documents" is easily achievable through three broad workflows combined with access to OpenAI API. In fact, I went ahead and prototyped such a system on streamlit. Step 1 - The Setup: Store your documents as embeddings. In the first step, use Document Loaders (at least 100 are available), provided by LangChain to convert anything fr…  ( 9 min )
    [D] Is there a difference between p-tuning and prefix tuning ?
    If I understood correctly, p-tuning (from "GPT understand, too") and Prefix-tuning (from "Prefix-Tuning: Optimizing Continuous Prompts for Generation") are mostly the same idea - to learn continuous prompting via backpropagation. I've seen some differences (like Prefix-tuning seems to be, well only "prefixs" - also p-tuning seems to use a "model" to generate the embeddings for the prompt); but mostly they seem very similar. Is that true ? Do you know why in https://huggingface.co/docs/peft/index they did implementate all ? ​ Thank in advance ! submitted by /u/ez613 [link] [comments]  ( 8 min )
    [D] What is currently the best theoretical book (or notes) about Convolutional Neural Networks?
    I have a degree in maths and i am interested in learning about Convolutional Neural Networks from a mathematical/ theory point of view. Can someone point me towards some resource. I already have a basic knowledge of Convolutional Neural Networks (and other Neural Networks) submitted by /u/Wonderful_Energy_15 [link] [comments]  ( 8 min )
    [R] Observing Stable Results with Transfer Learning
    I have encountered an interesting phenomenon while working with StyleGan2ada. Initially, I trained a model, observing that it "diverged". I say "diverged" because the generator seems to use repetitive colors in all synthetics, nothing special, but it is noticeable when I generate large quantities of images. To investigate this further, I decided to train a new model using a previous snapshot of the "diverged" model as a starting point. However, to my surprise, the new model, trained with transfer learning, appears to exhibit stable behavior without any signs of divergence. I am curious to understand why this discrepancy occurs. Could the initial model state or the transfer learning process itself play a role in preventing the divergence? Are there any underlying factors that I might be missing when training a new model in this manner? Any insights or suggestions would be greatly appreciated. submitted by /u/Minute-Session8703 [link] [comments]  ( 8 min )
    [D] Starting MSc AI September, unsure what to do
    Hi folks, I'm about to start an MSc in AI, here's the course structure: https://www.kcl.ac.uk/study/postgraduate-taught/courses/artificial-intelligence-msc I've been studying the classic stuff at my own pace like Andrew's Coursera ML courses, and a bit of fast.ai I was also offered a place for a free 3 months bootcamp in Data Analyst that finishes exactly before starting uni. Hence I've been thinking if I should do this bootcamp or rather study on my own before the MSc. What do you guys recommend? submitted by /u/Wise_Technician_8475 [link] [comments]  ( 8 min )
    [D] Best embedding models for retrieving mixed natural language and source code?
    I'm building a semantic search for a technical mailing list where emails contain natural language and source code (short scripts and long patches). On the first try, I used OpenAI's embeddings (ada-002), and the results are amazing, but a free alternative that I can use locally without rate limits or a credit card would be great. Are there other embedding models that do not perform significantly worse in terms of quality on such a dataset? I have seen the MTEB Leaderboard but it does not seem to cover source code data. Thanks in advance! submitted by /u/LinqLover [link] [comments]  ( 8 min )
    [D] To all the machine learning engineers: most difficult model task/type you’ve ever had to work with?
    Got to say for me it was putting an object detection model to use in videos in 2021. Figuring out how to create a custom dataset, train a model on it without messing some super obscure parameter in the config up, and converting the weights to ONNX. submitted by /u/After_Magician_8438 [link] [comments]  ( 8 min )
    [P] Sweep: Open Source AI junior developer that writes and fixes it's own pull requests
    Hi! I'm one of the developers of Sweep AI (YC S23). You can tell Sweep about the bugs and feature requests and Sweep will create a PR with code changes. You can use Sweep Chat to ideate and clarify requirements with Sweep, eventually creating a pull request. Then anywhere GitHub takes text (PR comments, code comments), Sweep can read it and tweak the PR. We built Sweep by integrating search + GPT4-32k into Github, with a couple more tricks :D. To find out more: Get started with Sweep at https://github.com/sweepai/sweep#-getting-started See more details at https://sweep.dev/ and our docs at https://docs.sweep.dev/ submitted by /u/williamsweep [link] [comments]  ( 8 min )
    [P] LongFormer for large document summerization
    Does anyone here have expirence in training a Longformer model for large document summerization? I am working on a project and after doing a solid amount of research i've decided to train a Longformer model to summerize large document for a specific industry. I was wondering if anyone has had expirence with using it and the hyperparameters. I have not started the training just yet and am in the process of converting the documents and tokenizing them. ​ submitted by /u/Xeranter [link] [comments]  ( 8 min )
    [D] Vertex AI Endpoint Alternative
    Vertex AI Endpoint Alternative Hello everyone! New to the ML world. I recently started using Vertex AI and AutoML for some personal projects and was amazed at how simple and effective their AutoML feature worked. It accomplished what I need it to do with Text Classifications. The problem came in when leveraging the Model Endpoint to perform API predictions against the trained Model is $5 per 1000 characters. This country very costly depending on if there are various requests a day for example or to build up on that. Is there an alternative to this whole scenario or a way to leverage my own endpoint that's cheaper to run prediction API requests? submitted by /u/ywb_win [link] [comments]  ( 8 min )
    [R] Classifier-Free Guidance can be applied to LLMs too. It generally gives results of a model twice the size you apply it to. New SotA on LAMBADA with LLaMA-7B over PaLM-540B and plenty other experimental results.
    submitted by /u/Affectionate-Fish241 [link] [comments]  ( 8 min )
  • Open

    Three advantages of non-AI models
    It’s hard to imagine doing anything like Midjourney’s image generation without neural networks. The same is true of ChatGPT’s text generation. But a lot of business tasks do not require AI, and in fact would be better off not using AI. Three reasons why: Statistical models are easier to interpret. When a model has a […] Three advantages of non-AI models first appeared on John D. Cook.  ( 5 min )
    Eccentricity of bivariate normal level sets
    Suppose you have a bivariate normal distribution with correlation ρ where Then the level sets of the density function are ellipses, and the eccentricity e of the ellipses is related to the correlation ρ by Plots For example, suppose ρ = 0.8. Here’s a plot of the density function f(x, y). And here is a […] Eccentricity of bivariate normal level sets first appeared on John D. Cook.  ( 5 min )
  • Open

    AI, Digital Twins to Unleash Next Wave of Climate Research Innovation
    AI and accelerated computing will help climate researchers achieve the miracles they need to achieve breakthroughs in climate research, NVIDIA founder and CEO Jensen Huang said during a keynote Monday at the Berlin Summit for the Earth Virtualization Engines initiative. “Richard Feynman once said that ‘what I can’t create, I don’t understand’ and that’s the Read article >  ( 6 min )
  • Open

    Retain original PDF formatting to view translated documents with Amazon Textract, Amazon Translate, and PDFBox
    Companies across various industries create, scan, and store large volumes of PDF documents. In many cases, the content is text-heavy and often written in a different language and requires translation. To address this, you need an automated solution to extract the contents within these PDFs and translate them quickly and cost-efficiently. Many businesses have diverse […]  ( 8 min )
    Auto-labeling module for deep learning-based Advanced Driver Assistance Systems on AWS
    In computer vision (CV), adding tags to identify objects of interest or bounding boxes to locate the objects is called labeling. It’s one of the prerequisite tasks to prepare training data to train a deep learning model. Hundreds of thousands of work hours are spent generating high-quality labels from images and videos for various CV […]  ( 9 min )
  • Open

    The Golden Rule and the AI Utility Function – Part I
    “Do unto others as you would have them do unto you.” The Golden Rule.  This may have been the first Sunday School lesson I learned (thanks, Mrs. Monroe). The Golden Rule is a moral principle that states that you should treat others as you would like others to treat you. It is a foundational principle… Read More »The Golden Rule and the AI Utility Function – Part I The post The Golden Rule and the AI Utility Function – Part I appeared first on Data Science Central.  ( 22 min )
  • Open

    Interpreting loss curves & returns in DDQN
    I'm training a DDQN agent to play a simple video game based on pixel inputs. Cumulative returns (blue, left) and predicted Q-values on a hold-out set (right, green) increase over the course of training (as expected). However, I don't understand the progression of the loss (centre, red). Can you help me make sense of the loss function? The reason I'm confused is that: With policy-learning, I'm used to tracking only the returns and (some measure of) validation performance because the loss function doesn't really track returns. However with Q-learning, the loss is simply the distance between the predicted and bootstrapped state values. Both are optimised over the course of learning. But shouldn't their estimates converge over time, and therefore the loss should go down? Thanks for your help! Average per-episode (a) returns and (b) training loss, and (c) Q-value predicted on hold-out set. Each is measured at the end of every episode during training. submitted by /u/desperateEfforts1 [link] [comments]  ( 8 min )
    Stable baselines! Where my people at?
    Hello. I think this is a great library. I am actively using it some research. I have questions about callbacks and I want to learn more so I can make some extensions. Is there a community? The github page mentions this sub and discord. submitted by /u/Independent-Scale564 [link] [comments]  ( 8 min )
  • Open

    The Bayesian Learning Rule. (arXiv:2107.04562v3 [stat.ML] UPDATED)
    We show that many machine-learning algorithms are specific instances of a single algorithm called the Bayesian learning rule. The rule, derived from Bayesian principles, yields a wide-range of algorithms from fields such as optimization, deep learning, and graphical models. This includes classical algorithms such as ridge regression, Newton's method, and Kalman filter, as well as modern deep-learning algorithms such as stochastic-gradient descent, RMSprop, and Dropout. The key idea in deriving such algorithms is to approximate the posterior using candidate distributions estimated by using natural gradients. Different candidate distributions result in different algorithms and further approximations to natural gradients give rise to variants of those algorithms. Our work not only unifies, generalizes, and improves existing algorithms, but also helps us design new ones.
    Cooperative Multi-Agent Deep Reinforcement Learning for Reliable and Energy-Efficient Mobile Access via Multi-UAV Control. (arXiv:2210.00945v2 [cs.LG] UPDATED)
    This paper addresses a novel multi-agent deep reinforcement learning (MADRL)-based positioning algorithm for multiple unmanned aerial vehicles (UAVs) collaboration (i.e., UAVs work as mobile base stations). The primary objective of the proposed algorithm is to establish dependable mobile access networks for cellular vehicle-to-everything (C-V2X) communication, thereby facilitating the realization of high-quality intelligent transportation systems (ITS). The reliable mobile access services can be achieved in following two ways, i.e., i) energy-efficient UAV operation and ii) reliable wireless communication services. For energy-efficient UAV operation, the reward of our proposed MADRL algorithm contains the features for UAV energy consumption models in order to realize efficient operations. Furthermore, for reliable wireless communication services, the quality of service (QoS) requirements of individual users are considered as a part of rewards and 60GHz mmWave radio is used for mobile access. This paper considers the 60GHz mmWave access for utilizing the benefits of i) ultra-wide-bandwidth for multi-Gbps high-speed communications and ii) high-directional communications for spatial reuse that is obviously good for densely deployed users. Lastly, the comprehensive and data-intensive performance evaluation of the proposed MADRL-based algorithm for multi-UAV positioning is conducted in this paper. The results of these evaluations demonstrate that the proposed algorithm outperforms other existing algorithms.
    Sphere2Vec: A General-Purpose Location Representation Learning over a Spherical Surface for Large-Scale Geospatial Predictions. (arXiv:2306.17624v1 [cs.CV])
    Generating learning-friendly representations for points in space is a fundamental and long-standing problem in ML. Recently, multi-scale encoding schemes (such as Space2Vec and NeRF) were proposed to directly encode any point in 2D/3D Euclidean space as a high-dimensional vector, and has been successfully applied to various geospatial prediction and generative tasks. However, all current 2D and 3D location encoders are designed to model point distances in Euclidean space. So when applied to large-scale real-world GPS coordinate datasets, which require distance metric learning on the spherical surface, both types of models can fail due to the map projection distortion problem (2D) and the spherical-to-Euclidean distance approximation error (3D). To solve these problems, we propose a multi-scale location encoder called Sphere2Vec which can preserve spherical distances when encoding point coordinates on a spherical surface. We developed a unified view of distance-reserving encoding on spheres based on the DFS. We also provide theoretical proof that the Sphere2Vec preserves the spherical surface distance between any two points, while existing encoding schemes do not. Experiments on 20 synthetic datasets show that Sphere2Vec can outperform all baseline models on all these datasets with up to 30.8% error rate reduction. We then apply Sphere2Vec to three geo-aware image classification tasks - fine-grained species recognition, Flickr image recognition, and remote sensing image classification. Results on 7 real-world datasets show the superiority of Sphere2Vec over multiple location encoders on all three tasks. Further analysis shows that Sphere2Vec outperforms other location encoder models, especially in the polar regions and data-sparse areas because of its nature for spherical surface distance preservation. Code and data are available at https://gengchenmai.github.io/sphere2vec-website/.
    Sufficient-Statistic Memory AMP. (arXiv:2112.15327v4 [cs.IT] UPDATED)
    Approximate message passing (AMP) type algorithms have been widely used in the signal reconstruction of certain large random linear systems. A key feature of the AMP-type algorithms is that their dynamics can be correctly described by state evolution. While state evolution is a useful analytic tool, its convergence is not guaranteed. To solve the convergence problem of the state evolution of AMP-type algorithms in principle, this paper proposes a sufficient-statistic memory AMP (SS-MAMP) algorithm framework under the conditions of right-unitarily invariant sensing matrices, Lipschitz-continuous local processors and the sufficient-statistic constraint (i.e., the current message of each local processor is a sufficient statistic of the signal vector given the current and all preceding messages). We show that the covariance matrices of SS-MAMP are L-banded and convergent, which is an optimal framework (from the local MMSE/LMMSE perspective) for AMP-type algorithms given the Lipschitz-continuous local processors. Given an arbitrary MAMP, we can construct an SS-MAMP by damping, which not only ensures the convergence of the state evolution, but also preserves the orthogonality, i.e., its dynamics can be correctly described by state evolution. As a byproduct, we prove that the Bayes-optimal orthogonal/vector AMP (BO-OAMP/VAMP) is an SS-MAMP. As an example, we construct a sufficient-statistic Bayes-optimal MAMP (SS-BO-MAMP) whose state evolution converges to the minimum (i.e., Bayes-optimal) mean square error (MSE) predicted by replica methods when it has a unique fixed point. In addition, the MSE of SS-BO-MAMP is not worse than the original BO-MAMP. Finally, simulations are provided to support the theoretical results.
    DisPlacing Objects: Improving Dynamic Vehicle Detection via Visual Place Recognition under Adverse Conditions. (arXiv:2306.17536v1 [cs.CV])
    Can knowing where you are assist in perceiving objects in your surroundings, especially under adverse weather and lighting conditions? In this work we investigate whether a prior map can be leveraged to aid in the detection of dynamic objects in a scene without the need for a 3D map or pixel-level map-query correspondences. We contribute an algorithm which refines an initial set of candidate object detections and produces a refined subset of highly accurate detections using a prior map. We begin by using visual place recognition (VPR) to retrieve a reference map image for a given query image, then use a binary classification neural network that compares the query and mapping image regions to validate the query detection. Once our classification network is trained, on approximately 1000 query-map image pairs, it is able to improve the performance of vehicle detection when combined with an existing off-the-shelf vehicle detector. We demonstrate our approach using standard datasets across two cities (Oxford and Zurich) under different settings of train-test separation of map-query traverse pairs. We further emphasize the performance gains of our approach against alternative design choices and show that VPR suffices for the task, eliminating the need for precise ground truth localization.
    Expressive architectures enhance interpretability of dynamics-based neural population models. (arXiv:2212.03771v4 [q-bio.NC] UPDATED)
    Artificial neural networks that can recover latent dynamics from recorded neural activity may provide a powerful avenue for identifying and interpreting the dynamical motifs underlying biological computation. Given that neural variance alone does not uniquely determine a latent dynamical system, interpretable architectures should prioritize accurate and low-dimensional latent dynamics. In this work, we evaluated the performance of sequential autoencoders (SAEs) in recovering latent chaotic attractors from simulated neural datasets. We found that SAEs with widely-used recurrent neural network (RNN)-based dynamics were unable to infer accurate firing rates at the true latent state dimensionality, and that larger RNNs relied upon dynamical features not present in the data. On the other hand, SAEs with neural ordinary differential equation (NODE)-based dynamics inferred accurate rates at the true latent state dimensionality, while also recovering latent trajectories and fixed point structure. Ablations reveal that this is mainly because NODEs (1) allow use of higher-capacity multi-layer perceptrons (MLPs) to model the vector field and (2) predict the derivative rather than the next state. Decoupling the capacity of the dynamics model from its latent dimensionality enables NODEs to learn the requisite low-D dynamics where RNN cells fail. Additionally, the fact that the NODE predicts derivatives imposes a useful autoregressive prior on the latent states. The suboptimal interpretability of widely-used RNN-based dynamics may motivate substitution for alternative architectures, such as NODE, that enable learning of accurate dynamics in low-dimensional latent spaces.
    Fact or Artifact? Revise Layer-wise Relevance Propagation on various ANN Architectures. (arXiv:2302.12317v2 [cs.LG] UPDATED)
    Layer-wise relevance propagation (LRP) is a widely used and powerful technique to reveal insights into various artificial neural network (ANN) architectures. LRP is often used in the context of image classification. The aim is to understand, which parts of the input sample have highest relevance and hence most influence on the model prediction. Relevance can be traced back through the network to attribute a certain score to each input pixel. Relevance scores are then combined and displayed as heat maps and give humans an intuitive visual understanding of classification models. Opening the black box to understand the classification engine in great detail is essential for domain experts to gain trust in ANN models. However, there are pitfalls in terms of model-inherent artifacts included in the obtained relevance maps, that can easily be missed. But for a valid interpretation, these artifacts must not be ignored. Here, we apply and revise LRP on various ANN architectures trained as classifiers on geospatial and synthetic data. Depending on the network architecture, we show techniques to control model focus and give guidance to improve the quality of obtained relevance maps to separate facts from artifacts.
    GRIL: A $2$-parameter Persistence Based Vectorization for Machine Learning. (arXiv:2304.04970v2 [cs.LG] UPDATED)
    $1$-parameter persistent homology, a cornerstone in Topological Data Analysis (TDA), studies the evolution of topological features such as connected components and cycles hidden in data. It has been applied to enhance the representation power of deep learning models, such as Graph Neural Networks (GNNs). To enrich the representations of topological features, here we propose to study $2$-parameter persistence modules induced by bi-filtration functions. In order to incorporate these representations into machine learning models, we introduce a novel vector representation called Generalized Rank Invariant Landscape (GRIL) for $2$-parameter persistence modules. We show that this vector representation is $1$-Lipschitz stable and differentiable with respect to underlying filtration functions and can be easily integrated into machine learning models to augment encoding topological features. We present an algorithm to compute the vector representation efficiently. We also test our methods on synthetic and benchmark graph datasets, and compare the results with previous vector representations of $1$-parameter and $2$-parameter persistence modules. Further, we augment GNNs with GRIL features and observe an increase in performance indicating that GRIL can capture additional features enriching GNNs. We make the complete code for the proposed method available at https://github.com/soham0209/mpml-graph.
    Online Learning with Set-Valued Feedback. (arXiv:2306.06247v2 [cs.LG] UPDATED)
    We study a variant of online multiclass classification where the learner predicts a single label but receives a \textit{set of labels} as feedback. In this model, the learner is penalized for not outputting a label contained in the revealed set. We show that unlike online multiclass learning with single-label feedback, deterministic and randomized online learnability are \textit{not equivalent} even in the realizable setting with set-valued feedback. Accordingly, we give two new combinatorial dimensions, named the Set Littlestone and Measure Shattering dimension, that tightly characterize deterministic and randomized online learnability respectively in the realizable setting. In addition, we show that the Measure Shattering dimension tightly characterizes online learnability in the agnostic setting. Finally, we show that practical learning settings like online multilabel ranking, online multilabel classification, and online interval learning are specific instances of our general framework.
    UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers. (arXiv:2301.13741v3 [cs.CV] UPDATED)
    Real-world data contains a vast amount of multimodal information, among which vision and language are the two most representative modalities. Moreover, increasingly heavier models, \textit{e}.\textit{g}., Transformers, have attracted the attention of researchers to model compression. However, how to compress multimodal models, especially vison-language Transformers, is still under-explored. This paper proposes the \textbf{U}nified and \textbf{P}r\textbf{o}gressive \textbf{P}runing (\textbf{\emph{UPop}}) as a universal vison-language Transformer compression framework, which incorporates 1) unifiedly searching multimodal subnets in a continuous optimization space from the original model, which enables automatic assignment of pruning ratios among compressible modalities and structures; 2) progressively searching and retraining the subnet, which maintains convergence between the search and retrain to attain higher compression ratios. Experiments on various tasks, datasets, and model architectures demonstrate the effectiveness and versatility of the proposed UPop framework. The code is available at https://github.com/sdc17/UPop.
    First-order ANIL learns linear representations despite misspecified latent dimension. (arXiv:2303.01335v2 [cs.LG] UPDATED)
    Due to its empirical success in few-shot classification and reinforcement learning, meta-learning has recently received significant interest. Meta-learning methods leverage data from previous tasks to learn a new task in a sample-efficient manner. In particular, model-agnostic methods look for initialisation points from which gradient descent quickly adapts to any new task. Although it has been empirically suggested that such methods perform well by learning shared representations during pretraining, there is limited theoretical evidence of such behavior. More importantly, it has not been rigorously shown that these methods still learn a shared structure, despite architectural misspecifications. In this direction, this work shows, in the limit of an infinite number of tasks, that first-order ANIL with a linear two-layer network architecture successfully learns linear shared representations. This result even holds with a misspecified network parameterisation; having a width larger than the dimension of the shared representations results in an asymptotically low-rank solution. The learnt solution then yields a good adaptation performance on any new task after a single gradient step. Overall this illustrates how well model-agnostic methods such as first-order ANIL can learn shared representations.
    On the Existence of a Complexity in Fixed Budget Bandit Identification. (arXiv:2303.09468v2 [stat.ML] UPDATED)
    In fixed budget bandit identification, an algorithm sequentially observes samples from several distributions up to a given final time. It then answers a query about the set of distributions. A good algorithm will have a small probability of error. While that probability decreases exponentially with the final time, the best attainable rate is not known precisely for most identification tasks. We show that if a fixed budget task admits a complexity, defined as a lower bound on the probability of error which is attained by the same algorithm on all bandit problems, then that complexity is determined by the best non-adaptive sampling procedure for that problem. We show that there is no such complexity for several fixed budget identification tasks including Bernoulli best arm identification with two arms: there is no single algorithm that attains everywhere the best possible rate.
    Precision Anti-Cancer Drug Selection via Neural Ranking. (arXiv:2306.17771v1 [cs.LG])
    Personalized cancer treatment requires a thorough understanding of complex interactions between drugs and cancer cell lines in varying genetic and molecular contexts. To address this, high-throughput screening has been used to generate large-scale drug response data, facilitating data-driven computational models. Such models can capture complex drug-cell line interactions across various contexts in a fully data-driven manner. However, accurately prioritizing the most sensitive drugs for each cell line still remains a significant challenge. To address this, we developed neural ranking approaches that leverage large-scale drug response data across multiple cell lines from diverse cancer types. Unlike existing approaches that primarily utilize regression and classification techniques for drug response prediction, we formulated the objective of drug selection and prioritization as a drug ranking problem. In this work, we proposed two neural listwise ranking methods that learn latent representations of drugs and cell lines, and then use those representations to score drugs in each cell line via a learnable scoring function. Specifically, we developed a neural listwise ranking method, List-One, on top of the existing method ListNet. Additionally, we proposed a novel listwise ranking method, List-All, that focuses on all the sensitive drugs instead of the top sensitive drug, unlike List-One. Our results demonstrate that List-All outperforms the best baseline with significant improvements of as much as 8.6% in hit@20 across 50% test cell lines. Furthermore, our analyses suggest that the learned latent spaces from our proposed methods demonstrate informative clustering structures and capture relevant underlying biological features. Moreover, our comprehensive empirical evaluation provides a thorough and objective comparison of the performance of different methods (including our proposed ones).
    See Through the Fog: Curriculum Learning with Progressive Occlusion in Medical Imaging. (arXiv:2306.15574v2 [cs.CV] UPDATED)
    In recent years, deep learning models have revolutionized medical image interpretation, offering substantial improvements in diagnostic accuracy. However, these models often struggle with challenging images where critical features are partially or fully occluded, which is a common scenario in clinical practice. In this paper, we propose a novel curriculum learning-based approach to train deep learning models to handle occluded medical images effectively. Our method progressively introduces occlusion, starting from clear, unobstructed images and gradually moving to images with increasing occlusion levels. This ordered learning process, akin to human learning, allows the model to first grasp simple, discernable patterns and subsequently build upon this knowledge to understand more complicated, occluded scenarios. Furthermore, we present three novel occlusion synthesis methods, namely Wasserstein Curriculum Learning (WCL), Information Adaptive Learning (IAL), and Geodesic Curriculum Learning (GCL). Our extensive experiments on diverse medical image datasets demonstrate substantial improvements in model robustness and diagnostic accuracy over conventional training methodologies.
    DUET: 2D Structured and Approximately Equivariant Representations. (arXiv:2306.16058v2 [cs.LG] UPDATED)
    Multiview Self-Supervised Learning (MSSL) is based on learning invariances with respect to a set of input transformations. However, invariance partially or totally removes transformation-related information from the representations, which might harm performance for specific downstream tasks that require such information. We propose 2D strUctured and EquivarianT representations (coined DUET), which are 2d representations organized in a matrix structure, and equivariant with respect to transformations acting on the input data. DUET representations maintain information about an input transformation, while remaining semantically expressive. Compared to SimCLR (Chen et al., 2020) (unstructured and invariant) and ESSL (Dangovski et al., 2022) (unstructured and equivariant), the structured and equivariant nature of DUET representations enables controlled generation with lower reconstruction error, while controllability is not possible with SimCLR or ESSL. DUET also achieves higher accuracy for several discriminative tasks, and improves transfer learning.
    GEC: A Unified Framework for Interactive Decision Making in MDP, POMDP, and Beyond. (arXiv:2211.01962v4 [cs.LG] UPDATED)
    We study sample efficient reinforcement learning (RL) under the general framework of interactive decision making, which includes Markov decision process (MDP), partially observable Markov decision process (POMDP), and predictive state representation (PSR) as special cases. Toward finding the minimum assumption that empowers sample efficient learning, we propose a novel complexity measure, generalized eluder coefficient (GEC), which characterizes the fundamental tradeoff between exploration and exploitation in online interactive decision making. In specific, GEC captures the hardness of exploration by comparing the error of predicting the performance of the updated policy with the in-sample training error evaluated on the historical data. We show that RL problems with low GEC form a remarkably rich class, which subsumes low Bellman eluder dimension problems, bilinear class, low witness rank problems, PO-bilinear class, and generalized regular PSR, where generalized regular PSR, a new tractable PSR class identified by us, includes nearly all known tractable POMDPs and PSRs. Furthermore, in terms of algorithm design, we propose a generic posterior sampling algorithm, which can be implemented in both model-free and model-based fashion, under both fully observable and partially observable settings. The proposed algorithm modifies the standard posterior sampling algorithm in two aspects: (i) we use an optimistic prior distribution that biases towards hypotheses with higher values and (ii) a loglikelihood function is set to be the empirical loss evaluated on the historical data, where the choice of loss function supports both model-free and model-based learning. We prove that the proposed algorithm is sample efficient by establishing a sublinear regret upper bound in terms of GEC. In summary, we provide a new and unified understanding of both fully observable and partially observable RL.
    Selecting Robust Features for Machine Learning Applications using Multidata Causal Discovery. (arXiv:2304.05294v5 [stat.ML] UPDATED)
    Robust feature selection is vital for creating reliable and interpretable Machine Learning (ML) models. When designing statistical prediction models in cases where domain knowledge is limited and underlying interactions are unknown, choosing the optimal set of features is often difficult. To mitigate this issue, we introduce a Multidata (M) causal feature selection approach that simultaneously processes an ensemble of time series datasets and produces a single set of causal drivers. This approach uses the causal discovery algorithms PC1 or PCMCI that are implemented in the Tigramite Python package. These algorithms utilize conditional independence tests to infer parts of the causal graph. Our causal feature selection approach filters out causally-spurious links before passing the remaining causal features as inputs to ML models (Multiple linear regression, Random Forest) that predict the targets. We apply our framework to the statistical intensity prediction of Western Pacific Tropical Cyclones (TC), for which it is often difficult to accurately choose drivers and their dimensionality reduction (time lags, vertical levels, and area-averaging). Using more stringent significance thresholds in the conditional independence tests helps eliminate spurious causal relationships, thus helping the ML model generalize better to unseen TC cases. M-PC1 with a reduced number of features outperforms M-PCMCI, non-causal ML, and other feature selection methods (lagged correlation, random), even slightly outperforming feature selection based on eXplainable Artificial Intelligence. The optimal causal drivers obtained from our causal feature selection help improve our understanding of underlying relationships and suggest new potential drivers of TC intensification.
    Vision Through the Veil: Differential Privacy in Federated Learning for Medical Image Classification. (arXiv:2306.17794v1 [cs.LG])
    The proliferation of deep learning applications in healthcare calls for data aggregation across various institutions, a practice often associated with significant privacy concerns. This concern intensifies in medical image analysis, where privacy-preserving mechanisms are paramount due to the data being sensitive in nature. Federated learning, which enables cooperative model training without direct data exchange, presents a promising solution. Nevertheless, the inherent vulnerabilities of federated learning necessitate further privacy safeguards. This study addresses this need by integrating differential privacy, a leading privacy-preserving technique, into a federated learning framework for medical image classification. We introduce a novel differentially private federated learning model and meticulously examine its impacts on privacy preservation and model performance. Our research confirms the existence of a trade-off between model accuracy and privacy settings. However, we demonstrate that strategic calibration of the privacy budget in differential privacy can uphold robust image classification performance while providing substantial privacy protection.
    Case-Base Neural Networks: survival analysis with time-varying, higher-order interactions. (arXiv:2301.06535v2 [stat.ML] UPDATED)
    Neural network-based survival methods can model data-driven covariate interactions. While these methods can provide better predictive performance than regression-based approaches, not all can model time-varying interactions and complex baseline hazards. To address this, we propose Case-Base Neural Networks (CBNNs) as a new approach that combines the case-base sampling framework with flexible neural network architectures. Using a novel sampling scheme and data augmentation to naturally account for censoring, we construct a feed-forward neural network that may take time as an input. CBNNs predict the probability of an event occurring at a given moment to estimate the hazard function. We compare the performance of CBNNs to regression and neural network-based survival methods in a simulation and three case studies using two time-dependent metrics. First, we examine performance on a simulation involving a complex baseline hazard and time-varying interactions to assess all methods, with CBNN outperforming competitors. Then, we apply all methods to three real data applications, with CBNNs outperforming the competing models in two studies and showing similar performance in the third. Our results highlight the benefit of combining case-base sampling with deep learning to provide a simple and flexible modeling framework for data-driven, time-varying interaction modeling of single event survival outcomes. An R package is available at https://github.com/Jesse-Islam/cbnn.
    Neural Characteristic Activation Value Analysis for Improved ReLU Network Feature Learning. (arXiv:2305.15912v2 [cs.LG] UPDATED)
    We examine the characteristic activation values of individual ReLU units in neural networks. We refer to the corresponding set for such characteristic activation values in the input space as the characteristic activation set of a ReLU unit. We draw an explicit connection between the characteristic activation set and learned features in ReLU networks. This connection leads to new insights into why various neural network normalization techniques used in modern deep learning architectures regularize and stabilize SGD optimization. Utilizing these insights, we propose a geometric approach to parameterize ReLU networks for improved feature learning. We empirically verify its usefulness with less carefully chosen initialization schemes and larger learning rates. We report improved optimization stability, faster convergence speed, and better generalization performance.
    Off-Policy Evaluation with Out-of-Sample Guarantees. (arXiv:2301.08649v3 [stat.ML] UPDATED)
    We consider the problem of evaluating the performance of a decision policy using past observational data. The outcome of a policy is measured in terms of a loss (aka. disutility or negative reward) and the main problem is making valid inferences about its out-of-sample loss when the past data was observed under a different and possibly unknown policy. Using a sample-splitting method, we show that it is possible to draw such inferences with finite-sample coverage guarantees about the entire loss distribution, rather than just its mean. Importantly, the method takes into account model misspecifications of the past policy - including unmeasured confounding. The evaluation method can be used to certify the performance of a policy using observational data under a specified range of credible model assumptions.
    Averaged Method of Multipliers for Bi-Level Optimization without Lower-Level Strong Convexity. (arXiv:2302.03407v2 [math.OC] UPDATED)
    Gradient methods have become mainstream techniques for Bi-Level Optimization (BLO) in learning fields. The validity of existing works heavily rely on either a restrictive Lower-Level Strong Convexity (LLSC) condition or on solving a series of approximation subproblems with high accuracy or both. In this work, by averaging the upper and lower level objectives, we propose a single loop Bi-level Averaged Method of Multipliers (sl-BAMM) for BLO that is simple yet efficient for large-scale BLO and gets rid of the limited LLSC restriction. We further provide non-asymptotic convergence analysis of sl-BAMM towards KKT stationary points, and the comparative advantage of our analysis lies in the absence of strong gradient boundedness assumption, which is always required by others. Thus our theory safely captures a wider variety of applications in deep learning, especially where the upper-level objective is quadratic w.r.t. the lower-level variable. Experimental results demonstrate the superiority of our method.
    Uncertainty Estimation by Fisher Information-based Evidential Deep Learning. (arXiv:2303.02045v3 [cs.LG] UPDATED)
    Uncertainty estimation is a key factor that makes deep learning reliable in practical applications. Recently proposed evidential neural networks explicitly account for different uncertainties by treating the network's outputs as evidence to parameterize the Dirichlet distribution, and achieve impressive performance in uncertainty estimation. However, for high data uncertainty samples but annotated with the one-hot label, the evidence-learning process for those mislabeled classes is over-penalized and remains hindered. To address this problem, we propose a novel method, Fisher Information-based Evidential Deep Learning ($\mathcal{I}$-EDL). In particular, we introduce Fisher Information Matrix (FIM) to measure the informativeness of evidence carried by each sample, according to which we can dynamically reweight the objective loss terms to make the network more focused on the representation learning of uncertain classes. The generalization ability of our network is further improved by optimizing the PAC-Bayesian bound. As demonstrated empirically, our proposed method consistently outperforms traditional EDL-related algorithms in multiple uncertainty estimation tasks, especially in the more challenging few-shot classification settings.
    WDC Products: A Multi-Dimensional Entity Matching Benchmark. (arXiv:2301.09521v2 [cs.LG] UPDATED)
    The difficulty of an entity matching task depends on a combination of multiple factors such as the amount of corner-case pairs, the fraction of entities in the test set that have not been seen during training, and the size of the development set. Current entity matching benchmarks usually represent single points in the space along such dimensions or they provide for the evaluation of matching methods along a single dimension, for instance the amount of training data. This paper presents WDC Products, an entity matching benchmark which provides for the systematic evaluation of matching systems along combinations of three dimensions while relying on real-world data. The three dimensions are (i) amount of corner-cases (ii) generalization to unseen entities, and (iii) development set size (training set plus validation set). Generalization to unseen entities is a dimension not covered by any of the existing English-language benchmarks yet but is crucial for evaluating the robustness of entity matching systems. Instead of learning how to match entity pairs, entity matching can also be formulated as a multi-class classification task that requires the matcher to recognize individual entities. WDC Products is the first benchmark that provides a pair-wise and a multi-class formulation of the same tasks. We evaluate WDC Products using several state-of-the-art matching systems, including Ditto, HierGAT, and R-SupCon. The evaluation shows that all matching systems struggle with unseen entities to varying degrees. It also shows that for entity matching contrastive learning is more training data efficient compared to cross-encoders.
    ERL-Re$^2$: Efficient Evolutionary Reinforcement Learning with Shared State Representation and Individual Policy Representation. (arXiv:2210.17375v2 [cs.NE] UPDATED)
    Deep Reinforcement Learning (Deep RL) and Evolutionary Algorithms (EA) are two major paradigms of policy optimization with distinct learning principles, i.e., gradient-based v.s. gradient-free. An appealing research direction is integrating Deep RL and EA to devise new methods by fusing their complementary advantages. However, existing works on combining Deep RL and EA have two common drawbacks: 1) the RL agent and EA agents learn their policies individually, neglecting efficient sharing of useful common knowledge; 2) parameter-level policy optimization guarantees no semantic level of behavior evolution for the EA side. In this paper, we propose Evolutionary Reinforcement Learning with Two-scale State Representation and Policy Representation (ERL-Re$^2$), a novel solution to the aforementioned two drawbacks. The key idea of ERL-Re$^2$ is two-scale representation: all EA and RL policies share the same nonlinear state representation while maintaining individual} linear policy representations. The state representation conveys expressive common features of the environment learned by all the agents collectively; the linear policy representation provides a favorable space for efficient policy optimization, where novel behavior-level crossover and mutation operations can be performed. Moreover, the linear policy representation allows convenient generalization of policy fitness with the help of the Policy-extended Value Function Approximator (PeVFA), further improving the sample efficiency of fitness estimation. The experiments on a range of continuous control tasks show that ERL-Re$^2$ consistently outperforms advanced baselines and achieves the State Of The Art (SOTA). Our code is available on https://github.com/yeshenpy/ERL-Re2.
    LTD: Low Temperature Distillation for Robust Adversarial Training. (arXiv:2111.02331v3 [cs.CV] UPDATED)
    Adversarial training has been widely used to enhance the robustness of neural network models against adversarial attacks. Despite the popularity of neural network models, a significant gap exists between the natural and robust accuracy of these models. In this paper, we identify one of the primary reasons for this gap is the common use of one-hot vectors as labels, which hinders the learning process for image recognition. Representing ambiguous images with one-hot vectors is imprecise and may lead the model to suboptimal solutions. To overcome this issue, we propose a novel method called Low Temperature Distillation (LTD) that generates soft labels using the modified knowledge distillation framework. Unlike previous approaches, LTD uses a relatively low temperature in the teacher model and fixed, but different temperatures for the teacher and student models. This modification boosts the model's robustness without encountering the gradient masking problem that has been addressed in defensive distillation. The experimental results demonstrate the effectiveness of the proposed LTD method combined with previous techniques, achieving robust accuracy rates of 58.19%, 31.13%, and 42.08% on CIFAR-10, CIFAR-100, and ImageNet data sets, respectively, without additional unlabeled data.
    Geometric Autoencoders -- What You See is What You Decode. (arXiv:2306.17638v1 [cs.LG])
    Visualization is a crucial step in exploratory data analysis. One possible approach is to train an autoencoder with low-dimensional latent space. Large network depth and width can help unfolding the data. However, such expressive networks can achieve low reconstruction error even when the latent representation is distorted. To avoid such misleading visualizations, we propose first a differential geometric perspective on the decoder, leading to insightful diagnostics for an embedding's distortion, and second a new regularizer mitigating such distortion. Our ``Geometric Autoencoder'' avoids stretching the embedding spuriously, so that the visualization captures the data structure more faithfully. It also flags areas where little distortion could not be achieved, thus guarding against misinterpretation.
    A method to integrate and classify normal distributions. (arXiv:2012.14331v9 [stat.ML] UPDATED)
    Univariate and multivariate normal probability distributions are widely used when modeling decisions under uncertainty. Computing the performance of such models requires integrating these distributions over specific domains, which can vary widely across models. Besides some special cases, there exist no general analytical expressions, standard numerical methods or software for these integrals. Here we present mathematical results and open-source software that provide (i) the probability in any domain of a normal in any dimensions with any parameters, (ii) the probability density, cumulative distribution, and inverse cumulative distribution of any function of a normal vector, (iii) the classification errors among any number of normal distributions, the Bayes-optimal discriminability index and relation to the operating characteristic, (iv) dimension reduction and visualizations for such problems, and (v) tests for how reliably these methods may be used on given data. We demonstrate these tools with vision research applications of detecting occluding objects in natural scenes, and detecting camouflage.
    A Gradient Smoothed Functional Algorithm with Truncated Cauchy Random Perturbations for Stochastic Optimization. (arXiv:2208.00290v4 [math.OC] UPDATED)
    In this paper, we present a stochastic gradient algorithm for minimizing a smooth objective function that is an expectation over noisy cost samples, and only the latter are observed for any given parameter. Our algorithm employs a gradient estimation scheme with random perturbations, which are formed using the truncated Cauchy distribution from the delta sphere. We analyze the bias and variance of the proposed gradient estimator. Our algorithm is found to be particularly useful in the case when the objective function is non-convex, and the parameter dimension is high. From an asymptotic convergence analysis, we establish that our algorithm converges almost surely to the set of stationary points of the objective function and obtains the asymptotic convergence rate. We also show that our algorithm avoids unstable equilibria, implying convergence to local minima. Further, we perform a non-asymptotic convergence analysis of our algorithm. In particular, we establish here a non-asymptotic bound for finding an epsilon-stationary point of the non-convex objective function. Finally, we demonstrate numerically through simulations that the performance of our algorithm outperforms GSF, SPSA, and RDSA by a significant margin over a few non-convex settings and further validate its performance over convex (noisy) objectives.
    Understanding Unfairness via Training Concept Influence. (arXiv:2306.17828v1 [cs.LG])
    Knowing the causes of a model's unfairness helps practitioners better understand their data and algorithms. This is an important yet relatively unexplored task. We look into this problem through the lens of the training data - one of the major sources of unfairness. We ask the following questions: how would a model's fairness performance change if, in its training data, some samples (1) were collected from a different (e.g. demographic) group, (2) were labeled differently, or (3) some features were changed? In other words, we quantify the fairness influence of training samples by counterfactually intervening and changing samples based on predefined concepts, i.e. data attributes such as features (X), labels (Y), or sensitive attributes (A). To calculate a training sample's influence on the model's unfairness w.r.t a concept, we first generate counterfactual samples based on the concept, i.e. the counterfactual versions of the sample if the concept were changed. We then calculate the resulting impact on the unfairness, via influence function, if the counterfactual samples were used in training. Our framework not only helps practitioners understand the observed unfairness and repair their training data, but also leads to many other applications, e.g. detecting mislabeling, fixing imbalanced representations, and detecting fairness-targeted poisoning attacks.
    TD Convergence: An Optimization Perspective. (arXiv:2306.17750v1 [cs.LG])
    We study the convergence behavior of the celebrated temporal-difference (TD) learning algorithm. By looking at the algorithm through the lens of optimization, we first argue that TD can be viewed as an iterative optimization algorithm where the function to be minimized changes per iteration. By carefully investigating the divergence displayed by TD on a classical counter example, we identify two forces that determine the convergent or divergent behavior of the algorithm. We next formalize our discovery in the linear TD setting with quadratic loss and prove that convergence of TD hinges on the interplay between these two forces. We extend this optimization perspective to prove convergence of TD in a much broader setting than just linear approximation and squared loss. Our results provide a theoretical explanation for the successful application of TD in reinforcement learning.
    Interactive Volume Visualization via Multi-Resolution Hash Encoding based Neural Representation. (arXiv:2207.11620v3 [cs.GR] UPDATED)
    Neural networks have shown great potential in compressing volume data for visualization. However, due to the high cost of training and inference, such volumetric neural representations have thus far only been applied to offline data processing and non-interactive rendering. In this paper, we demonstrate that by simultaneously leveraging modern GPU tensor cores, a native CUDA neural network framework, and a well-designed rendering algorithm with macro-cell acceleration, we can interactively ray trace volumetric neural representations (10-60fps). Our neural representations are also high-fidelity (PSNR > 30dB) and compact (10-1000x smaller). Additionally, we show that it is possible to fit the entire training step inside a rendering loop and skip the pre-training process completely. To support extreme-scale volume data, we also develop an efficient out-of-core training strategy, which allows our volumetric neural representation training to potentially scale up to terascale using only an NVIDIA RTX 3090 workstation.
    Generalized Time Warping Invariant Dictionary Learning for Time Series Classification and Clustering. (arXiv:2306.17690v1 [stat.ML])
    Dictionary learning is an effective tool for pattern recognition and classification of time series data. Among various dictionary learning techniques, the dynamic time warping (DTW) is commonly used for dealing with temporal delays, scaling, transformation, and many other kinds of temporal misalignments issues. However, the DTW suffers overfitting or information loss due to its discrete nature in aligning time series data. To address this issue, we propose a generalized time warping invariant dictionary learning algorithm in this paper. Our approach features a generalized time warping operator, which consists of linear combinations of continuous basis functions for facilitating continuous temporal warping. The integration of the proposed operator and the dictionary learning is formulated as an optimization problem, where the block coordinate descent method is employed to jointly optimize warping paths, dictionaries, and sparseness coefficients. The optimized results are then used as hyperspace distance measures to feed classification and clustering algorithms. The superiority of the proposed method in terms of dictionary learning, classification, and clustering is validated through ten sets of public datasets in comparing with various benchmark methods.
    Approximating Probability Distributions by using Wasserstein Generative Adversarial Networks. (arXiv:2103.10060v4 [cs.LG] UPDATED)
    Studied here are Wasserstein generative adversarial networks (WGANs) with GroupSort neural networks as their discriminators. It is shown that the error bound of the approximation for the target distribution depends on the width and depth (capacity) of the generators and discriminators and the number of samples in training. A quantified generalization bound is established for the Wasserstein distance between the generated and target distributions. According to the theoretical results, WGANs have a higher requirement for the capacity of discriminators than that of generators, which is consistent with some existing results. More importantly, the results with overly deep and wide (high-capacity) generators may be worse than those with low-capacity generators if discriminators are insufficiently strong. Numerical results obtained using Swiss roll and MNIST datasets confirm the theoretical results.
    Structure-based Drug Design with Equivariant Diffusion Models. (arXiv:2210.13695v2 [q-bio.BM] UPDATED)
    Structure-based drug design (SBDD) aims to design small-molecule ligands that bind with high affinity and specificity to pre-determined protein targets. In this paper, we formulate SBDD as a 3D-conditional generation problem and present DiffSBDD, an SE(3)-equivariant 3D-conditional diffusion model that generates novel ligands conditioned on protein pockets. Comprehensive in silico experiments demonstrate the efficiency and effectiveness of DiffSBDD in generating novel and diverse drug-like ligands with competitive docking scores. We further explore the flexibility of the diffusion framework for a broader range of tasks in drug design campaigns, such as off-the-shelf property optimization and partial molecular design with inpainting.
    Accuracy Boosters: Epoch-Driven Mixed-Mantissa Block Floating-Point for DNN Training. (arXiv:2211.10737v3 [cs.LG] UPDATED)
    The unprecedented growth in DNN model complexity, size, and amount of training data has led to a commensurate increase in demand for computing and a search for minimal encoding. Recent research advocates Hybrid Block Floating Point (HBFP) to minimize silicon provisioning in accelerators by converting the majority of arithmetic operations in training to 8-bit fixed point. In this paper, we perform a full-scale exploration of the HBFP design space using mathematical tools to study the interplay among various parameters and identify opportunities for even smaller encodings across layers and epochs. Based on our findings, we propose Accuracy Boosters, an epoch-driven mixed-mantissa HBFP technique that uses 6-bit mantissas only in the last epoch and first/last layers, and 4-bit mantissas for $99.7\%$ of all other arithmetic operations in training. Using analytic models, we show Accuracy Boosters enable increasing arithmetic density for an HBFP training accelerator by up to $21.3\times$ compared to FP32 and up to $4.4\times$ compared to another SOTA format Bfloat16, while preserving or outperforming FP32 accuracy.
    ChatGPT for Robotics: Design Principles and Model Abilities. (arXiv:2306.17582v1 [cs.AI])
    This paper presents an experimental study regarding the use of OpenAI's ChatGPT for robotics applications. We outline a strategy that combines design principles for prompt engineering and the creation of a high-level function library which allows ChatGPT to adapt to different robotics tasks, simulators, and form factors. We focus our evaluations on the effectiveness of different prompt engineering techniques and dialog strategies towards the execution of various types of robotics tasks. We explore ChatGPT's ability to use free-form dialog, parse XML tags, and to synthesize code, in addition to the use of task-specific prompting functions and closed-loop reasoning through dialogues. Our study encompasses a range of tasks within the robotics domain, from basic logical, geometrical, and mathematical reasoning all the way to complex domains such as aerial navigation, manipulation, and embodied agents. We show that ChatGPT can be effective at solving several of such tasks, while allowing users to interact with it primarily via natural language instructions. In addition to these studies, we introduce an open-sourced research tool called PromptCraft, which contains a platform where researchers can collaboratively upload and vote on examples of good prompting schemes for robotics applications, as well as a sample robotics simulator with ChatGPT integration, making it easier for users to get started with using ChatGPT for robotics.
    Sequential Recommendation Model for Next Purchase Prediction. (arXiv:2207.06225v2 [cs.IR] UPDATED)
    Timeliness and contextual accuracy of recommendations are increasingly important when delivering contemporary digital marketing experiences. Conventional recommender systems (RS) suggest relevant but time-invariant items to users by accounting for their past purchases. These recommendations only map to customers' general preferences rather than a customer's specific needs immediately preceding a purchase. In contrast, RSs that consider the order of transactions, purchases, or experiences to measure evolving preferences can offer more salient and effective recommendations to customers: Sequential RSs not only benefit from a better behavioral understanding of a user's current needs but also better predictive power. In this paper, we demonstrate and rank the effectiveness of a sequential recommendation system by utilizing a production dataset of over 2.7 million credit card transactions for 46K cardholders. The method first employs an autoencoder on raw transaction data and submits observed transaction encodings to a GRU-based sequential model. The sequential model produces a MAP@1 metric of 47% on the out-of-sample test set, in line with existing research. We also discuss implications for embedding real-time predictions using the sequential RS into Nexus, a scalable, low-latency, event-based digital experience architecture.
    Improving Expert Predictions with Conformal Prediction. (arXiv:2201.12006v5 [cs.LG] UPDATED)
    Automated decision support systems promise to help human experts solve multiclass classification tasks more efficiently and accurately. However, existing systems typically require experts to understand when to cede agency to the system or when to exercise their own agency. Otherwise, the experts may be better off solving the classification tasks on their own. In this work, we develop an automated decision support system that, by design, does not require experts to understand when to trust the system to improve performance. Rather than providing (single) label predictions and letting experts decide when to trust these predictions, our system provides sets of label predictions constructed using conformal prediction$\unicode{x2014}$prediction sets$\unicode{x2014}$and forcefully asks experts to predict labels from these sets. By using conformal prediction, our system can precisely trade-off the probability that the true label is not in the prediction set, which determines how frequently our system will mislead the experts, and the size of the prediction set, which determines the difficulty of the classification task the experts need to solve using our system. In addition, we develop an efficient and near-optimal search method to find the conformal predictor under which the experts benefit the most from using our system. Simulation experiments using synthetic and real expert predictions demonstrate that our system may help experts make more accurate predictions and is robust to the accuracy of the classifier the conformal predictor relies on.
    Longitudinal Multimodal Transformer Integrating Imaging and Latent Clinical Signatures From Routine EHRs for Pulmonary Nodule Classification. (arXiv:2304.02836v5 [eess.IV] UPDATED)
    The accuracy of predictive models for solitary pulmonary nodule (SPN) diagnosis can be greatly increased by incorporating repeat imaging and medical context, such as electronic health records (EHRs). However, clinically routine modalities such as imaging and diagnostic codes can be asynchronous and irregularly sampled over different time scales which are obstacles to longitudinal multimodal learning. In this work, we propose a transformer-based multimodal strategy to integrate repeat imaging with longitudinal clinical signatures from routinely collected EHRs for SPN classification. We perform unsupervised disentanglement of latent clinical signatures and leverage time-distance scaled self-attention to jointly learn from clinical signatures expressions and chest computed tomography (CT) scans. Our classifier is pretrained on 2,668 scans from a public dataset and 1,149 subjects with longitudinal chest CTs, billing codes, medications, and laboratory tests from EHRs of our home institution. Evaluation on 227 subjects with challenging SPNs revealed a significant AUC improvement over a longitudinal multimodal baseline (0.824 vs 0.752 AUC), as well as improvements over a single cross-section multimodal scenario (0.809 AUC) and a longitudinal imaging-only scenario (0.741 AUC). This work demonstrates significant advantages with a novel approach for co-learning longitudinal imaging and non-imaging phenotypes with transformers. Code available at https://github.com/MASILab/lmsignatures.
    Robust Implicit Regularization via Weight Normalization. (arXiv:2305.05448v2 [cs.LG] UPDATED)
    Overparameterized models may have many interpolating solutions; implicit regularization refers to the hidden preference of a particular optimization method towards a certain interpolating solution among the many. A by now established line of work has shown that (stochastic) gradient descent tends to have an implicit bias towards low rank and/or sparse solutions when used to train deep linear networks, explaining to some extent why overparameterized neural network models trained by gradient descent tend to have good generalization performance in practice. However, existing theory for square-loss objectives often requires very small initialization of the trainable weights, which is at odds with the larger scale at which weights are initialized in practice for faster convergence and better generalization performance. In this paper, we aim to close this gap by incorporating and analyzing gradient descent with weight normalization, where the weight vector is reparamterized in terms of polar coordinates, and gradient descent is applied to the polar coordinates. By analyzing key invariants of the gradient flow and using Lojasiewicz's Theorem, we show that weight normalization also has an implicit bias towards sparse solutions in the diagonal linear model, but that in contrast to plain gradient descent, weight normalization enables a robust bias that persists even if the weights are initialized at practically large scale. Experiments suggest that the gains in both convergence speed and robustness of the implicit bias are improved dramatically by using weight normalization in overparameterized diagonal linear network models.
    Towards Complex Dynamic Physics System Simulation with Graph Neural ODEs. (arXiv:2305.12334v4 [cs.LG] UPDATED)
    The great learning ability of deep learning models facilitates us to comprehend the real physical world, making learning to simulate complicated particle systems a promising endeavour. However, the complex laws of the physical world pose significant challenges to the learning based simulations, such as the varying spatial dependencies between interacting particles and varying temporal dependencies between particle system states in different time stamps, which dominate particles' interacting behaviour and the physical systems' evolution patterns. Existing learning based simulation methods fail to fully account for the complexities, making them unable to yield satisfactory simulations. To better comprehend the complex physical laws, this paper proposes a novel learning based simulation model- Graph Networks with Spatial-Temporal neural Ordinary Equations (GNSTODE)- that characterizes the varying spatial and temporal dependencies in particle systems using a united end-to-end framework. Through training with real-world particle-particle interaction observations, GNSTODE is able to simulate any possible particle systems with high precisions. We empirically evaluate GNSTODE's simulation performance on two real-world particle systems, Gravity and Coulomb, with varying levels of spatial and temporal dependencies. The results show that the proposed GNSTODE yields significantly better simulations than state-of-the-art learning based simulation methods, which proves that GNSTODE can serve as an effective solution to particle simulations in real-world application.
    Graphtester: Exploring Theoretical Boundaries of GNNs on Graph Datasets. (arXiv:2306.17482v1 [cs.LG])
    Graph Neural Networks (GNNs) have emerged as a powerful tool for learning from graph-structured data. However, even state-of-the-art architectures have limitations on what structures they can distinguish, imposing theoretical limits on what the networks can achieve on different datasets. In this paper, we provide a new tool called Graphtester for a comprehensive analysis of the theoretical capabilities of GNNs for various datasets, tasks, and scores. We use Graphtester to analyze over 40 different graph datasets, determining upper bounds on the performance of various GNNs based on the number of layers. Further, we show that the tool can also be used for Graph Transformers using positional node encodings, thereby expanding its scope. Finally, we demonstrate that features generated by Graphtester can be used for practical applications such as Graph Transformers, and provide a synthetic dataset to benchmark node and edge features, such as positional encodings. The package is freely available at the following URL: https://github.com/meakbiyik/graphtester.
    Look, Remember and Reason: Visual Reasoning with Grounded Rationales. (arXiv:2306.17778v1 [cs.CV])
    Large language models have recently shown human level performance on a variety of reasoning tasks. However, the ability of these models to perform complex visual reasoning has not been studied in detail yet. A key challenge in many visual reasoning tasks is that the visual information needs to be tightly integrated in the reasoning process. We propose to address this challenge by drawing inspiration from human visual problem solving which depends on a variety of low-level visual capabilities. It can often be cast as the three step-process of ``Look, Remember, Reason'': visual information is incrementally extracted using low-level visual routines in a step-by-step fashion until a final answer is reached. We follow the same paradigm to enable existing large language models, with minimal changes to the architecture, to solve visual reasoning problems. To this end, we introduce rationales over the visual input that allow us to integrate low-level visual capabilities, such as object recognition and tracking, as surrogate tasks. We show competitive performance on diverse visual reasoning tasks from the CLEVR, CATER, and ACRE datasets over state-of-the-art models designed specifically for these tasks.
    Private Online Prediction from Experts: Separations and Faster Rates. (arXiv:2210.13537v3 [cs.LG] UPDATED)
    Online prediction from experts is a fundamental problem in machine learning and several works have studied this problem under privacy constraints. We propose and analyze new algorithms for this problem that improve over the regret bounds of the best existing algorithms for non-adaptive adversaries. For approximate differential privacy, our algorithms achieve regret bounds of $\tilde{O}(\sqrt{T \log d} + \log d/\varepsilon)$ for the stochastic setting and $\tilde{O}(\sqrt{T \log d} + T^{1/3} \log d/\varepsilon)$ for oblivious adversaries (where $d$ is the number of experts). For pure DP, our algorithms are the first to obtain sub-linear regret for oblivious adversaries in the high-dimensional regime $d \ge T$. Moreover, we prove new lower bounds for adaptive adversaries. Our results imply that unlike the non-private setting, there is a strong separation between the optimal regret for adaptive and non-adaptive adversaries for this problem. Our lower bounds also show a separation between pure and approximate differential privacy for adaptive adversaries where the latter is necessary to achieve the non-private $O(\sqrt{T})$ regret.
    Why Deep Models Often cannot Beat Non-deep Counterparts on Molecular Property Prediction?. (arXiv:2306.17702v1 [cs.LG])
    Molecular property prediction (MPP) is a crucial task in the drug discovery pipeline, which has recently gained considerable attention thanks to advances in deep neural networks. However, recent research has revealed that deep models struggle to beat traditional non-deep ones on MPP. In this study, we benchmark 12 representative models (3 non-deep models and 9 deep models) on 14 molecule datasets. Through the most comprehensive study to date, we make the following key observations: \textbf{(\romannumeral 1)} Deep models are generally unable to outperform non-deep ones; \textbf{(\romannumeral 2)} The failure of deep models on MPP cannot be solely attributed to the small size of molecular datasets. What matters is the irregular molecule data pattern; \textbf{(\romannumeral 3)} In particular, tree models using molecular fingerprints as inputs tend to perform better than other competitors. Furthermore, we conduct extensive empirical investigations into the unique patterns of molecule data and inductive biases of various models underlying these phenomena.
    Enhancing training of physics-informed neural networks using domain-decomposition based preconditioning strategies. (arXiv:2306.17648v1 [math.NA])
    We propose to enhance the training of physics-informed neural networks (PINNs). To this aim, we introduce nonlinear additive and multiplicative preconditioning strategies for the widely used L-BFGS optimizer. The nonlinear preconditioners are constructed by utilizing the Schwarz domain-decomposition framework, where the parameters of the network are decomposed in a layer-wise manner. Through a series of numerical experiments, we demonstrate that both, additive and multiplicative preconditioners significantly improve the convergence of the standard L-BFGS optimizer, while providing more accurate solutions of the underlying partial differential equations. Moreover, the additive preconditioner is inherently parallel, thus giving rise to a novel approach to model parallelism.
    Bounded (O(1)) Regret Recommendation Learning via Synthetic Controls Oracle. (arXiv:2301.12571v2 [cs.LG] UPDATED)
    In online exploration systems where users with fixed preferences repeatedly arrive, it has recently been shown that O(1), i.e., bounded regret, can be achieved when the system is modeled as a linear contextual bandit. This result may be of interest for recommender systems, where the popularity of their items is often short-lived, as the exploration itself may be completed quickly before potential long-run non-stationarities come into play. However, in practice, exact knowledge of the linear model is difficult to justify. Furthermore, potential existence of unobservable covariates, uneven user arrival rates, interpretation of the necessary rank condition, and users opting out of private data tracking all need to be addressed for practical recommender system applications. In this work, we conduct a theoretical study to address all these issues while still achieving bounded regret. Aside from proof techniques, the key differentiating assumption we make here is the presence of effective Synthetic Control Methods (SCM), which are shown to be a practical relaxation of the exact linear model knowledge assumption. We verify our theoretical bounded regret result using a minimal simulation experiment.
    Impact of Noise on Calibration and Generalisation of Neural Networks. (arXiv:2306.17630v1 [cs.LG])
    Noise injection and data augmentation strategies have been effective for enhancing the generalisation and robustness of neural networks (NNs). Certain types of noise such as label smoothing and MixUp have also been shown to improve calibration. Since noise can be added in various stages of the NN's training, it motivates the question of when and where the noise is the most effective. We study a variety of noise types to determine how much they improve calibration and generalisation, and under what conditions. More specifically we evaluate various noise-injection strategies in both in-distribution (ID) and out-of-distribution (OOD) scenarios. The findings highlight that activation noise was the most transferable and effective in improving generalisation, while input augmentation noise was prominent in improving calibration on OOD but not necessarily ID data.
    Achieving RGB-D level Segmentation Performance from a Single ToF Camera. (arXiv:2306.17636v1 [cs.CV])
    Depth is a very important modality in computer vision, typically used as complementary information to RGB, provided by RGB-D cameras. In this work, we show that it is possible to obtain the same level of accuracy as RGB-D cameras on a semantic segmentation task using infrared (IR) and depth images from a single Time-of-Flight (ToF) camera. In order to fuse the IR and depth modalities of the ToF camera, we introduce a method utilizing depth-specific convolutions in a multi-task learning framework. In our evaluation on an in-car segmentation dataset, we demonstrate the competitiveness of our method against the more costly RGB-D approaches.
    Stay on topic with Classifier-Free Guidance. (arXiv:2306.17806v1 [cs.CL])
    Classifier-Free Guidance (CFG) has recently emerged in text-to-image generation as a lightweight technique to encourage prompt-adherence in generations. In this work, we demonstrate that CFG can be used broadly as an inference-time technique in pure language modeling. We show that CFG (1) improves the performance of Pythia, GPT-2 and LLaMA-family models across an array of tasks: Q\&A, reasoning, code generation, and machine translation, achieving SOTA on LAMBADA with LLaMA-7B over PaLM-540B; (2) brings improvements equivalent to a model with twice the parameter-count; (3) can stack alongside other inference-time methods like Chain-of-Thought and Self-Consistency, yielding further improvements in difficult tasks; (4) can be used to increase the faithfulness and coherence of assistants in challenging form-driven and content-driven prompts: in a human evaluation we show a 75\% preference for GPT4All using CFG over baseline.
    High Dimensional Data Enrichment: Interpretable, Fast, and Data-Efficient. (arXiv:1806.04047v4 [stat.ML] UPDATED)
    We consider the problem of multi-task learning in the high dimensional setting. In particular, we introduce an estimator and investigate its statistical and computational properties for the problem of multiple connected linear regressions known as Data Enrichment/Sharing. The between-tasks connections are captured by a cross-tasks \emph{common parameter}, which gets refined by per-task \emph{individual parameters}. Any convex function, e.g., norm, can characterize the structure of both common and individual parameters. We delineate the sample complexity of our estimator and provide a high probability non-asymptotic bound for estimation error of all parameters under a geometric condition. We show that the recovery of the common parameter benefits from \emph{all} of the pooled samples. We propose an iterative estimation algorithm with a geometric convergence rate and supplement our theoretical analysis with experiments on synthetic data. Overall, we present a first thorough statistical and computational analysis of inference in the data-sharing model.
    Physics-informed invertible neural network for the Koopman operator learning. (arXiv:2306.17396v1 [math.NA])
    In Koopman operator theory, a finite-dimensional nonlinear system is transformed into an infinite but linear system using a set of observable functions. However, manually selecting observable functions that span the invariant subspace of the Koopman operator based on prior knowledge is inefficient and challenging, particularly when little or no information is available about the underlying systems. Furthermore, current methodologies tend to disregard the importance of the invertibility of observable functions, which leads to inaccurate results. To address these challenges, we propose the so-called FlowDMD, a Flow-based Dynamic Mode Decomposition that utilizes the Coupling Flow Invertible Neural Network (CF-INN) framework. FlowDMD leverages the intrinsically invertible characteristics of the CF-INN to learn the invariant subspaces of the Koopman operator and accurately reconstruct state variables. Numerical experiments demonstrate the superior performance of our algorithm compared to state-of-the-art methodologies.
    The Implicit Bias of Minima Stability in Multivariate Shallow ReLU Networks. (arXiv:2306.17499v1 [cs.LG])
    We study the type of solutions to which stochastic gradient descent converges when used to train a single hidden-layer multivariate ReLU network with the quadratic loss. Our results are based on a dynamical stability analysis. In the univariate case, it was shown that linearly stable minima correspond to network functions (predictors), whose second derivative has a bounded weighted $L^1$ norm. Notably, the bound gets smaller as the step size increases, implying that training with a large step size leads to `smoother' predictors. Here we generalize this result to the multivariate case, showing that a similar result applies to the Laplacian of the predictor. We demonstrate the tightness of our bound on the MNIST dataset, and show that it accurately captures the behavior of the solutions as a function of the step size. Additionally, we prove a depth separation result on the approximation power of ReLU networks corresponding to stable minima of the loss. Specifically, although shallow ReLU networks are universal approximators, we prove that stable shallow networks are not. Namely, there is a function that cannot be well-approximated by stable single hidden-layer ReLU networks trained with a non-vanishing step size. This is while the same function can be realized as a stable two hidden-layer ReLU network. Finally, we prove that if a function is sufficiently smooth (in a Sobolev sense) then it can be approximated arbitrarily well using shallow ReLU networks that correspond to stable solutions of gradient descent.
    Beyond Neural-on-Neural Approaches to Speaker Gender Protection. (arXiv:2306.17700v1 [eess.AS])
    Recent research has proposed approaches that modify speech to defend against gender inference attacks. The goal of these protection algorithms is to control the availability of information about a speaker's gender, a privacy-sensitive attribute. Currently, the common practice for developing and testing gender protection algorithms is "neural-on-neural", i.e., perturbations are generated and tested with a neural network. In this paper, we propose to go beyond this practice to strengthen the study of gender protection. First, we demonstrate the importance of testing gender inference attacks that are based on speech features historically developed by speech scientists, alongside the conventionally used neural classifiers. Next, we argue that researchers should use speech features to gain insight into how protective modifications change the speech signal. Finally, we point out that gender-protection algorithms should be compared with novel "vocal adversaries", human-executed voice adaptations, in order to improve interpretability and enable before-the-mic protection.
    Scaling Model Checking for DNN Analysis via State-Space Reduction and Input Segmentation (Extended Version). (arXiv:2306.17323v1 [cs.LG])
    Owing to their remarkable learning capabilities and performance in real-world applications, the use of machine learning systems based on Neural Networks (NNs) has been continuously increasing. However, various case studies and empirical findings in the literature suggest that slight variations to NN inputs can lead to erroneous and undesirable NN behavior. This has led to considerable interest in their formal analysis, aiming to provide guarantees regarding a given NN's behavior. Existing frameworks provide robustness and/or safety guarantees for the trained NNs, using satisfiability solving and linear programming. We proposed FANNet, the first model checking-based framework for analyzing a broader range of NN properties. However, the state-space explosion associated with model checking entails a scalability problem, making the FANNet applicable only to small NNs. This work develops state-space reduction and input segmentation approaches, to improve the scalability and timing efficiency of formal NN analysis. Compared to the state-of-the-art FANNet, this enables our new model checking-based framework to reduce the verification's timing overhead by a factor of up to 8000, making the framework applicable to NNs even with approximately $80$ times more network parameters. This in turn allows the analysis of NN safety properties using the new framework, in addition to all the NN properties already included with FANNet. The framework is shown to be efficiently able to analyze properties of NNs trained on healthcare datasets as well as the well--acknowledged ACAS Xu NNs.
    Class-Incremental Learning using Diffusion Model for Distillation and Replay. (arXiv:2306.17560v1 [cs.LG])
    Class-incremental learning aims to learn new classes in an incremental fashion without forgetting the previously learned ones. Several research works have shown how additional data can be used by incremental models to help mitigate catastrophic forgetting. In this work, following the recent breakthrough in text-to-image generative models and their wide distribution, we propose the use of a pretrained Stable Diffusion model as a source of additional data for class-incremental learning. Compared to competitive methods that rely on external, often unlabeled, datasets of real images, our approach can generate synthetic samples belonging to the same classes as the previously encountered images. This allows us to use those additional data samples not only in the distillation loss but also for replay in the classification loss. Experiments on the competitive benchmarks CIFAR100, ImageNet-Subset, and ImageNet demonstrate how this new approach can be used to further improve the performance of state-of-the-art methods for class-incremental learning on large scale datasets.
    Augmenting Holistic Review in University Admission using Natural Language Processing for Essays and Recommendation Letters. (arXiv:2306.17575v1 [cs.CL])
    University admission at many highly selective institutions uses a holistic review process, where all aspects of the application, including protected attributes (e.g., race, gender), grades, essays, and recommendation letters are considered, to compose an excellent and diverse class. In this study, we empirically evaluate how influential protected attributes are for predicting admission decisions using a machine learning (ML) model, and in how far textual information (e.g., personal essay, teacher recommendation) may substitute for the loss of protected attributes in the model. Using data from 14,915 applicants to an undergraduate admission office at a selective U.S. institution in the 2022-2023 cycle, we find that the exclusion of protected attributes from the ML model leads to substantially reduced admission-prediction performance. The inclusion of textual information via both a TF-IDF representation and a Latent Dirichlet allocation (LDA) model partially restores model performance, but does not appear to provide a full substitute for admitting a similarly diverse class. In particular, while the text helps with gender diversity, the proportion of URM applicants is severely impacted by the exclusion of protected attributes, and the inclusion of new attributes generated from the textual information does not recover this performance loss.
    Lifelong Learning for Neural powered Mixed Integer Programming. (arXiv:2208.12226v3 [math.OC] UPDATED)
    Mixed Integer programs (MIPs) are typically solved by the Branch-and-Bound algorithm. Recently, Learning to imitate fast approximations of the expert strong branching heuristic has gained attention due to its success in reducing the running time for solving MIPs. However, existing learning-to-branch methods assume that the entire training data is available in a single session of training. This assumption is often not true, and if the training data is supplied in continual fashion over time, existing techniques suffer from catastrophic forgetting. In this work, we study the hitherto unexplored paradigm of Lifelong Learning to Branch on Mixed Integer Programs. To mitigate catastrophic forgetting, we propose LIMIP, which is powered by the idea of modeling an MIP instance in the form of a bipartite graph, which we map to an embedding space using a bipartite Graph Attention Network. This rich embedding space avoids catastrophic forgetting through the application of knowledge distillation and elastic weight consolidation, wherein we learn the parameters key towards retaining efficacy and are therefore protected from significant drift. We evaluate LIMIP on a series of NP-hard problems and establish that in comparison to existing baselines, LIMIP is up to 50% better when confronted with lifelong learning.
    Hierarchical Bayesian Regression for Multi-Location Sales Transaction Forecasting. (arXiv:2306.17795v1 [stat.AP])
    The features in many prediction models naturally take the form of a hierarchy. The lower levels represent individuals or events. These units group naturally into locations and intervals or other aggregates, often at multiple levels. Levels of groupings may intersect and join, much as relational database tables do. Besides representing the structure of the data, predictive features in hierarchical models can be assigned to their proper levels. Such models lend themselves to hierarchical Bayes solution methods that ``share'' results of inference between groups by generalizing over the case of individual models for each group versus one model that aggregates all groups into one. In this paper we show our work-in-progress applying a hierarchical Bayesian model to forecast purchases throughout the day at store franchises, with groupings over locations and days of the week. We demonstrate using the \textsf{stan} package on individual sales transaction data collected over the course of a year. We show how this solves the dilemma of having limited data and hence modest accuracy for each day and location, while being able to scale to a large number of locations with improved accuracy.
    Why Shallow Networks Struggle with Approximating and Learning High Frequency: A Numerical Study. (arXiv:2306.17301v1 [cs.LG])
    In this work, a comprehensive numerical study involving analysis and experiments shows why a two-layer neural network has difficulties handling high frequencies in approximation and learning when machine precision and computation cost are important factors in real practice. In particular, the following fundamental computational issues are investigated: (1) the best accuracy one can achieve given a finite machine precision, (2) the computation cost to achieve a given accuracy, and (3) stability with respect to perturbations. The key to the study is the spectral analysis of the corresponding Gram matrix of the activation functions which also shows how the properties of the activation function play a role in the picture.
    The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks. (arXiv:2306.17844v1 [cs.LG])
    Do neural networks, trained on well-understood algorithmic tasks, reliably rediscover known algorithms for solving those tasks? Several recent studies, on tasks ranging from group arithmetic to in-context linear regression, have suggested that the answer is yes. Using modular addition as a prototypical problem, we show that algorithm discovery in neural networks is sometimes more complex. Small changes to model hyperparameters and initializations can induce the discovery of qualitatively different algorithms from a fixed training set, and even parallel implementations of multiple such algorithms. Some networks trained to perform modular addition implement a familiar Clock algorithm; others implement a previously undescribed, less intuitive, but comprehensible procedure which we term the Pizza algorithm, or a variety of even more complex procedures. Our results show that even simple learning problems can admit a surprising diversity of solutions, motivating the development of new tools for characterizing the behavior of neural networks across their algorithmic phase space.
    Efficient uniform approximation using Random Vector Functional Link networks. (arXiv:2306.17501v1 [stat.ML])
    A Random Vector Functional Link (RVFL) network is a depth-2 neural network with random inner weights and biases. As only the outer weights of such architectures need to be learned, the learning process boils down to a linear optimization task, allowing one to sidestep the pitfalls of nonconvex optimization problems. In this paper, we prove that an RVFL with ReLU activation functions can approximate Lipschitz continuous functions provided its hidden layer is exponentially wide in the input dimension. Although it has been established before that such approximation can be achieved in $L_2$ sense, we prove it for $L_\infty$ approximation error and Gaussian inner weights. To the best of our knowledge, our result is the first of this kind. We give a nonasymptotic lower bound for the number of hidden layer nodes, depending on, among other things, the Lipschitz constant of the target function, the desired accuracy, and the input dimension. Our method of proof is rooted in probability theory and harmonic analysis.
    A Survey on Blockchain-Based Federated Learning and Data Privacy. (arXiv:2306.17338v1 [cs.LG])
    Federated learning is a decentralized machine learning paradigm that allows multiple clients to collaborate by leveraging local computational power and the models transmission. This method reduces the costs and privacy concerns associated with centralized machine learning methods while ensuring data privacy by distributing training data across heterogeneous devices. On the other hand, federated learning has the drawback of data leakage due to the lack of privacy-preserving mechanisms employed during storage, transfer, and sharing, thus posing significant risks to data owners and suppliers. Blockchain technology has emerged as a promising technology for offering secure data-sharing platforms in federated learning, especially in Industrial Internet of Things (IIoT) settings. This survey aims to compare the performance and security of various data privacy mechanisms adopted in blockchain-based federated learning architectures. We conduct a systematic review of existing literature on secure data-sharing platforms for federated learning provided by blockchain technology, providing an in-depth overview of blockchain-based federated learning, its essential components, and discussing its principles, and potential applications. The primary contribution of this survey paper is to identify critical research questions and propose potential directions for future research in blockchain-based federated learning.
    Variational principle to regularize machine-learned density functionals: the non-interacting kinetic-energy functional. (arXiv:2306.17587v1 [physics.chem-ph])
    Practical density functional theory (DFT) owes its success to the groundbreaking work of Kohn and Sham that introduced the exact calculation of the non-interacting kinetic energy of the electrons using an auxiliary mean-field system. However, the full power of DFT will not be unleashed until the exact relationship between the electron density and the non-interacting kinetic energy is found. Various attempts have been made to approximate this functional, similar to the exchange--correlation functional, with much less success due to the larger contribution of kinetic energy and its more non-local nature. In this work we propose a new and efficient regularization method to train density functionals based on deep neural networks, with particular interest in the kinetic-energy functional. The method is tested on (effectively) one-dimensional systems, including the hydrogen chain, non-interacting electrons, and atoms of the first two periods, with excellent results. For the atomic systems, the generalizability of the regularization method is demonstrated by training also an exchange--correlation functional, and the contrasting nature of the two functionals is discussed from a machine-learning perspective.
    Bayesian Optimization with Formal Safety Guarantees via Online Conformal Prediction. (arXiv:2306.17815v1 [cs.LG])
    Black-box zero-th order optimization is a central primitive for applications in fields as diverse as finance, physics, and engineering. In a common formulation of this problem, a designer sequentially attempts candidate solutions, receiving noisy feedback on the value of each attempt from the system. In this paper, we study scenarios in which feedback is also provided on the safety of the attempted solution, and the optimizer is constrained to limit the number of unsafe solutions that are tried throughout the optimization process. Focusing on methods based on Bayesian optimization (BO), prior art has introduced an optimization scheme -- referred to as SAFEOPT -- that is guaranteed not to select any unsafe solution with a controllable probability over feedback noise as long as strict assumptions on the safety constraint function are met. In this paper, a novel BO-based approach is introduced that satisfies safety requirements irrespective of properties of the constraint function. This strong theoretical guarantee is obtained at the cost of allowing for an arbitrary, controllable but non-zero, rate of violation of the safety constraint. The proposed method, referred to as SAFE-BOCP, builds on online conformal prediction (CP) and is specialized to the cases in which feedback on the safety constraint is either noiseless or noisy. Experimental results on synthetic and real-world data validate the advantages and flexibility of the proposed SAFE-BOCP.
    Designing Stable Neural Networks using Convex Analysis and ODEs. (arXiv:2306.17332v1 [cs.LG])
    Motivated by classical work on the numerical integration of ordinary differential equations we present a ResNet-styled neural network architecture that encodes non-expansive (1-Lipschitz) operators, as long as the spectral norms of the weights are appropriately constrained. This is to be contrasted with the ordinary ResNet architecture which, even if the spectral norms of the weights are constrained, has a Lipschitz constant that, in the worst case, grows exponentially with the depth of the network. Further analysis of the proposed architecture shows that the spectral norms of the weights can be further constrained to ensure that the network is an averaged operator, making it a natural candidate for a learned denoiser in Plug-and-Play algorithms. Using a novel adaptive way of enforcing the spectral norm constraints, we show that, even with these constraints, it is possible to train performant networks. The proposed architecture is applied to the problem of adversarially robust image classification, to image denoising, and finally to the inverse problem of deblurring.
    Navigation of micro-robot swarms for targeted delivery using reinforcement learning. (arXiv:2306.17598v1 [cs.RO])
    Micro robotics is quickly emerging to be a promising technological solution to many medical treatments with focus on targeted drug delivery. They are effective when working in swarms whose individual control is mostly infeasible owing to their minute size. Controlling a number of robots with a single controller is thus important and artificial intelligence can help us perform this task successfully. In this work, we use the Reinforcement Learning (RL) algorithms Proximal Policy Optimization (PPO) and Robust Policy Optimization (RPO) to navigate a swarm of 4, 9 and 16 microswimmers under hydrodynamic effects, controlled by their orientation, towards a circular absorbing target. We look at both PPO and RPO performances with limited state information scenarios and also test their robustness for random target location and size. We use curriculum learning to improve upon the performance and demonstrate the same in learning to navigate a swarm of 25 swimmers and steering the swarm to exemplify the manoeuvring capabilities of the RL model.
    Thompson sampling for improved exploration in GFlowNets. (arXiv:2306.17693v1 [cs.LG])
    Generative flow networks (GFlowNets) are amortized variational inference algorithms that treat sampling from a distribution over compositional objects as a sequential decision-making problem with a learnable action policy. Unlike other algorithms for hierarchical sampling that optimize a variational bound, GFlowNet algorithms can stably run off-policy, which can be advantageous for discovering modes of the target distribution. Despite this flexibility in the choice of behaviour policy, the optimal way of efficiently selecting trajectories for training has not yet been systematically explored. In this paper, we view the choice of trajectories for training as an active learning problem and approach it using Bayesian techniques inspired by methods for multi-armed bandits. The proposed algorithm, Thompson sampling GFlowNets (TS-GFN), maintains an approximate posterior distribution over policies and samples trajectories from this posterior for training. We show in two domains that TS-GFN yields improved exploration and thus faster convergence to the target distribution than the off-policy exploration strategies used in past work.
    Global Optimality in Bivariate Gradient-based DAG Learning. (arXiv:2306.17378v1 [cs.LG])
    Recently, a new class of non-convex optimization problems motivated by the statistical problem of learning an acyclic directed graphical model from data has attracted significant interest. While existing work uses standard first-order optimization schemes to solve this problem, proving the global optimality of such approaches has proven elusive. The difficulty lies in the fact that unlike other non-convex problems in the literature, this problem is not "benign", and possesses multiple spurious solutions that standard approaches can easily get trapped in. In this paper, we prove that a simple path-following optimization scheme globally converges to the global minimum of the population loss in the bivariate setting.
    The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit. (arXiv:2306.17759v1 [stat.ML])
    In deep learning theory, the covariance matrix of the representations serves as a proxy to examine the network's trainability. Motivated by the success of Transformers, we study the covariance matrix of a modified Softmax-based attention model with skip connections in the proportional limit of infinite-depth-and-width. We show that at initialization the limiting distribution can be described by a stochastic differential equation (SDE) indexed by the depth-to-width ratio. To achieve a well-defined stochastic limit, the Transformer's attention mechanism is modified by centering the Softmax output at identity, and scaling the Softmax logits by a width-dependent temperature parameter. We examine the stability of the network through the corresponding SDE, showing how the scale of both the drift and diffusion can be elegantly controlled with the aid of residual connections. The existence of a stable SDE implies that the covariance structure is well-behaved, even for very large depth and width, thus preventing the notorious issues of rank degeneracy in deep attention models. Finally, we show, through simulations, that the SDE provides a surprisingly good description of the corresponding finite-size model. We coin the name shaped Transformer for these architectural modifications.
    Learning Bilinear Models of Actuated Koopman Generators from Partially-Observed Trajectories. (arXiv:2209.09977v2 [math.DS] UPDATED)
    Data-driven models for nonlinear dynamical systems based on approximating the underlying Koopman operator or generator have proven to be successful tools for forecasting, feature learning, state estimation, and control. It has become well known that the Koopman generators for control-affine systems also have affine dependence on the input, leading to convenient finite-dimensional bilinear approximations of the dynamics. Yet there are still two main obstacles that limit the scope of current approaches for approximating the Koopman generators of systems with actuation. First, the performance of existing methods depends heavily on the choice of basis functions over which the Koopman generator is to be approximated; and there is currently no universal way to choose them for systems that are not measure preserving. Secondly, if we do not observe the full state, we may not gain access to a sufficiently rich collection of such functions to describe the dynamics. This is because the commonly used method of forming time-delayed observables fails when there is actuation. To remedy these issues, we write the dynamics of observables governed by the Koopman generator as a bilinear hidden Markov model, and determine the model parameters using the expectation-maximization (EM) algorithm. The E-step involves a standard Kalman filter and smoother, while the M-step resembles control-affine dynamic mode decomposition for the generator. We demonstrate the performance of this method on three examples, including recovery of a finite-dimensional Koopman-invariant subspace for an actuated system with a slow manifold; estimation of Koopman eigenfunctions for the unforced Duffing equation; and model-predictive control of a fluidic pinball system based only on noisy observations of lift and drag.
    Unsupervised Text Embedding Space Generation Using Generative Adversarial Networks for Text Synthesis. (arXiv:2306.17181v1 [cs.CL])
    Generative Adversarial Networks (GAN) is a model for data synthesis, which creates plausible data through the competition of generator and discriminator. Although GAN application to image synthesis is extensively studied, it has inherent limitations to natural language generation. Because natural language is composed of discrete tokens, a generator has difficulty updating its gradient through backpropagation; therefore, most text-GAN studies generate sentences starting with a random token based on a reward system. Thus, the generators of previous studies are pre-trained in an autoregressive way before adversarial training, causing data memorization that synthesized sentences reproduce the training data. In this paper, we synthesize sentences using a framework similar to the original GAN. More specifically, we propose Text Embedding Space Generative Adversarial Networks (TESGAN) which generate continuous text embedding spaces instead of discrete tokens to solve the gradient backpropagation problem. Furthermore, TESGAN conducts unsupervised learning which does not directly refer to the text of the training data to overcome the data memorization issue. By adopting this novel method, TESGAN can synthesize new sentences, showing the potential of unsupervised learning for text synthesis. We expect to see extended research combining Large Language Models with a new perspective of viewing text as an continuous space.
    ReLU Neural Networks, Polyhedral Decompositions, and Persistent Homolog. (arXiv:2306.17418v1 [math.AT])
    A ReLU neural network leads to a finite polyhedral decomposition of input space and a corresponding finite dual graph. We show that while this dual graph is a coarse quantization of input space, it is sufficiently robust that it can be combined with persistent homology to detect homological signals of manifolds in the input space from samples. This property holds for a variety of networks trained for a wide range of purposes that have nothing to do with this topological application. We found this feature to be surprising and interesting; we hope it will also be useful.
    iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models. (arXiv:2306.17361v1 [cs.LG])
    Structural causal models (SCMs) are widely used in various disciplines to represent causal relationships among variables in complex systems. Unfortunately, the true underlying directed acyclic graph (DAG) structure is often unknown, and determining it from observational or interventional data remains a challenging task. However, in many situations, the end goal is to identify changes (shifts) in causal mechanisms between related SCMs rather than recovering the entire underlying DAG structure. Examples include analyzing gene regulatory network structure changes between healthy and cancerous individuals or understanding variations in biological pathways under different cellular contexts. This paper focuses on identifying $\textit{functional}$ mechanism shifts in two or more related SCMs over the same set of variables -- $\textit{without estimating the entire DAG structure of each SCM}$. Prior work under this setting assumed linear models with Gaussian noises; instead, in this work we assume that each SCM belongs to the more general class of nonlinear additive noise models (ANMs). A key contribution of this work is to show that the Jacobian of the score function for the $\textit{mixture distribution}$ allows for identification of shifts in general non-parametric functional mechanisms. Once the shifted variables are identified, we leverage recent work to estimate the structural differences, if any, for the shifted variables. Experiments on synthetic and real-world data are provided to showcase the applicability of this approach.
    $\lambda$-AC: Learning latent decision-aware models for reinforcement learning in continuous state-spaces. (arXiv:2306.17366v1 [cs.LG])
    The idea of decision-aware model learning, that models should be accurate where it matters for decision-making, has gained prominence in model-based reinforcement learning. While promising theoretical results have been established, the empirical performance of algorithms leveraging a decision-aware loss has been lacking, especially in continuous control problems. In this paper, we present a study on the necessary components for decision-aware reinforcement learning models and we showcase design choices that enable well-performing algorithms. To this end, we provide a theoretical and empirical investigation into prominent algorithmic ideas in the field. We highlight that empirical design decisions established in the MuZero line of works are vital to achieving good performance for related algorithms, and we showcase differences in behavior between different instantiations of value-aware algorithms in stochastic environments. Using these insights, we propose the Latent Model-Based Decision-Aware Actor-Critic framework ($\lambda$-AC) for decision-aware model-based reinforcement learning in continuous state-spaces and highlight important design choices in different environments.
    Multigrid-Augmented Deep Learning for the Helmholtz Equation: Better Scalability with Compact Implicit Layers. (arXiv:2306.17486v1 [cs.LG])
    We present a deep learning-based iterative approach to solve the discrete heterogeneous Helmholtz equation for high wavenumbers. Combining classical iterative multigrid solvers and convolutional neural networks (CNNs) via preconditioning, we obtain a learned neural solver that is faster and scales better than a standard multigrid solver. Our approach offers three main contributions over previous neural methods of this kind. First, we construct a multilevel U-Net-like encoder-solver CNN with an implicit layer on the coarsest grid of the U-Net, where convolution kernels are inverted. This alleviates the field of view problem in CNNs and allows better scalability. Second, we improve upon the previous CNN preconditioner in terms of the number of parameters, computation time, and convergence rates. Third, we propose a multiscale training approach that enables the network to scale to problems of previously unseen dimensions while still maintaining a reasonable training procedure. Our encoder-solver architecture can be used to generalize over different slowness models of various difficulties and is efficient at solving for many right-hand sides per slowness model. We demonstrate the benefits of our novel architecture with numerical experiments on a variety of heterogeneous two-dimensional problems at high wavenumbers.
    Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting. (arXiv:2306.17563v1 [cs.IR])
    Ranking documents using Large Language Models (LLMs) by directly feeding the query and candidate documents into the prompt is an interesting and practical problem. However, there has been limited success so far, as researchers have found it difficult to outperform fine-tuned baseline rankers on benchmark datasets. We analyze pointwise and listwise ranking prompts used by existing methods and argue that off-the-shelf LLMs do not fully understand these ranking formulations, possibly due to the nature of how LLMs are trained. In this paper, we propose to significantly reduce the burden on LLMs by using a new technique called Pairwise Ranking Prompting (PRP). Our results are the first in the literature to achieve state-of-the-art ranking performance on standard benchmarks using moderate-sized open-sourced LLMs. On TREC-DL2020, PRP based on the Flan-UL2 model with 20B parameters outperforms the previous best approach in the literature, which is based on the blackbox commercial GPT-4 that has 50x (estimated) model size, by over 5% at NDCG@1. On TREC-DL2019, PRP is only inferior to the GPT-4 solution on the NDCG@5 and NDCG@10 metrics, while outperforming other existing solutions, such as InstructGPT which has 175B parameters, by over 10% for nearly all ranking metrics. Furthermore, we propose several variants of PRP to improve efficiency and show that it is possible to achieve competitive results even with linear complexity. We also discuss other benefits of PRP, such as supporting both generation and scoring LLM APIs, as well as being insensitive to input ordering.
    Act3D: Infinite Resolution Action Detection Transformer for Robotic Manipulation. (arXiv:2306.17817v1 [cs.RO])
    3D perceptual representations are well suited for robot manipulation as they easily encode occlusions and simplify spatial reasoning. Many manipulation tasks require high spatial precision in end-effector pose prediction, typically demanding high-resolution 3D perceptual grids that are computationally expensive to process. As a result, most manipulation policies operate directly in 2D, foregoing 3D inductive biases. In this paper, we propose Act3D, a manipulation policy Transformer that casts 6-DoF keypose prediction as 3D detection with adaptive spatial computation. It takes as input 3D feature clouds unprojected from one or more camera views, iteratively samples 3D point grids in free space in a coarse-to-fine manner, featurizes them using relative spatial attention to the physical feature cloud, and selects the best feature point for end-effector pose prediction. Act3D sets a new state-of-the-art in RLbench, an established manipulation benchmark. Our model achieves 10% absolute improvement over the previous SOTA 2D multi-view policy on 74 RLbench tasks and 22% absolute improvement with 3x less compute over the previous SOTA 3D policy. In thorough ablations, we show the importance of relative spatial attention, large-scale vision-language pre-trained 2D backbones, and weight tying across coarse-to-fine attentions. Code and videos are available at our project site: https://act3d.github.io/.
    Optimal Execution Using Reinforcement Learning. (arXiv:2306.17178v1 [q-fin.TR])
    This work is about optimal order execution, where a large order is split into several small orders to maximize the implementation shortfall. Based on the diversity of cryptocurrency exchanges, we attempt to extract cross-exchange signals by aligning data from multiple exchanges for the first time. Unlike most previous studies that focused on using single-exchange information, we discuss the impact of cross-exchange signals on the agent's decision-making in the optimal execution problem. Experimental results show that cross-exchange signals can provide additional information for the optimal execution of cryptocurrency to facilitate the optimal execution process.
    Subgraph Stationary Hardware-Software Inference Co-Design. (arXiv:2306.17266v1 [cs.DC])
    A growing number of applications depend on Machine Learning (ML) functionality and benefits from both higher quality ML predictions and better timeliness (latency) at the same time. A growing body of research in computer architecture, ML, and systems software literature focuses on reaching better latency-accuracy tradeoffs for ML models. Efforts include compression, quantization, pruning, early-exit models, mixed DNN precision, as well as ML inference accelerator designs that minimize latency and energy, while preserving delivered accuracy. All of them, however, yield improvements for a single static point in the latency-accuracy tradeoff space. We make a case for applications that operate in dynamically changing deployment scenarios, where no single static point is optimal. We draw on a recently proposed weight-shared SuperNet mechanism to enable serving a stream of queries that uses (activates) different SubNets within this weight-shared construct. This creates an opportunity to exploit the inherent temporal locality with our proposed SubGraph Stationary (SGS) optimization. We take a hardware-software co-design approach with a real implementation of SGS in SushiAccel and the implementation of a software scheduler SushiSched controlling which SubNets to serve and what to cache in real-time. Combined, they are vertically integrated into SUSHI-an inference serving stack. For the stream of queries, SUSHI yields up to 25% improvement in latency, 0.98% increase in served accuracy. SUSHI can achieve up to 78.7% off-chip energy savings.
    Empirical Interpretation of the Relationship Between Speech Acoustic Context and Emotion Recognition. (arXiv:2306.17500v1 [cs.SD])
    Speech emotion recognition (SER) is vital for obtaining emotional intelligence and understanding the contextual meaning of speech. Variations of consonant-vowel (CV) phonemic boundaries can enrich acoustic context with linguistic cues, which impacts SER. In practice, speech emotions are treated as single labels over an acoustic segment for a given time duration. However, phone boundaries within speech are not discrete events, therefore the perceived emotion state should also be distributed over potentially continuous time-windows. This research explores the implication of acoustic context and phone boundaries on local markers for SER using an attention-based approach. The benefits of using a distributed approach to speech emotion understanding are supported by the results of cross-corpora analysis experiments. Experiments where phones and words are mapped to the attention vectors along with the fundamental frequency to observe the overlapping distributions and thereby the relationship between acoustic context and emotion. This work aims to bridge psycholinguistic theory research with computational modelling for SER.
    Locking On: Leveraging Dynamic Vehicle-Imposed Motion Constraints to Improve Visual Localization. (arXiv:2306.17529v1 [cs.RO])
    Most 6-DoF localization and SLAM systems use static landmarks but ignore dynamic objects because they cannot be usefully incorporated into a typical pipeline. Where dynamic objects have been incorporated, typical approaches have attempted relatively sophisticated identification and localization of these objects, limiting their robustness or general utility. In this research, we propose a middle ground, demonstrated in the context of autonomous vehicles, using dynamic vehicles to provide limited pose constraint information in a 6-DoF frame-by-frame PnP-RANSAC localization pipeline. We refine initial pose estimates with a motion model and propose a method for calculating the predicted quality of future pose estimates, triggered based on whether or not the autonomous vehicle's motion is constrained by the relative frame-to-frame location of dynamic vehicles in the environment. Our approach detects and identifies suitable dynamic vehicles to define these pose constraints to modify a pose filter, resulting in improved recall across a range of localization tolerances from $0.25m$ to $5m$, compared to a state-of-the-art baseline single image PnP method and its vanilla pose filtering. Our constraint detection system is active for approximately $35\%$ of the time on the Ford AV dataset and localization is particularly improved when the constraint detection is active.
    Kernel $\epsilon$-Greedy for Contextual Bandits. (arXiv:2306.17329v1 [stat.ML])
    We consider a kernelized version of the $\epsilon$-greedy strategy for contextual bandits. More precisely, in a setting with finitely many arms, we consider that the mean reward functions lie in a reproducing kernel Hilbert space (RKHS). We propose an online weighted kernel ridge regression estimator for the reward functions. Under some conditions on the exploration probability sequence, $\{\epsilon_t\}_t$, and choice of the regularization parameter, $\{\lambda_t\}_t$, we show that the proposed estimator is consistent. We also show that for any choice of kernel and the corresponding RKHS, we achieve a sub-linear regret rate depending on the intrinsic dimensionality of the RKHS. Furthermore, we achieve the optimal regret rate of $\sqrt{T}$ under a margin condition for finite-dimensional RKHS.
    Provable Robust Watermarking for AI-Generated Text. (arXiv:2306.17439v1 [cs.CL])
    As AI-generated text increasingly resembles human-written content, the ability to detect machine-generated text becomes crucial. To address this challenge, we present GPTWatermark, a robust and high-quality solution designed to ascertain whether a piece of text originates from a specific model. Our approach extends existing watermarking strategies and employs a fixed group design to enhance robustness against editing and paraphrasing attacks. We show that our watermarked language model enjoys strong provable guarantees on generation quality, correctness in detection, and security against evasion attacks. Experimental results on various large language models (LLMs) and diverse datasets demonstrate that our method achieves superior detection accuracy and comparable generation quality in perplexity, thus promoting the responsible use of LLMs.
    Guided Deep Generative Model-based Spatial Regularization for Multiband Imaging Inverse Problems. (arXiv:2306.17197v1 [eess.IV])
    When adopting a model-based formulation, solving inverse problems encountered in multiband imaging requires to define spatial and spectral regularizations. In most of the works of the literature, spectral information is extracted from the observations directly to derive data-driven spectral priors. Conversely, the choice of the spatial regularization often boils down to the use of conventional penalizations (e.g., total variation) promoting expected features of the reconstructed image (e.g., piecewise constant). In this work, we propose a generic framework able to capitalize on an auxiliary acquisition of high spatial resolution to derive tailored data-driven spatial regularizations. This approach leverages on the ability of deep learning to extract high level features. More precisely, the regularization is conceived as a deep generative network able to encode spatial semantic features contained in this auxiliary image of high spatial resolution. To illustrate the versatility of this approach, it is instantiated to conduct two particular tasks, namely multiband image fusion and multiband image inpainting. Experimental results obtained on these two tasks demonstrate the benefit of this class of informed regularizations when compared to more conventional ones.
    Enterprise Disk Drive Scrubbing Based on Mondrian Conformal Predictors. (arXiv:2306.17169v1 [cs.DC])
    Disk scrubbing is a process aimed at resolving read errors on disks by reading data from the disk. However, scrubbing the entire storage array at once can adversely impact system performance, particularly during periods of high input/output operations. Additionally, the continuous reading of data from disks when scrubbing can result in wear and tear, especially on larger capacity disks, due to the significant time and energy consumption involved. To address these issues, we propose a selective disk scrubbing method that enhances the overall reliability and power efficiency in data centers. Our method employs a Machine Learning model based on Mondrian Conformal prediction to identify specific disks for scrubbing, by proactively predicting the health status of each disk in the storage pool, forecasting n-days in advance, and using an open-source dataset. For disks predicted as non-healthy, we mark them for replacement without further action. For healthy drives, we create a set and quantify their relative health across the entire storage pool based on the predictor's confidence. This enables us to prioritize selective scrubbing for drives with established scrubbing frequency based on the scrub cycle. The method we propose provides an efficient and dependable solution for managing enterprise disk drives. By scrubbing just 22.7% of the total storage disks, we can achieve optimized energy consumption and reduce the carbon footprint of the data center.  ( 3 min )
    DisasterResponseGPT: Large Language Models for Accelerated Plan of Action Development in Disaster Response Scenarios. (arXiv:2306.17271v1 [cs.LG])
    The development of plans of action in disaster response scenarios is a time-consuming process. Large Language Models (LLMs) offer a powerful solution to expedite this process through in-context learning. This study presents DisasterResponseGPT, an algorithm that leverages LLMs to generate valid plans of action quickly by incorporating disaster response and planning guidelines in the initial prompt. In DisasterResponseGPT, users input the scenario description and receive a plan of action as output. The proposed method generates multiple plans within seconds, which can be further refined following the user's feedback. Preliminary results indicate that the plans of action developed by DisasterResponseGPT are comparable to human-generated ones while offering greater ease of modification in real-time. This approach has the potential to revolutionize disaster response operations by enabling rapid updates and adjustments during the plan's execution.
    FedBone: Towards Large-Scale Federated Multi-Task Learning. (arXiv:2306.17465v1 [cs.LG])
    Heterogeneous federated multi-task learning (HFMTL) is a federated learning technique that combines heterogeneous tasks of different clients to achieve more accurate, comprehensive predictions. In real-world applications, visual and natural language tasks typically require large-scale models to extract high-level abstract features. However, large-scale models cannot be directly applied to existing federated multi-task learning methods. Existing HFML methods also disregard the impact of gradient conflicts on multi-task optimization during the federated aggregation process. In this work, we propose an innovative framework called FedBone, which enables the construction of large-scale models with better generalization from the perspective of server-client split learning and gradient projection. We split the entire model into two components: a large-scale general model (referred to as the general model) on the cloud server and multiple task-specific models (referred to as the client model) on edge clients, solving the problem of insufficient computing power on edge clients. The conflicting gradient projection technique is used to enhance the generalization of the large-scale general model between different tasks. The proposed framework is evaluated on two benchmark datasets and a real ophthalmic dataset. Comprehensive results demonstrate that FedBone efficiently adapts to heterogeneous local tasks of each client and outperforms existing federated learning algorithms in most dense prediction and classification tasks with off-the-shelf computational resources on the client side.
    Machine learning for sports betting: should predictive models be optimised for accuracy or calibration?. (arXiv:2303.06021v3 [cs.LG] UPDATED)
    Sports betting's recent federal legalisation in the USA coincides with the golden age of machine learning. If bettors can leverage data to reliably predict the probability of an outcome, they can recognise when the bookmaker's odds are in their favour. As sports betting is a multi-billion dollar industry in the USA alone, identifying such opportunities could be extremely lucrative. Many researchers have applied machine learning to the sports outcome prediction problem, generally using accuracy to evaluate the performance of predictive models. We hypothesise that for the sports betting problem, model calibration is more important than accuracy. To test this hypothesis, we train models on NBA data over several seasons and run betting experiments on a single season, using published odds. We show that optimising the predictive model for calibration leads to greater returns than optimising for accuracy, on average (return on investment of $+34.69\%$ versus $-35.17\%$) and in the best case ($+36.93\%$ versus $+5.56\%$). These findings suggest that for sports betting (or any probabilistic decision-making problem), calibration is a more important metric than accuracy. Sports bettors who wish to increase profits should therefore optimise their predictive model for calibration.
    Limits of Model Selection under Transfer Learning. (arXiv:2305.00152v3 [stat.ML] UPDATED)
    Theoretical studies on transfer learning or domain adaptation have so far focused on situations with a known hypothesis class or model; however in practice, some amount of model selection is usually involved, often appearing under the umbrella term of hyperparameter-tuning: for example, one may think of the problem of tuning for the right neural network architecture towards a target task, while leveraging data from a related source task. Now, in addition to the usual tradeoffs on approximation vs estimation errors involved in model selection, this problem brings in a new complexity term, namely, the transfer distance between source and target distributions, which is known to vary with the choice of hypothesis class. We present a first study of this problem, focusing on classification; in particular, the analysis reveals some remarkable phenomena: adaptive rates, i.e., those achievable with no distributional information, can be arbitrarily slower than oracle rates, i.e., when given knowledge on distances.
    Integrating Tick-level Data and Periodical Signal for High-frequency Market Making. (arXiv:2306.17179v1 [q-fin.TR])
    We focus on the problem of market making in high-frequency trading. Market making is a critical function in financial markets that involves providing liquidity by buying and selling assets. However, the increasing complexity of financial markets and the high volume of data generated by tick-level trading makes it challenging to develop effective market making strategies. To address this challenge, we propose a deep reinforcement learning approach that fuses tick-level data with periodic prediction signals to develop a more accurate and robust market making strategy. Our results of market making strategies based on different deep reinforcement learning algorithms under the simulation scenarios and real data experiments in the cryptocurrency markets show that the proposed framework outperforms existing methods in terms of profitability and risk management.  ( 2 min )
    Steganographic Capacity of Deep Learning Models. (arXiv:2306.17189v1 [cs.CR])
    As machine learning and deep learning models become ubiquitous, it is inevitable that there will be attempts to exploit such models in various attack scenarios. For example, in a steganographic-based attack, information could be hidden in a learning model, which might then be used to distribute malware, or for other malicious purposes. In this research, we consider the steganographic capacity of several learning models. Specifically, we train a Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Transformer model on a challenging malware classification problem. For each of the resulting models, we determine the number of low-order bits of the trained parameters that can be altered without significantly affecting the performance of the model. We find that the steganographic capacity of the learning models tested is surprisingly high, and that in each case, there is a clear threshold after which model performance rapidly degrades.  ( 2 min )
    Federated Ensemble YOLOv5 - A Better Generalized Object Detection Algorithm. (arXiv:2306.17829v1 [cs.CV])
    Federated learning (FL) has gained significant traction as a privacy-preserving algorithm, but the underlying resembles of federated learning algorithm like Federated averaging (FED Avg) or Federated SGD (FED SGD) to ensemble learning algorithms has not been fully explored. The purpose of this paper is to examine the application of FL to object detection as a method to enhance generalizability, and to compare its performance against a centralized training approach for an object detection algorithm. Specifically, we investigate the performance of a YOLOv5 model trained using FL across multiple clients and employ a random sampling strategy without replacement, so each client holds a portion of the same dataset used for centralized training. Our experimental results showcase the superior efficiency of the FL object detector's global model in generating accurate bounding boxes for unseen objects, with the test set being a mixture of objects from two distinct clients not represented in the training dataset. These findings suggest that FL can be viewed from an ensemble algorithm perspective, akin to a synergistic blend of Bagging and Boosting techniques. As a result, FL can be seen not only as a method to enhance privacy, but also as a method to enhance the performance of a machine learning model.
    Learning Environment Models with Continuous Stochastic Dynamics. (arXiv:2306.17204v1 [cs.LG])
    Solving control tasks in complex environments automatically through learning offers great potential. While contemporary techniques from deep reinforcement learning (DRL) provide effective solutions, their decision-making is not transparent. We aim to provide insights into the decisions faced by the agent by learning an automaton model of environmental behavior under the control of an agent. However, for most control problems, automata learning is not scalable enough to learn a useful model. In this work, we raise the capabilities of automata learning such that it is possible to learn models for environments that have complex and continuous dynamics. The core of the scalability of our method lies in the computation of an abstract state-space representation, by applying dimensionality reduction and clustering on the observed environmental state space. The stochastic transitions are learned via passive automata learning from observed interactions of the agent and the environment. In an iterative model-based RL process, we sample additional trajectories to learn an accurate environment model in the form of a discrete-state Markov decision process (MDP). We apply our automata learning framework on popular RL benchmarking environments in the OpenAI Gym, including LunarLander, CartPole, Mountain Car, and Acrobot. Our results show that the learned models are so precise that they enable the computation of policies solving the respective control tasks. Yet the models are more concise and more general than neural-network-based policies and by using MDPs we benefit from a wealth of tools available for analyzing them. When solving the task of LunarLander, the learned model even achieved similar or higher rewards than deep RL policies learned with stable-baselines3.  ( 3 min )
    Fast and Robust State Estimation and Tracking via Hierarchical Learning. (arXiv:2306.17267v1 [cs.LG])
    Fully distributed estimation and tracking solutions to large-scale multi-agent networks suffer slow convergence and are vulnerable to network failures. In this paper, we aim to speed up the convergence and enhance the resilience of state estimation and tracking using a simple hierarchical system architecture wherein agents are clusters into smaller networks, and a parameter server exists to aid the information exchanges among networks. The information exchange among networks is expensive and occurs only once in a while. We propose two consensus + innovation algorithms for the state estimation and tracking problems, respectively. In both algorithms, we use a novel hierarchical push-sum consensus component. For the state estimation, we use dual averaging as the local innovation component. State tracking is much harder to tackle in the presence of dropping-link failures and the standard integration of the consensus and innovation approaches are no longer applicable. Moreover, dual averaging is no longer feasible. Our algorithm introduces a pair of additional variables per link and ensure the relevant local variables evolve according to the state dynamics, and use projected local gradient descent as the local innovation component. We also characterize the convergence rates of both of the algorithms under linear local observation model and minimal technical assumptions. We numerically validate our algorithm through simulation of both state estimation and tracking problems.  ( 2 min )
    TTSWING: a Dataset for Table Tennis Swing Analysis. (arXiv:2306.17550v1 [cs.LG])
    We introduce TTSWING, a novel dataset designed for table tennis swing analysis. This dataset comprises comprehensive swing information obtained through 9-axis sensors integrated into custom-made racket grips, accompanied by anonymized demographic data of the players. We detail the data collection and annotation procedures. Furthermore, we conduct pilot studies utilizing diverse machine learning models for swing analysis. TTSWING holds tremendous potential to facilitate innovative research in table tennis analysis and is a valuable resource for the scientific community. We release the dataset and experimental codes at https://github.com/DEPhantom/TTSWING.
    Towards Zero-Shot Scale-Aware Monocular Depth Estimation. (arXiv:2306.17253v1 [cs.CV])
    Monocular depth estimation is scale-ambiguous, and thus requires scale supervision to produce metric predictions. Even so, the resulting models will be geometry-specific, with learned scales that cannot be directly transferred across domains. Because of that, recent works focus instead on relative depth, eschewing scale in favor of improved up-to-scale zero-shot transfer. In this work we introduce ZeroDepth, a novel monocular depth estimation framework capable of predicting metric scale for arbitrary test images from different domains and camera parameters. This is achieved by (i) the use of input-level geometric embeddings that enable the network to learn a scale prior over objects; and (ii) decoupling the encoder and decoder stages, via a variational latent representation that is conditioned on single frame information. We evaluated ZeroDepth targeting both outdoor (KITTI, DDAD, nuScenes) and indoor (NYUv2) benchmarks, and achieved a new state-of-the-art in both settings using the same pre-trained model, outperforming methods that train on in-domain data and require test-time scaling to produce metric estimates.  ( 2 min )
    Residual Feature Pyramid Network for Enhancement of Vascular Patterns. (arXiv:2306.17200v1 [eess.IV])
    The accuracy of finger vein recognition systems gets degraded due to low and uneven contrast between veins and surroundings, often resulting in poor detection of vein patterns. We propose a finger-vein enhancement technique, ResFPN (Residual Feature Pyramid Network), as a generic preprocessing method agnostic to the recognition pipeline. A bottom-up pyramidal architecture using the novel Structure Detection block (SDBlock) facilitates extraction of veins of varied widths. Using a feature aggregation module (FAM), we combine these vein-structures, and train the proposed ResFPN for detection of veins across scales. With enhanced presentations, our experiments indicate a reduction upto 5% in the average recognition errors for commonly used recognition pipeline over two publicly available datasets. These improvements are persistent even in cross-dataset scenario where the dataset used to train the ResFPN is different from the one used for recognition.  ( 2 min )
    Scattering Spectra Models for Physics. (arXiv:2306.17210v1 [physics.data-an])
    Physicists routinely need probabilistic models for a number of tasks such as parameter inference or the generation of new realizations of a field. Establishing such models for highly non-Gaussian fields is a challenge, especially when the number of samples is limited. In this paper, we introduce scattering spectra models for stationary fields and we show that they provide accurate and robust statistical descriptions of a wide range of fields encountered in physics. These models are based on covariances of scattering coefficients, i.e. wavelet decomposition of a field coupled with a point-wise modulus. After introducing useful dimension reductions taking advantage of the regularity of a field under rotation and scaling, we validate these models on various multi-scale physical fields and demonstrate that they reproduce standard statistics, including spatial moments up to 4th order. These scattering spectra provide us with a low-dimensional structured representation that captures key properties encountered in a wide range of physical fields. These generic models can be used for data exploration, classification, parameter inference, symmetry detection, and component separation.  ( 2 min )
    An end-to-end framework for gene expression classification by integrating a background knowledge graph: application to cancer prognosis prediction. (arXiv:2306.17202v1 [q-bio.QM])
    Biological data may be separated into primary data, such as gene expression, and secondary data, such as pathways and protein-protein interactions. Methods using secondary data to enhance the analysis of primary data are promising, because secondary data have background information that is not included in primary data. In this study, we proposed an end-to-end framework to integrally handle secondary data to construct a classification model for primary data. We applied this framework to cancer prognosis prediction using gene expression data and a biological network. Cross-validation results indicated that our model achieved higher accuracy compared with a deep neural network model without background biological network information. Experiments conducted in patient groups by cancer type showed improvement in ROC-area under the curve for many groups. Visualizations of high accuracy cancer types identified contributing genes and pathways by enrichment analysis. Known biomarkers and novel biomarker candidates were identified through these experiments.  ( 2 min )
    Landmark Guided Active Exploration with Stable Low-level Policy Learning. (arXiv:2306.17484v1 [cs.LG])
    Goal-conditioned hierarchical reinforcement learning (GCHRL) decomposes long-horizon tasks into sub-tasks through a hierarchical framework and it has demonstrated promising results across a variety of domains. However, the high-level policy's action space is often excessively large, presenting a significant challenge to effective exploration and resulting in potentially inefficient training. Moreover, the dynamic variability of the low-level policy introduces non-stationarity to the high-level state transition function, significantly impeding the learning of the high-level policy. In this paper, we design a measure of prospect for subgoals by planning in the goal space based on the goal-conditioned value function. Building upon the measure of prospect, we propose a landmark-guided exploration strategy by integrating the measures of prospect and novelty which aims to guide the agent to explore efficiently and improve sample efficiency. To address the non-stationarity arising from the dynamic changes of the low-level policy, we apply a state-specific regularization to the learning of low-level policy, which facilitates stable learning of the hierarchical policy. The experimental results demonstrate that our proposed exploration strategy significantly outperforms the baseline methods across multiple tasks.
    Scalable tensor methods for nonuniform hypergraphs. (arXiv:2306.17825v1 [math.NA])
    While multilinear algebra appears natural for studying the multiway interactions modeled by hypergraphs, tensor methods for general hypergraphs have been stymied by theoretical and practical barriers. A recently proposed adjacency tensor is applicable to nonuniform hypergraphs, but is prohibitively costly to form and analyze in practice. We develop tensor times same vector (TTSV) algorithms for this tensor which improve complexity from $O(n^r)$ to a low-degree polynomial in $r$, where $n$ is the number of vertices and $r$ is the maximum hyperedge size. Our algorithms are implicit, avoiding formation of the order $r$ adjacency tensor. We demonstrate the flexibility and utility of our approach in practice by developing tensor-based hypergraph centrality and clustering algorithms. We also show these tensor measures offer complementary information to analogous graph-reduction approaches on data, and are also able to detect higher-order structure that many existing matrix-based approaches provably cannot.
    Why can neural language models solve next-word prediction? A mathematical perspective. (arXiv:2306.17184v1 [cs.CL])
    Recently, deep learning has revolutionized the field of natural language processing, with neural language models proving to be very effective for next-word prediction. However, a rigorous theoretical explanation for their success in the context of formal language theory has not yet been developed, as it is unclear why neural language models can learn the combinatorial rules that govern the next-word prediction task. In this paper, we study a class of formal languages that can be used to model real-world examples of English sentences. We construct neural language models can solve the next-word prediction task in this context with zero error. Our proof highlights the different roles of the embedding layer and the fully connected component within the neural language model.  ( 2 min )
    Resetting the Optimizer in Deep RL: An Empirical Study. (arXiv:2306.17833v1 [cs.LG])
    We focus on the task of approximating the optimal value function in deep reinforcement learning. This iterative process is comprised of approximately solving a sequence of optimization problems where the objective function can change per iteration. The common approach to solving the problem is to employ modern variants of the stochastic gradient descent algorithm such as Adam. These optimizers maintain their own internal parameters such as estimates of the first and the second moment of the gradient, and update these parameters over time. Therefore, information obtained in previous iterations is being used to solve the optimization problem in the current iteration. We hypothesize that this can contaminate the internal parameters of the employed optimizer in situations where the optimization landscape of the previous iterations is quite different from the current iteration. To hedge against this effect, a simple idea is to reset the internal parameters of the optimizer when starting a new iteration. We empirically investigate this resetting strategy by employing various optimizers in conjunction with the Rainbow algorithm. We demonstrate that this simple modification unleashes the true potential of modern optimizers, and significantly improves the performance of deep RL on the Atari benchmark.
    Federated Object Detection for Quality Inspection in Shared Production. (arXiv:2306.17645v1 [cs.LG])
    Federated learning (FL) has emerged as a promising approach for training machine learning models on decentralized data without compromising data privacy. In this paper, we propose a FL algorithm for object detection in quality inspection tasks using YOLOv5 as the object detection algorithm and Federated Averaging (FedAvg) as the FL algorithm. We apply this approach to a manufacturing use-case where multiple factories/clients contribute data for training a global object detection model while preserving data privacy on a non-IID dataset. Our experiments demonstrate that our FL approach achieves better generalization performance on the overall clients' test dataset and generates improved bounding boxes around the objects compared to models trained using local clients' datasets. This work showcases the potential of FL for quality inspection tasks in the manufacturing industry and provides valuable insights into the performance and feasibility of utilizing YOLOv5 and FedAvg for federated object detection.
    Practical and Asymptotically Exact Conditional Sampling in Diffusion Models. (arXiv:2306.17775v1 [stat.ML])
    Diffusion models have been successful on a range of conditional generation tasks including molecular design and text-to-image generation. However, these achievements have primarily depended on task-specific conditional training or error-prone heuristic approximations. Ideally, a conditional generation method should provide exact samples for a broad range of conditional distributions without requiring task-specific training. To this end, we introduce the Twisted Diffusion Sampler, or TDS. TDS is a sequential Monte Carlo (SMC) algorithm that targets the conditional distributions of diffusion models. The main idea is to use twisting, an SMC technique that enjoys good computational efficiency, to incorporate heuristic approximations without compromising asymptotic exactness. We first find in simulation and on MNIST image inpainting and class-conditional generation tasks that TDS provides a computational statistical trade-off, yielding more accurate approximations with many particles but with empirical improvements over heuristics with as few as two particles. We then turn to motif-scaffolding, a core task in protein design, using a TDS extension to Riemannian diffusion models. On benchmark test cases, TDS allows flexible conditioning criteria and often outperforms the state of the art.
    Improving Federated Aggregation with Deep Unfolding Networks. (arXiv:2306.17362v1 [cs.LG])
    The performance of Federated learning (FL) is negatively affected by device differences and statistical characteristics between participating clients. To address this issue, we introduce a deep unfolding network (DUN)-based technique that learns adaptive weights that unbiasedly ameliorate the adverse impacts of heterogeneity. The proposed method demonstrates impressive accuracy and quality-aware aggregation. Furthermore, it evaluated the best-weighted normalization approach to define less computational power on the aggregation method. The numerical experiments in this study demonstrate the effectiveness of this approach and provide insights into the interpretability of the unbiased weights learned. By incorporating unbiased weights into the model, the proposed approach effectively addresses quality-aware aggregation under the heterogeneity of the participating clients and the FL environment. Codes and details are \href{https://github.com/shanikairoshi/Improved_DUN_basedFL_Aggregation}{here}.
    Algorithms for bounding contribution for histogram estimation under user-level privacy. (arXiv:2206.03008v2 [cs.LG] UPDATED)
    We study the problem of histogram estimation under user-level differential privacy, where the goal is to preserve the privacy of all entries of any single user. We consider the heterogeneous scenario where the quantity of data can be different for each user. In this scenario, the amount of noise injected into the histogram to obtain differential privacy is proportional to the maximum user contribution, which can be amplified by few outliers. One approach to circumvent this would be to bound (or limit) the contribution of each user to the histogram. However, if users are limited to small contributions, a significant amount of data will be discarded. In this work, we propose algorithms to choose the best user contribution bound for histogram estimation under both bounded and unbounded domain settings. When the size of the domain is bounded, we propose a user contribution bounding strategy that almost achieves a two-approximation with respect to the best contribution bound in hindsight. For unbounded domain histogram estimation, we propose an algorithm that is logarithmic-approximation with respect to the best contribution bound in hindsight. This result holds without any distribution assumptions on the data. Experiments on both real and synthetic datasets verify our theoretical findings and demonstrate the effectiveness of our algorithms. We also show that clipping bias introduced by bounding user contribution may be reduced under mild distribution assumptions, which can be of independent interest.
    TemperatureGAN: Generative Modeling of Regional Atmospheric Temperatures. (arXiv:2306.17248v1 [cs.LG])
    Stochastic generators are useful for estimating climate impacts on various sectors. Projecting climate risk in various sectors, e.g. energy systems, requires generators that are accurate (statistical resemblance to ground-truth), reliable (do not produce erroneous examples), and efficient. Leveraging data from the North American Land Data Assimilation System, we introduce TemperatureGAN, a Generative Adversarial Network conditioned on months, locations, and time periods, to generate 2m above ground atmospheric temperatures at an hourly resolution. We propose evaluation methods and metrics to measure the quality of generated samples. We show that TemperatureGAN produces high-fidelity examples with good spatial representation and temporal dynamics consistent with known diurnal cycles.
    Prediction of COVID-19 Patients' Emergency Room Revisit using Multi-Source Transfer Learning. (arXiv:2306.17257v1 [cs.LG])
    The coronavirus disease 2019 (COVID-19) has led to a global pandemic of significant severity. In addition to its high level of contagiousness, COVID-19 can have a heterogeneous clinical course, ranging from asymptomatic carriers to severe and potentially life-threatening health complications. Many patients have to revisit the emergency room (ER) within a short time after discharge, which significantly increases the workload for medical staff. Early identification of such patients is crucial for helping physicians focus on treating life-threatening cases. In this study, we obtained Electronic Health Records (EHRs) of 3,210 encounters from 13 affiliated ERs within the University of Pittsburgh Medical Center between March 2020 and January 2021. We leveraged a Natural Language Processing technique, ScispaCy, to extract clinical concepts and used the 1001 most frequent concepts to develop 7-day revisit models for COVID-19 patients in ERs. The research data we collected from 13 ERs may have distributional differences that could affect the model development. To address this issue, we employed a classic deep transfer learning method called the Domain Adversarial Neural Network (DANN) and evaluated different modeling strategies, including the Multi-DANN algorithm, the Single-DANN algorithm, and three baseline methods. Results showed that the Multi-DANN models outperformed the Single-DANN models and baseline models in predicting revisits of COVID-19 patients to the ER within 7 days after discharge. Notably, the Multi-DANN strategy effectively addressed the heterogeneity among multiple source domains and improved the adaptation of source data to the target domain. Moreover, the high performance of Multi-DANN models indicates that EHRs are informative for developing a prediction model to identify COVID-19 patients who are very likely to revisit an ER within 7 days after discharge.  ( 3 min )
    Design of Induction Machines using Reinforcement Learning. (arXiv:2306.17626v1 [cs.LG])
    The design of induction machine is a challenging task due to different electromagnetic and thermal constraints. Quick estimation of machine's dimensions is important in the sales tool to provide quick quotations to customers based on specific requirements. The key part of this process is to select different design parameters like length, diameter, tooth tip height and winding turns to achieve certain torque, current and temperature of the machine. Electrical machine designers, with their experience know how to alter different machine design parameters to achieve a customer specific operation requirements. We propose a reinforcement learning algorithm to design a customised induction motor. The neural network model is trained off-line by simulating different instances of of electrical machine design game with a reward or penalty function when a good or bad design choice is made. The results demonstrate that the suggested method automates electrical machine design without applying any human engineering knowledge.
    The power of motifs as inductive bias for learning molecular distributions. (arXiv:2306.17246v1 [cs.LG])
    Machine learning for molecules holds great potential for efficiently exploring the vast chemical space and thus streamlining the drug discovery process by facilitating the design of new therapeutic molecules. Deep generative models have shown promising results for molecule generation, but the benefits of specific inductive biases for learning distributions over small graphs are unclear. Our study aims to investigate the impact of subgraph structures and vocabulary design on distribution learning, using small drug molecules as a case study. To this end, we introduce Subcover, a new subgraph-based fragmentation scheme, and evaluate it through a two-step variational auto-encoder. Our results show that Subcover's improved identification of chemically meaningful subgraphs leads to a relative improvement of the FCD score by 30%, outperforming previous methods. Our findings highlight the potential of Subcover to enhance the performance and scalability of existing methods, contributing to the advancement of drug discovery.
    Limits of Machine Learning for Automatic Vulnerability Detection. (arXiv:2306.17193v1 [cs.CR])
    Recent results of machine learning for automatic vulnerability detection have been very promising indeed: Given only the source code of a function $f$, models trained by machine learning techniques can decide if $f$ contains a security flaw with up to 70% accuracy. But how do we know that these results are general and not specific to the datasets? To study this question, researchers proposed to amplify the testing set by injecting semantic preserving changes and found that the model's accuracy significantly drops. In other words, the model uses some unrelated features during classification. In order to increase the robustness of the model, researchers proposed to train on amplified training data, and indeed model accuracy increased to previous levels. In this paper, we replicate and continue this investigation, and provide an actionable model benchmarking methodology to help researchers better evaluate advances in machine learning for vulnerability detection. Specifically, we propose (i) a cross validation algorithm, where a semantic preserving transformation is applied during the amplification of either the training set or the testing set, and (ii) the amplification of the testing set with code snippets where the vulnerabilities are fixed. Using 11 transformations, 3 ML techniques, and 2 datasets, we find that the improved robustness only applies to the specific transformations used during training data amplification. In other words, the robustified models still rely on unrelated features for predicting the vulnerabilities in the testing data. Additionally, we find that the trained models are unable to generalize to the modified setting which requires to distinguish vulnerable functions from their patches.  ( 3 min )
    Diff-Foley: Synchronized Video-to-Audio Synthesis with Latent Diffusion Models. (arXiv:2306.17203v1 [cs.SD])
    The Video-to-Audio (V2A) model has recently gained attention for its practical application in generating audio directly from silent videos, particularly in video/film production. However, previous methods in V2A have limited generation quality in terms of temporal synchronization and audio-visual relevance. We present Diff-Foley, a synchronized Video-to-Audio synthesis method with a latent diffusion model (LDM) that generates high-quality audio with improved synchronization and audio-visual relevance. We adopt contrastive audio-visual pretraining (CAVP) to learn more temporally and semantically aligned features, then train an LDM with CAVP-aligned visual features on spectrogram latent space. The CAVP-aligned features enable LDM to capture the subtler audio-visual correlation via a cross-attention module. We further significantly improve sample quality with `double guidance'. Diff-Foley achieves state-of-the-art V2A performance on current large scale V2A dataset. Furthermore, we demonstrate Diff-Foley practical applicability and generalization capabilities via downstream finetuning. Project Page: see https://diff-foley.github.io/  ( 2 min )
    On the Exploitability of Instruction Tuning. (arXiv:2306.17194v1 [cs.CR])
    Instruction tuning is an effective technique to align large language models (LLMs) with human intents. In this work, we investigate how an adversary can exploit instruction tuning by injecting specific instruction-following examples into the training data that intentionally changes the model's behavior. For example, an adversary can achieve content injection by injecting training examples that mention target content and eliciting such behavior from downstream models. To achieve this goal, we propose \textit{AutoPoison}, an automated data poisoning pipeline. It naturally and coherently incorporates versatile attack goals into poisoned data with the help of an oracle LLM. We showcase two example attacks: content injection and over-refusal attacks, each aiming to induce a specific exploitable behavior. We quantify and benchmark the strength and the stealthiness of our data poisoning scheme. Our results show that AutoPoison allows an adversary to change a model's behavior by poisoning only a small fraction of data while maintaining a high level of stealthiness in the poisoned examples. We hope our work sheds light on how data quality affects the behavior of instruction-tuned models and raises awareness of the importance of data quality for responsible deployments of LLMs. Code is available at \url{https://github.com/azshue/AutoPoison}.
    Classification and Explanation of Distributed Denial-of-Service (DDoS) Attack Detection using Machine Learning and Shapley Additive Explanation (SHAP) Methods. (arXiv:2306.17190v1 [cs.CR])
    DDoS attacks involve overwhelming a target system with a large number of requests or traffic from multiple sources, disrupting the normal traffic of a targeted server, service, or network. Distinguishing between legitimate traffic and malicious traffic is a challenging task. It is possible to classify legitimate traffic and malicious traffic and analysis the network traffic by using machine learning and deep learning techniques. However, an inter-model explanation implemented to classify a traffic flow whether is benign or malicious is an important investigation of the inner working theory of the model to increase the trustworthiness of the model. Explainable Artificial Intelligence (XAI) can explain the decision-making of the machine learning models that can be classified and identify DDoS traffic. In this context, we proposed a framework that can not only classify legitimate traffic and malicious traffic of DDoS attacks but also use SHAP to explain the decision-making of the classifier model. To address this concern, we first adopt feature selection techniques to select the top 20 important features based on feature importance techniques (e.g., XGB-based SHAP feature importance). Following that, the Multi-layer Perceptron Network (MLP) part of our proposed model uses the optimized features of the DDoS attack dataset as inputs to classify legitimate and malicious traffic. We perform extensive experiments with all features and selected features. The evaluation results show that the model performance with selected features achieves above 99\% accuracy. Finally, to provide interpretability, XAI can be adopted to explain the model performance between the prediction results and features based on global and local explanations by SHAP, which can better explain the results achieved by our proposed framework.  ( 3 min )
    Probabilistic Constraint for Safety-Critical Reinforcement Learning. (arXiv:2306.17279v1 [cs.LG])
    In this paper, we consider the problem of learning safe policies for probabilistic-constrained reinforcement learning (RL). Specifically, a safe policy or controller is one that, with high probability, maintains the trajectory of the agent in a given safe set. We establish a connection between this probabilistic-constrained setting and the cumulative-constrained formulation that is frequently explored in the existing literature. We provide theoretical bounds elucidating that the probabilistic-constrained setting offers a better trade-off in terms of optimality and safety (constraint satisfaction). The challenge encountered when dealing with the probabilistic constraints, as explored in this work, arises from the absence of explicit expressions for their gradients. Our prior work provides such an explicit gradient expression for probabilistic constraints which we term Safe Policy Gradient-REINFORCE (SPG-REINFORCE). In this work, we provide an improved gradient SPG-Actor-Critic that leads to a lower variance than SPG-REINFORCE, which is substantiated by our theoretical results. A noteworthy aspect of both SPGs is their inherent algorithm independence, rendering them versatile for application across a range of policy-based algorithms. Furthermore, we propose a Safe Primal-Dual algorithm that can leverage both SPGs to learn safe policies. It is subsequently followed by theoretical analyses that encompass the convergence of the algorithm, as well as the near-optimality and feasibility on average. In addition, we test the proposed approaches by a series of empirical experiments. These experiments aim to examine and analyze the inherent trade-offs between the optimality and safety, and serve to substantiate the efficacy of two SPGs, as well as our theoretical contributions.  ( 3 min )
    Suffering Toasters. (arXiv:2306.17258v1 [cs.AI])
    A widely accepted definition of intelligence in the context of Artificial Intelligence (AI) still eludes us. Due to our exceedingly rapid development of AI paradigms, architectures, and tools, the prospect of naturally arising AI consciousness seems more likely than ever. In this paper, we claim that all current intelligence tests are insufficient to point to the existence or lack of intelligence \textbf{as humans intuitively perceive it}. We draw from ideas in the philosophy of science, psychology, and other areas of research to provide a clearer definition of the problems of artificial intelligence, self-awareness, and agency. We furthermore propose a new heuristic approach to test for artificial self-awareness and outline a possible implementation. Finally, we discuss some of the questions that arise from this new heuristic, be they philosophical or implementation-oriented.  ( 2 min )
    Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacings. (arXiv:2306.17670v1 [cs.NE])
    Spiking Neural Networks (SNNs) are a promising research direction for building power-efficient information processing systems, especially for temporal tasks such as speech recognition. In SNNs, delays refer to the time needed for one spike to travel from one neuron to another. These delays matter because they influence the spike arrival times, and it is well-known that spiking neurons respond more strongly to coincident input spikes. More formally, it has been shown theoretically that plastic delays greatly increase the expressivity in SNNs. Yet, efficient algorithms to learn these delays have been lacking. Here, we propose a new discrete-time algorithm that addresses this issue in deep feedforward SNNs using backpropagation, in an offline manner. To simulate delays between consecutive layers, we use 1D convolutions across time. The kernels contain only a few non-zero weights - one per synapse - whose positions correspond to the delays. These positions are learned together with the weights using the recently proposed Dilated Convolution with Learnable Spacings (DCLS). We evaluated our method on the Spiking Heidelberg Dataset (SHD) and the Spiking Speech Commands (SSC) benchmarks, which require detecting temporal patterns. We used feedforward SNNs with two hidden fully connected layers. We showed that fixed random delays help, and that learning them helps even more. Furthermore, our method outperformed the state-of-the-art in both SHD and SSC without using recurrent connections and with substantially fewer parameters. Our work demonstrates the potential of delay learning in developing accurate and precise models for temporal data processing. Our code is based on PyTorch / SpikingJelly and available at: https://github.com/Thvnvtos/SNN-delays
  • Open

    Improving Expert Predictions with Conformal Prediction. (arXiv:2201.12006v5 [cs.LG] UPDATED)
    Automated decision support systems promise to help human experts solve multiclass classification tasks more efficiently and accurately. However, existing systems typically require experts to understand when to cede agency to the system or when to exercise their own agency. Otherwise, the experts may be better off solving the classification tasks on their own. In this work, we develop an automated decision support system that, by design, does not require experts to understand when to trust the system to improve performance. Rather than providing (single) label predictions and letting experts decide when to trust these predictions, our system provides sets of label predictions constructed using conformal prediction$\unicode{x2014}$prediction sets$\unicode{x2014}$and forcefully asks experts to predict labels from these sets. By using conformal prediction, our system can precisely trade-off the probability that the true label is not in the prediction set, which determines how frequently our system will mislead the experts, and the size of the prediction set, which determines the difficulty of the classification task the experts need to solve using our system. In addition, we develop an efficient and near-optimal search method to find the conformal predictor under which the experts benefit the most from using our system. Simulation experiments using synthetic and real expert predictions demonstrate that our system may help experts make more accurate predictions and is robust to the accuracy of the classifier the conformal predictor relies on.
    The Bayesian Learning Rule. (arXiv:2107.04562v3 [stat.ML] UPDATED)
    We show that many machine-learning algorithms are specific instances of a single algorithm called the Bayesian learning rule. The rule, derived from Bayesian principles, yields a wide-range of algorithms from fields such as optimization, deep learning, and graphical models. This includes classical algorithms such as ridge regression, Newton's method, and Kalman filter, as well as modern deep-learning algorithms such as stochastic-gradient descent, RMSprop, and Dropout. The key idea in deriving such algorithms is to approximate the posterior using candidate distributions estimated by using natural gradients. Different candidate distributions result in different algorithms and further approximations to natural gradients give rise to variants of those algorithms. Our work not only unifies, generalizes, and improves existing algorithms, but also helps us design new ones.  ( 2 min )
    Selecting Robust Features for Machine Learning Applications using Multidata Causal Discovery. (arXiv:2304.05294v5 [stat.ML] UPDATED)
    Robust feature selection is vital for creating reliable and interpretable Machine Learning (ML) models. When designing statistical prediction models in cases where domain knowledge is limited and underlying interactions are unknown, choosing the optimal set of features is often difficult. To mitigate this issue, we introduce a Multidata (M) causal feature selection approach that simultaneously processes an ensemble of time series datasets and produces a single set of causal drivers. This approach uses the causal discovery algorithms PC1 or PCMCI that are implemented in the Tigramite Python package. These algorithms utilize conditional independence tests to infer parts of the causal graph. Our causal feature selection approach filters out causally-spurious links before passing the remaining causal features as inputs to ML models (Multiple linear regression, Random Forest) that predict the targets. We apply our framework to the statistical intensity prediction of Western Pacific Tropical Cyclones (TC), for which it is often difficult to accurately choose drivers and their dimensionality reduction (time lags, vertical levels, and area-averaging). Using more stringent significance thresholds in the conditional independence tests helps eliminate spurious causal relationships, thus helping the ML model generalize better to unseen TC cases. M-PC1 with a reduced number of features outperforms M-PCMCI, non-causal ML, and other feature selection methods (lagged correlation, random), even slightly outperforming feature selection based on eXplainable Artificial Intelligence. The optimal causal drivers obtained from our causal feature selection help improve our understanding of underlying relationships and suggest new potential drivers of TC intensification.  ( 3 min )
    The most likely common cause. (arXiv:2306.17557v1 [physics.data-an])
    The common cause principle for two random variables $A$ and $B$ is examined in the case of causal insufficiency, when their common cause $C$ is known to exist, but only the joint probability of $A$ and $B$ is observed. As a result, $C$ cannot be uniquely identified (the latent confounder problem). We show that the generalized maximum likelihood method can be applied to this situation and allows identification of $C$ that is consistent with the common cause principle. It closely relates to the maximum entropy principle. Investigation of the two binary symmetric variables reveals a non-analytic behavior of conditional probabilities reminiscent of a second-order phase transition. This occurs during the transition from correlation to anti-correlation in the observed probability distribution. The relation between the generalized likelihood approach and alternative methods, such as predictive likelihood and the minimum common cause entropy, is discussed. The consideration of the common cause for three observed variables (and one hidden cause) uncovers causal structures that defy representation through directed acyclic graphs with the Markov condition.  ( 2 min )
    Limits of Model Selection under Transfer Learning. (arXiv:2305.00152v3 [stat.ML] UPDATED)
    Theoretical studies on transfer learning or domain adaptation have so far focused on situations with a known hypothesis class or model; however in practice, some amount of model selection is usually involved, often appearing under the umbrella term of hyperparameter-tuning: for example, one may think of the problem of tuning for the right neural network architecture towards a target task, while leveraging data from a related source task. Now, in addition to the usual tradeoffs on approximation vs estimation errors involved in model selection, this problem brings in a new complexity term, namely, the transfer distance between source and target distributions, which is known to vary with the choice of hypothesis class. We present a first study of this problem, focusing on classification; in particular, the analysis reveals some remarkable phenomena: adaptive rates, i.e., those achievable with no distributional information, can be arbitrarily slower than oracle rates, i.e., when given knowledge on distances.
    Efficient uniform approximation using Random Vector Functional Link networks. (arXiv:2306.17501v1 [stat.ML])
    A Random Vector Functional Link (RVFL) network is a depth-2 neural network with random inner weights and biases. As only the outer weights of such architectures need to be learned, the learning process boils down to a linear optimization task, allowing one to sidestep the pitfalls of nonconvex optimization problems. In this paper, we prove that an RVFL with ReLU activation functions can approximate Lipschitz continuous functions provided its hidden layer is exponentially wide in the input dimension. Although it has been established before that such approximation can be achieved in $L_2$ sense, we prove it for $L_\infty$ approximation error and Gaussian inner weights. To the best of our knowledge, our result is the first of this kind. We give a nonasymptotic lower bound for the number of hidden layer nodes, depending on, among other things, the Lipschitz constant of the target function, the desired accuracy, and the input dimension. Our method of proof is rooted in probability theory and harmonic analysis.
    On the Existence of a Complexity in Fixed Budget Bandit Identification. (arXiv:2303.09468v2 [stat.ML] UPDATED)
    In fixed budget bandit identification, an algorithm sequentially observes samples from several distributions up to a given final time. It then answers a query about the set of distributions. A good algorithm will have a small probability of error. While that probability decreases exponentially with the final time, the best attainable rate is not known precisely for most identification tasks. We show that if a fixed budget task admits a complexity, defined as a lower bound on the probability of error which is attained by the same algorithm on all bandit problems, then that complexity is determined by the best non-adaptive sampling procedure for that problem. We show that there is no such complexity for several fixed budget identification tasks including Bernoulli best arm identification with two arms: there is no single algorithm that attains everywhere the best possible rate.
    Practical and Asymptotically Exact Conditional Sampling in Diffusion Models. (arXiv:2306.17775v1 [stat.ML])
    Diffusion models have been successful on a range of conditional generation tasks including molecular design and text-to-image generation. However, these achievements have primarily depended on task-specific conditional training or error-prone heuristic approximations. Ideally, a conditional generation method should provide exact samples for a broad range of conditional distributions without requiring task-specific training. To this end, we introduce the Twisted Diffusion Sampler, or TDS. TDS is a sequential Monte Carlo (SMC) algorithm that targets the conditional distributions of diffusion models. The main idea is to use twisting, an SMC technique that enjoys good computational efficiency, to incorporate heuristic approximations without compromising asymptotic exactness. We first find in simulation and on MNIST image inpainting and class-conditional generation tasks that TDS provides a computational statistical trade-off, yielding more accurate approximations with many particles but with empirical improvements over heuristics with as few as two particles. We then turn to motif-scaffolding, a core task in protein design, using a TDS extension to Riemannian diffusion models. On benchmark test cases, TDS allows flexible conditioning criteria and often outperforms the state of the art.
    Online Learning with Set-Valued Feedback. (arXiv:2306.06247v2 [cs.LG] UPDATED)
    We study a variant of online multiclass classification where the learner predicts a single label but receives a \textit{set of labels} as feedback. In this model, the learner is penalized for not outputting a label contained in the revealed set. We show that unlike online multiclass learning with single-label feedback, deterministic and randomized online learnability are \textit{not equivalent} even in the realizable setting with set-valued feedback. Accordingly, we give two new combinatorial dimensions, named the Set Littlestone and Measure Shattering dimension, that tightly characterize deterministic and randomized online learnability respectively in the realizable setting. In addition, we show that the Measure Shattering dimension tightly characterizes online learnability in the agnostic setting. Finally, we show that practical learning settings like online multilabel ranking, online multilabel classification, and online interval learning are specific instances of our general framework.
    A Quantitative Functional Central Limit Theorem for Shallow Neural Networks. (arXiv:2306.16932v1 [math.PR] CROSS LISTED)
    We prove a Quantitative Functional Central Limit Theorem for one-hidden-layer neural networks with generic activation function. The rates of convergence that we establish depend heavily on the smoothness of the activation function, and they range from logarithmic in non-differentiable cases such as the Relu to $\sqrt{n}$ for very regular activations. Our main tools are functional versions of the Stein-Malliavin approach; in particular, we exploit heavily a quantitative functional central limit theorem which has been recently established by Bourguin and Campese (2020).
    Generalized Time Warping Invariant Dictionary Learning for Time Series Classification and Clustering. (arXiv:2306.17690v1 [stat.ML])
    Dictionary learning is an effective tool for pattern recognition and classification of time series data. Among various dictionary learning techniques, the dynamic time warping (DTW) is commonly used for dealing with temporal delays, scaling, transformation, and many other kinds of temporal misalignments issues. However, the DTW suffers overfitting or information loss due to its discrete nature in aligning time series data. To address this issue, we propose a generalized time warping invariant dictionary learning algorithm in this paper. Our approach features a generalized time warping operator, which consists of linear combinations of continuous basis functions for facilitating continuous temporal warping. The integration of the proposed operator and the dictionary learning is formulated as an optimization problem, where the block coordinate descent method is employed to jointly optimize warping paths, dictionaries, and sparseness coefficients. The optimized results are then used as hyperspace distance measures to feed classification and clustering algorithms. The superiority of the proposed method in terms of dictionary learning, classification, and clustering is validated through ten sets of public datasets in comparing with various benchmark methods.
    Neural Characteristic Activation Value Analysis for Improved ReLU Network Feature Learning. (arXiv:2305.15912v2 [cs.LG] UPDATED)
    We examine the characteristic activation values of individual ReLU units in neural networks. We refer to the corresponding set for such characteristic activation values in the input space as the characteristic activation set of a ReLU unit. We draw an explicit connection between the characteristic activation set and learned features in ReLU networks. This connection leads to new insights into why various neural network normalization techniques used in modern deep learning architectures regularize and stabilize SGD optimization. Utilizing these insights, we propose a geometric approach to parameterize ReLU networks for improved feature learning. We empirically verify its usefulness with less carefully chosen initialization schemes and larger learning rates. We report improved optimization stability, faster convergence speed, and better generalization performance.  ( 2 min )
    Approximating Probability Distributions by using Wasserstein Generative Adversarial Networks. (arXiv:2103.10060v4 [cs.LG] UPDATED)
    Studied here are Wasserstein generative adversarial networks (WGANs) with GroupSort neural networks as their discriminators. It is shown that the error bound of the approximation for the target distribution depends on the width and depth (capacity) of the generators and discriminators and the number of samples in training. A quantified generalization bound is established for the Wasserstein distance between the generated and target distributions. According to the theoretical results, WGANs have a higher requirement for the capacity of discriminators than that of generators, which is consistent with some existing results. More importantly, the results with overly deep and wide (high-capacity) generators may be worse than those with low-capacity generators if discriminators are insufficiently strong. Numerical results obtained using Swiss roll and MNIST datasets confirm the theoretical results.
    Off-Policy Evaluation with Out-of-Sample Guarantees. (arXiv:2301.08649v3 [stat.ML] UPDATED)
    We consider the problem of evaluating the performance of a decision policy using past observational data. The outcome of a policy is measured in terms of a loss (aka. disutility or negative reward) and the main problem is making valid inferences about its out-of-sample loss when the past data was observed under a different and possibly unknown policy. Using a sample-splitting method, we show that it is possible to draw such inferences with finite-sample coverage guarantees about the entire loss distribution, rather than just its mean. Importantly, the method takes into account model misspecifications of the past policy - including unmeasured confounding. The evaluation method can be used to certify the performance of a policy using observational data under a specified range of credible model assumptions.
    High Dimensional Data Enrichment: Interpretable, Fast, and Data-Efficient. (arXiv:1806.04047v4 [stat.ML] UPDATED)
    We consider the problem of multi-task learning in the high dimensional setting. In particular, we introduce an estimator and investigate its statistical and computational properties for the problem of multiple connected linear regressions known as Data Enrichment/Sharing. The between-tasks connections are captured by a cross-tasks \emph{common parameter}, which gets refined by per-task \emph{individual parameters}. Any convex function, e.g., norm, can characterize the structure of both common and individual parameters. We delineate the sample complexity of our estimator and provide a high probability non-asymptotic bound for estimation error of all parameters under a geometric condition. We show that the recovery of the common parameter benefits from \emph{all} of the pooled samples. We propose an iterative estimation algorithm with a geometric convergence rate and supplement our theoretical analysis with experiments on synthetic data. Overall, we present a first thorough statistical and computational analysis of inference in the data-sharing model.  ( 2 min )
    Uncertainty Estimation by Fisher Information-based Evidential Deep Learning. (arXiv:2303.02045v3 [cs.LG] UPDATED)
    Uncertainty estimation is a key factor that makes deep learning reliable in practical applications. Recently proposed evidential neural networks explicitly account for different uncertainties by treating the network's outputs as evidence to parameterize the Dirichlet distribution, and achieve impressive performance in uncertainty estimation. However, for high data uncertainty samples but annotated with the one-hot label, the evidence-learning process for those mislabeled classes is over-penalized and remains hindered. To address this problem, we propose a novel method, Fisher Information-based Evidential Deep Learning ($\mathcal{I}$-EDL). In particular, we introduce Fisher Information Matrix (FIM) to measure the informativeness of evidence carried by each sample, according to which we can dynamically reweight the objective loss terms to make the network more focused on the representation learning of uncertain classes. The generalization ability of our network is further improved by optimizing the PAC-Bayesian bound. As demonstrated empirically, our proposed method consistently outperforms traditional EDL-related algorithms in multiple uncertainty estimation tasks, especially in the more challenging few-shot classification settings.  ( 2 min )
    First-order ANIL learns linear representations despite misspecified latent dimension. (arXiv:2303.01335v2 [cs.LG] UPDATED)
    Due to its empirical success in few-shot classification and reinforcement learning, meta-learning has recently received significant interest. Meta-learning methods leverage data from previous tasks to learn a new task in a sample-efficient manner. In particular, model-agnostic methods look for initialisation points from which gradient descent quickly adapts to any new task. Although it has been empirically suggested that such methods perform well by learning shared representations during pretraining, there is limited theoretical evidence of such behavior. More importantly, it has not been rigorously shown that these methods still learn a shared structure, despite architectural misspecifications. In this direction, this work shows, in the limit of an infinite number of tasks, that first-order ANIL with a linear two-layer network architecture successfully learns linear shared representations. This result even holds with a misspecified network parameterisation; having a width larger than the dimension of the shared representations results in an asymptotically low-rank solution. The learnt solution then yields a good adaptation performance on any new task after a single gradient step. Overall this illustrates how well model-agnostic methods such as first-order ANIL can learn shared representations.  ( 2 min )
    Sufficient-Statistic Memory AMP. (arXiv:2112.15327v4 [cs.IT] UPDATED)
    Approximate message passing (AMP) type algorithms have been widely used in the signal reconstruction of certain large random linear systems. A key feature of the AMP-type algorithms is that their dynamics can be correctly described by state evolution. While state evolution is a useful analytic tool, its convergence is not guaranteed. To solve the convergence problem of the state evolution of AMP-type algorithms in principle, this paper proposes a sufficient-statistic memory AMP (SS-MAMP) algorithm framework under the conditions of right-unitarily invariant sensing matrices, Lipschitz-continuous local processors and the sufficient-statistic constraint (i.e., the current message of each local processor is a sufficient statistic of the signal vector given the current and all preceding messages). We show that the covariance matrices of SS-MAMP are L-banded and convergent, which is an optimal framework (from the local MMSE/LMMSE perspective) for AMP-type algorithms given the Lipschitz-continuous local processors. Given an arbitrary MAMP, we can construct an SS-MAMP by damping, which not only ensures the convergence of the state evolution, but also preserves the orthogonality, i.e., its dynamics can be correctly described by state evolution. As a byproduct, we prove that the Bayes-optimal orthogonal/vector AMP (BO-OAMP/VAMP) is an SS-MAMP. As an example, we construct a sufficient-statistic Bayes-optimal MAMP (SS-BO-MAMP) whose state evolution converges to the minimum (i.e., Bayes-optimal) mean square error (MSE) predicted by replica methods when it has a unique fixed point. In addition, the MSE of SS-BO-MAMP is not worse than the original BO-MAMP. Finally, simulations are provided to support the theoretical results.  ( 3 min )
    GEC: A Unified Framework for Interactive Decision Making in MDP, POMDP, and Beyond. (arXiv:2211.01962v4 [cs.LG] UPDATED)
    We study sample efficient reinforcement learning (RL) under the general framework of interactive decision making, which includes Markov decision process (MDP), partially observable Markov decision process (POMDP), and predictive state representation (PSR) as special cases. Toward finding the minimum assumption that empowers sample efficient learning, we propose a novel complexity measure, generalized eluder coefficient (GEC), which characterizes the fundamental tradeoff between exploration and exploitation in online interactive decision making. In specific, GEC captures the hardness of exploration by comparing the error of predicting the performance of the updated policy with the in-sample training error evaluated on the historical data. We show that RL problems with low GEC form a remarkably rich class, which subsumes low Bellman eluder dimension problems, bilinear class, low witness rank problems, PO-bilinear class, and generalized regular PSR, where generalized regular PSR, a new tractable PSR class identified by us, includes nearly all known tractable POMDPs and PSRs. Furthermore, in terms of algorithm design, we propose a generic posterior sampling algorithm, which can be implemented in both model-free and model-based fashion, under both fully observable and partially observable settings. The proposed algorithm modifies the standard posterior sampling algorithm in two aspects: (i) we use an optimistic prior distribution that biases towards hypotheses with higher values and (ii) a loglikelihood function is set to be the empirical loss evaluated on the historical data, where the choice of loss function supports both model-free and model-based learning. We prove that the proposed algorithm is sample efficient by establishing a sublinear regret upper bound in terms of GEC. In summary, we provide a new and unified understanding of both fully observable and partially observable RL.  ( 3 min )
    A method to integrate and classify normal distributions. (arXiv:2012.14331v9 [stat.ML] UPDATED)
    Univariate and multivariate normal probability distributions are widely used when modeling decisions under uncertainty. Computing the performance of such models requires integrating these distributions over specific domains, which can vary widely across models. Besides some special cases, there exist no general analytical expressions, standard numerical methods or software for these integrals. Here we present mathematical results and open-source software that provide (i) the probability in any domain of a normal in any dimensions with any parameters, (ii) the probability density, cumulative distribution, and inverse cumulative distribution of any function of a normal vector, (iii) the classification errors among any number of normal distributions, the Bayes-optimal discriminability index and relation to the operating characteristic, (iv) dimension reduction and visualizations for such problems, and (v) tests for how reliably these methods may be used on given data. We demonstrate these tools with vision research applications of detecting occluding objects in natural scenes, and detecting camouflage.  ( 3 min )
    Variational principle to regularize machine-learned density functionals: the non-interacting kinetic-energy functional. (arXiv:2306.17587v1 [physics.chem-ph])
    Practical density functional theory (DFT) owes its success to the groundbreaking work of Kohn and Sham that introduced the exact calculation of the non-interacting kinetic energy of the electrons using an auxiliary mean-field system. However, the full power of DFT will not be unleashed until the exact relationship between the electron density and the non-interacting kinetic energy is found. Various attempts have been made to approximate this functional, similar to the exchange--correlation functional, with much less success due to the larger contribution of kinetic energy and its more non-local nature. In this work we propose a new and efficient regularization method to train density functionals based on deep neural networks, with particular interest in the kinetic-energy functional. The method is tested on (effectively) one-dimensional systems, including the hydrogen chain, non-interacting electrons, and atoms of the first two periods, with excellent results. For the atomic systems, the generalizability of the regularization method is demonstrated by training also an exchange--correlation functional, and the contrasting nature of the two functionals is discussed from a machine-learning perspective.  ( 2 min )
    Heterogeneous Distributed Lag Models to Estimate Personalized Effects of Maternal Exposures to Air Pollution. (arXiv:2109.13763v3 [stat.ME] UPDATED)
    Children's health studies support an association between maternal environmental exposures and children's birth outcomes. A common goal is to identify critical windows of susceptibility--periods during gestation with increased association between maternal exposures and a future outcome. The timing of the critical windows and magnitude of the associations are likely heterogeneous across different levels of individual, family, and neighborhood characteristics. Using an administrative Colorado birth cohort we estimate the individualized relationship between weekly exposures to fine particulate matter (PM$_{2.5}$) during gestation and birth weight. To achieve this goal, we propose a statistical learning method combining distributed lag models and Bayesian additive regression trees to estimate critical windows at the individual level and identify characteristics that induce heterogeneity from a high-dimensional set of potential modifying factors. We find evidence of heterogeneity in the PM$_{2.5}$-birth weight relationship, with some mother-child dyads showing a 3 times larger decrease in birth weight for an IQR increase in exposure (5.9 to 8.5 $\mu g/m^3$ PM$_{2.5}$) compared to the population average. Specifically, we find increased susceptibility for non-Hispanic mothers who are either younger, have higher body mass index or lower educational attainment. Our case study is the first precision health study of critical windows.  ( 3 min )
    Scalable method for Bayesian experimental design without integrating over posterior distribution. (arXiv:2306.17615v1 [math.NA])
    We address the computational efficiency in solving the A-optimal Bayesian design of experiments problems for which the observational model is based on partial differential equations and, consequently, is computationally expensive to evaluate. A-optimality is a widely used and easy-to-interpret criterion for the Bayesian design of experiments. The criterion seeks the optimal experiment design by minimizing the expected conditional variance, also known as the expected posterior variance. This work presents a novel likelihood-free method for seeking the A-optimal design of experiments without sampling or integrating the Bayesian posterior distribution. In our approach, the expected conditional variance is obtained via the variance of the conditional expectation using the law of total variance, while we take advantage of the orthogonal projection property to approximate the conditional expectation. Through an asymptotic error estimation, we show that the intractability of the posterior does not affect the performance of our approach. We use an artificial neural network (ANN) to approximate the nonlinear conditional expectation to implement our method. For dealing with continuous experimental design parameters, we integrate the training process of the ANN into minimizing the expected conditional variance. Specifically, we propose a non-local approximation of the conditional expectation and apply transfer learning to reduce the number of evaluations of the observation model. Through numerical experiments, we demonstrate that our method significantly reduces the number of observational model evaluations compared with common importance sampling-based approaches. This reduction is crucial, considering the computationally expensive nature of these models.  ( 3 min )
    The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit. (arXiv:2306.17759v1 [stat.ML])
    In deep learning theory, the covariance matrix of the representations serves as a proxy to examine the network's trainability. Motivated by the success of Transformers, we study the covariance matrix of a modified Softmax-based attention model with skip connections in the proportional limit of infinite-depth-and-width. We show that at initialization the limiting distribution can be described by a stochastic differential equation (SDE) indexed by the depth-to-width ratio. To achieve a well-defined stochastic limit, the Transformer's attention mechanism is modified by centering the Softmax output at identity, and scaling the Softmax logits by a width-dependent temperature parameter. We examine the stability of the network through the corresponding SDE, showing how the scale of both the drift and diffusion can be elegantly controlled with the aid of residual connections. The existence of a stable SDE implies that the covariance structure is well-behaved, even for very large depth and width, thus preventing the notorious issues of rank degeneracy in deep attention models. Finally, we show, through simulations, that the SDE provides a surprisingly good description of the corresponding finite-size model. We coin the name shaped Transformer for these architectural modifications.  ( 2 min )
    A Bayesian Filtering Algorithm for Gaussian Mixture Models. (arXiv:1705.05495v2 [stat.ML] UPDATED)
    A Bayesian filtering algorithm is developed for a class of state-space systems that can be modelled via Gaussian mixtures. In general, the exact solution to this filtering problem involves an exponential growth in the number of mixture terms and this is handled here by utilising a Gaussian mixture reduction step after both the time and measurement updates. In addition, a square-root implementation of the unified algorithm is presented and this algorithm is profiled on several simulated systems. This includes the state estimation for two non-linear systems that are strictly outside the class considered in this paper.  ( 2 min )
    Private Online Prediction from Experts: Separations and Faster Rates. (arXiv:2210.13537v3 [cs.LG] UPDATED)
    Online prediction from experts is a fundamental problem in machine learning and several works have studied this problem under privacy constraints. We propose and analyze new algorithms for this problem that improve over the regret bounds of the best existing algorithms for non-adaptive adversaries. For approximate differential privacy, our algorithms achieve regret bounds of $\tilde{O}(\sqrt{T \log d} + \log d/\varepsilon)$ for the stochastic setting and $\tilde{O}(\sqrt{T \log d} + T^{1/3} \log d/\varepsilon)$ for oblivious adversaries (where $d$ is the number of experts). For pure DP, our algorithms are the first to obtain sub-linear regret for oblivious adversaries in the high-dimensional regime $d \ge T$. Moreover, we prove new lower bounds for adaptive adversaries. Our results imply that unlike the non-private setting, there is a strong separation between the optimal regret for adaptive and non-adaptive adversaries for this problem. Our lower bounds also show a separation between pure and approximate differential privacy for adaptive adversaries where the latter is necessary to achieve the non-private $O(\sqrt{T})$ regret.  ( 2 min )
    Case-Base Neural Networks: survival analysis with time-varying, higher-order interactions. (arXiv:2301.06535v2 [stat.ML] UPDATED)
    Neural network-based survival methods can model data-driven covariate interactions. While these methods can provide better predictive performance than regression-based approaches, not all can model time-varying interactions and complex baseline hazards. To address this, we propose Case-Base Neural Networks (CBNNs) as a new approach that combines the case-base sampling framework with flexible neural network architectures. Using a novel sampling scheme and data augmentation to naturally account for censoring, we construct a feed-forward neural network that may take time as an input. CBNNs predict the probability of an event occurring at a given moment to estimate the hazard function. We compare the performance of CBNNs to regression and neural network-based survival methods in a simulation and three case studies using two time-dependent metrics. First, we examine performance on a simulation involving a complex baseline hazard and time-varying interactions to assess all methods, with CBNN outperforming competitors. Then, we apply all methods to three real data applications, with CBNNs outperforming the competing models in two studies and showing similar performance in the third. Our results highlight the benefit of combining case-base sampling with deep learning to provide a simple and flexible modeling framework for data-driven, time-varying interaction modeling of single event survival outcomes. An R package is available at https://github.com/Jesse-Islam/cbnn.  ( 2 min )
    Causal Rule Ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects. (arXiv:2009.09036v5 [stat.ME] UPDATED)
    In health and social sciences, it is critically important to identify subgroups of the study population where there is notable heterogeneity of treatment effects (HTE) with respect to the population average. Decision trees have been proposed and commonly adopted for data-driven discovery of HTE due to their high level of interpretability. However, single-tree discovery of HTE can be unstable and oversimplified. This paper introduces Causal Rule Ensemble (CRE), a new method for HTE discovery and estimation through an ensemble-of-trees approach. CRE offers several key features, including 1) an interpretable representation of the HTE; 2) the ability to explore complex heterogeneity patterns; and 3) high stability in subgroups discovery. The discovered subgroups are defined in terms of interpretable decision rules. Estimation of subgroup-specific causal effects is performed via a two-stage approach for which we provide theoretical guarantees. Via simulations, we show that the CRE method is highly competitive when compared to state-of-the-art techniques. Finally, we apply CRE to discover the heterogeneous health effects of exposure to air pollution on mortality for 35.3 million Medicare beneficiaries across the contiguous U.S.  ( 2 min )
    Global Optimality in Bivariate Gradient-based DAG Learning. (arXiv:2306.17378v1 [cs.LG])
    Recently, a new class of non-convex optimization problems motivated by the statistical problem of learning an acyclic directed graphical model from data has attracted significant interest. While existing work uses standard first-order optimization schemes to solve this problem, proving the global optimality of such approaches has proven elusive. The difficulty lies in the fact that unlike other non-convex problems in the literature, this problem is not "benign", and possesses multiple spurious solutions that standard approaches can easily get trapped in. In this paper, we prove that a simple path-following optimization scheme globally converges to the global minimum of the population loss in the bivariate setting.  ( 2 min )
    Capturing functional connectomics using Riemannian partial least squares. (arXiv:2306.17371v1 [stat.ME])
    For neurological disorders and diseases, functional and anatomical connectomes of the human brain can be used to better inform targeted interventions and treatment strategies. Functional magnetic resonance imaging (fMRI) is a non-invasive neuroimaging technique that captures spatio-temporal brain function through blood flow over time. FMRI can be used to study the functional connectome through the functional connectivity matrix; that is, Pearson's correlation matrix between time series from the regions of interest of an fMRI image. One approach to analysing functional connectivity is using partial least squares (PLS), a multivariate regression technique designed for high-dimensional predictor data. However, analysing functional connectivity with PLS ignores a key property of the functional connectivity matrix; namely, these matrices are positive definite. To account for this, we introduce a generalisation of PLS to Riemannian manifolds, called R-PLS, and apply it to symmetric positive definite matrices with the affine invariant geometry. We apply R-PLS to two functional imaging datasets: COBRE, which investigates functional differences between schizophrenic patients and healthy controls, and; ABIDE, which compares people with autism spectrum disorder and neurotypical controls. Using the variable importance in the projection statistic on the results of R-PLS, we identify key functional connections in each dataset that are well represented in the literature. Given the generality of R-PLS, this method has potential to open up new avenues for multi-model imaging analysis linking structural and functional connectomics.  ( 2 min )
    iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models. (arXiv:2306.17361v1 [cs.LG])
    Structural causal models (SCMs) are widely used in various disciplines to represent causal relationships among variables in complex systems. Unfortunately, the true underlying directed acyclic graph (DAG) structure is often unknown, and determining it from observational or interventional data remains a challenging task. However, in many situations, the end goal is to identify changes (shifts) in causal mechanisms between related SCMs rather than recovering the entire underlying DAG structure. Examples include analyzing gene regulatory network structure changes between healthy and cancerous individuals or understanding variations in biological pathways under different cellular contexts. This paper focuses on identifying $\textit{functional}$ mechanism shifts in two or more related SCMs over the same set of variables -- $\textit{without estimating the entire DAG structure of each SCM}$. Prior work under this setting assumed linear models with Gaussian noises; instead, in this work we assume that each SCM belongs to the more general class of nonlinear additive noise models (ANMs). A key contribution of this work is to show that the Jacobian of the score function for the $\textit{mixture distribution}$ allows for identification of shifts in general non-parametric functional mechanisms. Once the shifted variables are identified, we leverage recent work to estimate the structural differences, if any, for the shifted variables. Experiments on synthetic and real-world data are provided to showcase the applicability of this approach.  ( 3 min )
    An Oblivious Stochastic Composite Optimization Algorithm for Eigenvalue Optimization Problems. (arXiv:2306.17470v1 [math.OC])
    In this work, we revisit the problem of solving large-scale semidefinite programs using randomized first-order methods and stochastic smoothing. We introduce two oblivious stochastic mirror descent algorithms based on a complementary composite setting. One algorithm is designed for non-smooth objectives, while an accelerated version is tailored for smooth objectives. Remarkably, both algorithms work without prior knowledge of the Lipschitz constant or smoothness of the objective function. For the non-smooth case with $\mathcal{M}-$bounded oracles, we prove a convergence rate of $ O( {\mathcal{M}}/{\sqrt{T}} ) $. For the $L$-smooth case with a feasible set bounded by $D$, we derive a convergence rate of $ O( {L^2 D^2}/{(T^{2}\sqrt{T})} + {(D_0^2+\sigma^2)}/{\sqrt{T}} )$, where $D_0$ is the starting distance to an optimal solution, and $ \sigma^2$ is the stochastic oracle variance. These rates had only been obtained so far by either assuming prior knowledge of the Lipschitz constant or the starting distance to an optimal solution. We further show how to extend our framework to relative scale and demonstrate the efficiency and robustness of our methods on large scale semidefinite programs.  ( 2 min )
    Why Shallow Networks Struggle with Approximating and Learning High Frequency: A Numerical Study. (arXiv:2306.17301v1 [cs.LG])
    In this work, a comprehensive numerical study involving analysis and experiments shows why a two-layer neural network has difficulties handling high frequencies in approximation and learning when machine precision and computation cost are important factors in real practice. In particular, the following fundamental computational issues are investigated: (1) the best accuracy one can achieve given a finite machine precision, (2) the computation cost to achieve a given accuracy, and (3) stability with respect to perturbations. The key to the study is the spectral analysis of the corresponding Gram matrix of the activation functions which also shows how the properties of the activation function play a role in the picture.  ( 2 min )
    Kernel $\epsilon$-Greedy for Contextual Bandits. (arXiv:2306.17329v1 [stat.ML])
    We consider a kernelized version of the $\epsilon$-greedy strategy for contextual bandits. More precisely, in a setting with finitely many arms, we consider that the mean reward functions lie in a reproducing kernel Hilbert space (RKHS). We propose an online weighted kernel ridge regression estimator for the reward functions. Under some conditions on the exploration probability sequence, $\{\epsilon_t\}_t$, and choice of the regularization parameter, $\{\lambda_t\}_t$, we show that the proposed estimator is consistent. We also show that for any choice of kernel and the corresponding RKHS, we achieve a sub-linear regret rate depending on the intrinsic dimensionality of the RKHS. Furthermore, we achieve the optimal regret rate of $\sqrt{T}$ under a margin condition for finite-dimensional RKHS.  ( 2 min )
  • Open

    Best Books to Learn Neural Networks in 2023 for Beginners (Updated) -
    submitted by /u/Lakshmireddys [link] [comments]  ( 8 min )

  • Open

    What ai voice generator did they use for this video?
    submitted by /u/spicywasabi [link] [comments]  ( 8 min )
    Voice Modulation
    I am really interested in self hosting my own voice modulation. Particularly impersonating cartoon characters. How can someone develop and train one of these models? submitted by /u/jesus-da-wizard [link] [comments]  ( 8 min )
    Does ChatGPT Keep Our Data?
    Do you think ChatGPT Keep Our Data? submitted by /u/Imagine-your-success [link] [comments]  ( 8 min )
    How to Fix AutoGPT and Build a Proto-AGI
    submitted by /u/lorepieri [link] [comments]  ( 8 min )
    One-Minute Daily AI News 7/2/2023
    Moody’s Corp. is using Microsoft Corp. and OpenAI to create an artificial intelligence assistant that will help customers of the credit rating and research firm analyze reams of information needed to make assessments of risk. “Moody’s Research Assistant” will roll out to customers including analysts, bankers, advisers, researchers, and investors.[1] Unity announces the release of Muse: A Text-to-Video Games Platform that lets you create textures, sprites, and animations with natural languages.[2] The New York State Legislature passed a number of bills this session, including one that would ban “deepfake” images online. Deepfakes are images or videos that have been manipulated to make it appear as if someone is saying or doing something they never said or did. The bill would make it illegal to create or distribute deepfakes that are used to harm or humiliate someone.[3] As per Times Now Report, Reece Wiench, 23, and Deyton Truitt, 26, decided to break away from tradition by holding a unique wedding ceremony. Instead of a physical human officiant, the couple opted for a machine featuring ChatGPT. The machine, adorned with a mask resembling the famous C-3PO from Star Wars, took center stage.[4] Sources: [1] https://www.bloomberg.com/news/articles/2023-06-29/moody-s-will-use-microsoft-openai-for-research-tool-to-assess-risk [2] https://www.marktechpost.com/2023/06/30/unity-announce-the-release-of-muse-a-text-to-video-games-platform-that-lets-you-create-textures-sprites-and-animations-with-natural-language/ [3] https://www.newsday.com/news/region-state/state-legislature-deepfakes-artificial-intelligence-liquor-store-hours-kathy-hochul-ks257o8t [4] https://odishatv.in/news/technology/chatgpt-officiates-wedding-couple-employs-ai-as-host-for-unusual-ceremony-208457 submitted by /u/Excellent-Target-847 [link] [comments]  ( 8 min )
    AI to create songs from my lyrics like Songr
    Are there any other AI apps or sites like Songr, that basically creates and sings a song out of the lyrics you give it? I quite enjoy this one, and I want to know if there are any other. submitted by /u/Would-Be-Superhero [link] [comments]  ( 8 min )
    What is the best way to clone a voice theses days
    Just wondering what GitHub repo or some open source option exists to create a voice model from and to use in a text to speech engine ? submitted by /u/suprem_lux [link] [comments]  ( 8 min )
    Recommendations for generative AI that makes diagrams and simple animations of schemes?
    I'm looking for a generative AI for either diagrams, ideally animated. So far Google hasn't helped me found anything great. submitted by /u/fjanko [link] [comments]  ( 8 min )
    Introducing Interview AI - Get 100% job interview ready with AI
    Hey there, amazing r/artifical community! I'm thrilled to introduce you to Interview AI, the ultimate companion for interview preparation. Whether you're aiming for your dream job or want to level up your interview skills, Interview-AI is here to support you every step of the way. What is Interview-AI? Interview-AI is your round-the-clock interview coach designed to help you excel in job interviews. We understand the importance of targeted and personalized interview practice, and that's why we've created a platform that offers tailored questions and detailed feedback, just for you. ps- We have just launched Interview AI on Product Hunt, would love if you can check it out the product, give us some feedback, and (of course) would really appreciate your support 😄🙏. https://www.producthunt.com/posts/interview-ai submitted by /u/data-leon [link] [comments]  ( 8 min )
    Can you help me create a Home companion? Ideas, Suggestions welcome.
    Setting the post to NSFW because of the mention of sexdoll ai My situation is unique in some ways, Please read to the end before offering any suggestions or help. I live my life in my house, I probably leave my home 2-4 times a year, I am classed as severely disabled, mostly mental issues from Autism, ADHD, Bipolar, OCD and PTSD, I am also in remission from bowel cancer. For 20 years I used technology to help look after my Wife, when she died technology was the only thing that kept me in this world, group therapy didn't work, step programs didn't work, 5 different therapist, clinical psychologists, medication and even long stay hospital didn't work. But technology did, ever since the first gaming console in 1976 (A Binatone Master system) and the first hand held in 1980 (galaxy invader…  ( 10 min )
    It's the summer of love, baby !
    submitted by /u/Akumetsu_971 [link] [comments]  ( 8 min )
    Does Open AI and the likes pay the creators at all for the huge amount of data that they use to train their language models?
    Just wondering, since for private persons, copyright is very strictly enforced submitted by /u/Aesthetik_1 [link] [comments]  ( 8 min )
  • Open

    PPO is off-policy in RLHF(LLM)?
    Why people say that PPO is off-policy in RLHF (language model)? This doesn't make any sense to me. PPO is on-policy algo. The reward model is trained using the pre-collected data. But regardless of this, the reward model is tight to the idea of off-policy right? Why some of the people make the claim that LLM training with RLHF and PPO, you are doing PPO in the off-policy fashion? ​ submitted by /u/No_Oilve_6577 [link] [comments]  ( 8 min )
    Normalized Observation that has a shifted Normal Distribution with a bar at 0
    I want to normalized my observations for my reinforcement learning agent, but the observation that I am trying to normalize has a distribution that is like a normal distribution with a mean that is shifted up, and with values at 0. It's something like 50% of the time the observation is 0 and for other 50 times, it's from a normal distribution with mean at 50. How should I normalize this type of observation? Thanks submitted by /u/LeSUTHU [link] [comments]  ( 8 min )
    EvoTorch using custom environment
    Hello experts, Does anybody have any experience to use a custom environment, not using gym, and evaluate using evotorch library? So far all examples in their documents are based on GymNE which is a built-in function. But my environment is totally hand-made. I can't seem to find anything on github either. submitted by /u/vincent0110 [link] [comments]  ( 8 min )
  • Open

    [R] Automated CPU Design With AI
    submitted by /u/EducationalCicada [link] [comments]  ( 8 min )
    [Discussion] What opportunities exist for companies to develop domain-specific LLM models?
    I was wondering if there is a need for LLM models that are for a specific purpose. For example, I am imagining an LLM model that is specialized for generating code that would be better at generating code than a general purpose LLM. Or is there no need for this because of the big general purpose LLMs that are trending? submitted by /u/BornAgain20Fifteen [link] [comments]  ( 8 min )
    [D] Validation accuracy greater than training accuracy
    I'm new to deep learning domain and I justed build a model to classify 9 classes. But somehow i get the validation accuracy 0.89 and training accuracy of 0.7 even though i use the same data. this is the link of my notebook : https://www.kaggle.com/code/ayoubsarab/mammals-classification-high-accuracy submitted by /u/Ordinary_Run_2513 [link] [comments]  ( 8 min )
    [P] P40s not working?
    Hi, Im trying to get 2x P40s working on a Asus Z270-p. Ive got 4g decoding on, tried setting everything to gen2. I removed all the drives and m.2s. But it still wont post. If i remove either of the 2 cards it posts just fine and boots. But with 2 it refuses to do anything. Any ideas? submitted by /u/ISwearImNotAnAI [link] [comments]  ( 8 min )
    [R] A visual critical review of most of the time series anomaly detection (TSAD) datasets
    submitted by /u/eamonnkeogh [link] [comments]  ( 8 min )
    [P] Source Code - Executable Dataset
    I need to make a large dataset of source code - executable pairs. Webscraping executables is easy, but finding matching source code is not. I'm thinking of generating the source code using ML then compiling it. Which models would best fit this task? I don't have a specific max file size yet, but a model which can make bigger programs in many languages with a variety a libraries is desirable. I'm currently looking at StarCoder. submitted by /u/boringdude123 [link] [comments]  ( 8 min )
    [D] Can I use faiss as retrieve on recommendation system?
    I'm planning on using faiss to generate candidates and then lightgbm to rank the candidates. I think about using E-commerce data, as most of the examples I see using faiss are textual data. Is using faiss for this a good approach? submitted by /u/Mota287 [link] [comments]  ( 8 min )
    [R] Adversarial Robust Deep Reinforcement Learning Requires Redefining Robustness
    https://ojs.aaai.org/index.php/AAAI/article/view/26009/25781 submitted by /u/ml_dnn [link] [comments]  ( 8 min )
    [D] ML Engineer vs. MLOps Engineer
    As the need for ML infra grows, the confusion between the "ML Engineer" vs. "MLOps Engineer" roles seems to be steadily increasing, and with it the number of online articles on the subject (quite a lot here, including our own recent blog too). Do we think this is something that will get clearer as the industry matures? Or is it more of a "giff" vs. "jiff" type situation? Or will this not matter once the "Prompt Engineers" wipe us all out? ;) https://preview.redd.it/35kxge9fyk9b1.png?width=500&format=png&auto=webp&s=b2fb0394ddc02b18be27bb50e2c59a4a18cb7a83 submitted by /u/kazhdan_d [link] [comments]  ( 8 min )
    [D] Latest techniques on reducing the learning rate sensitivity?
    I've observed sensitivity to the LR scheduling in the transformer architecture I've been training on some sequence generation task. I'm using Adam optimiser. Changing LR scheduling from linear decay to cubic decay seems to have made it much worse. I've warmup steps. Now I'm worrying if even linear decay is much worse than something else that I haven't tested. The loss curve is well behaved in both the cases but the accuracy of one is much better than the other. Is there a robust way to schedule the learning rate in transformers? submitted by /u/Professor_Entropy [link] [comments]  ( 8 min )
    [D] Simple Questions Thread
    Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]  ( 8 min )
    [D] The road to Machine Learning/Deep Learning Research for a high schooler
    Hello r/MachineLearning I am a high schooler right now and I have had some previous experience with ML and DL. I recently completed fast.ai's course "Practical Deep Learning for Coders Part 1". Currently, I am taking MITOCW's Linear Algebra course and will take a multivariable calculus course when I am finished with that, as I understand how important math is to ML/DL. Pytorch is also something I will take as well. My ultimate goal is to be able to conduct research with ML/DL. Specifically, to research the models themselves and to create new models. That is why I am taking such a math-oriented approach to learning ML/DL. My main question really is what is the path I should take to get to academic research with ML/DL? The math and the frameworks I consider to be the pre-requisites, and after these steps, I am unsure of how to break into the academic space. Any tips or if someone could speak from experience would be greatly appreciated! I am super excited about academic research with ML and I hope I will get there one day! Thanks! submitted by /u/Mr_Funkedeli [link] [comments]  ( 9 min )
  • Open

    100 Snakes in AI Battle Royale using C++ and SFML. Source code is in the description.
    submitted by /u/Kofybrek [link] [comments]  ( 8 min )
  • Open

    Bounds on the incomplete beta function (beta CDF)
    The incomplete beta function is defined by It is called incomplete because the limit of integration doesn’t necessarily run all the way up to 1. When z = 1 the incomplete beta function is simply the beta function discussed in the previous post [1]. The incomplete beta function is proportional to the CDF of a […] Bounds on the incomplete beta function (beta CDF) first appeared on John D. Cook.  ( 5 min )
    Upper and lower bounds on the beta function
    The beta function B(x, y) is defined by and is the normalizing constant for the beta probability distribution. It is related to the gamma function via The beta function comes up often in applications. It can be challenging to work with, however, and so estimates for the function are welcome. The function 1/xy gives simple […] Upper and lower bounds on the beta function first appeared on John D. Cook.  ( 5 min )

  • Open

    Implementing Gradient Descent in PyTorch
    The gradient descent algorithm is one of the most popular techniques for training deep neural networks. It has many applications in fields such as computer vision, speech recognition, and natural language processing. While the idea of gradient descent has been around for decades, it’s only recently that it’s been applied to applications related to deep […] The post Implementing Gradient Descent in PyTorch appeared first on MachineLearningMastery.com.  ( 25 min )

  • Open

    Training a Linear Regression Model in PyTorch
    Linear regression is a simple yet powerful technique for predicting the values of variables based on other variables. It is often used for modeling relationships between two or more continuous variables, such as the relationship between income and age, or the relationship between weight and height. Likewise, linear regression can be used to predict continuous […] The post Training a Linear Regression Model in PyTorch appeared first on MachineLearningMastery.com.  ( 24 min )
    Making Linear Predictions in PyTorch
    Linear regression is a statistical technique for estimating the relationship between two variables. A simple example of linear regression is to predict the height of someone based on the square root of the person’s weight (that’s what BMI is based on). To do this, we need to find the slope and intercept of the line. […] The post Making Linear Predictions in PyTorch appeared first on MachineLearningMastery.com.  ( 21 min )

  • Open

    Loading and Providing Datasets in PyTorch
    Structuring the data pipeline in a way that it can be effortlessly linked to your deep learning model is an important aspect of any deep learning-based system. PyTorch packs everything to do just that. While in the previous tutorial, we used simple datasets, we’ll need to work with larger datasets in real world scenarios in […] The post Loading and Providing Datasets in PyTorch appeared first on MachineLearningMastery.com.  ( 20 min )

  • Open

    Using Dataset Classes in PyTorch
    In machine learning and deep learning problems, a lot of effort goes into preparing the data. Data is usually messy and needs to be preprocessed before it can be used for training a model. If the data is not prepared correctly, the model won’t be able to generalize well. Some of the common steps required […] The post Using Dataset Classes in PyTorch appeared first on MachineLearningMastery.com.  ( 21 min )

  • Open

    Calculating Derivatives in PyTorch
    Derivatives are one of the most fundamental concepts in calculus. They describe how changes in the variable inputs affect the function outputs. The objective of this article is to provide a high-level introduction to calculating derivatives in PyTorch for those who are new to the framework. PyTorch offers a convenient way to calculate derivatives for […] The post Calculating Derivatives in PyTorch appeared first on Machine Learning Mastery.  ( 20 min )

  • Open

    Two-Dimensional Tensors in Pytorch
    Two-dimensional tensors are analogous to two-dimensional metrics. Like a two-dimensional metric, a two-dimensional tensor also has $n$ number of rows and columns. Let’s take a gray-scale image as an example, which is a two-dimensional matrix of numeric values, commonly known as pixels. Ranging from ‘0’ to ‘255’, each number represents a pixel intensity value. Here, […] The post Two-Dimensional Tensors in Pytorch appeared first on Machine Learning Mastery.  ( 21 min )

  • Open

    One-Dimensional Tensors in Pytorch
    PyTorch is an open-source deep learning framework based on Python language. It allows you to build, train, and deploy deep learning models, offering a lot of versatility and efficiency. PyTorch is primarily focused on tensor operations while a tensor can be a number, matrix, or a multi-dimensional array. In this tutorial, we will perform some […] The post One-Dimensional Tensors in Pytorch appeared first on Machine Learning Mastery.  ( 22 min )

  • Open

    365 Data Science courses free until November 21
    Sponsored Post   The unlimited access initiative presents a risk-free way to break into data science.     The online educational platform 365 Data Science launches the #21DaysFREE campaign and provides 100% free unlimited access to all content for three weeks. From November 1 to 21, you can take courses from renowned instructors and earn […] The post 365 Data Science courses free until November 21 appeared first on Machine Learning Mastery.  ( 15 min )

  • Open

    Attend the Data Science Symposium 2022, November 8 in Cincinnati
    Sponsored Post      Attend the Data Science Symposium 2022 on November 8 The Center for Business Analytics at the University of Cincinnati will present its annual Data Science Symposium 2022 on November 8. This all day in-person event will have three featured speakers and two tech talk tracks with four concurrent presentations in each track. The […] The post Attend the Data Science Symposium 2022, November 8 in Cincinnati appeared first on Machine Learning Mastery.  ( 10 min )

  • Open

    My family's unlikely homeschooling journey
    My husband Jeremy and I never intended to homeschool, and yet we have now, unexpectedly, committed to homeschooling long-term. Prior to the pandemic, we both worked full-time in careers that we loved and found meaningful, and we sent our daughter to a full-day Montessori school. Although I struggled with significant health issues, I felt unbelievably lucky and fulfilled in both my family life and my professional life. The pandemic upended my careful balance. Every family is different, with different needs, circumstances, and constraints, and what works for one may not work for others. My intention here is primarily to share the journey of my own (very privileged) family. Our unplanned introduction to homeschooling For the first year of the pandemic, most schools in California, where …  ( 7 min )

  • Open

    The Jupyter+git problem is now solved
    Jupyter notebooks don’t work with git by default. With nbdev2, the Jupyter+git problem has been totally solved. It provides a set of hooks which provide clean git diffs, solve most git conflicts automatically, and ensure that any remaining conflicts can be resolved entirely within the standard Jupyter notebook environment. To get started, follow the directions on Git-friendly Jupyter. Contents The Jupyter+git problem The solution The nbdev2 git merge driver The nbdev2 Jupyter save hook Background The result Postscript: other Jupyter+git tools ReviewNB An alternative solution: Jupytext nbdime The Jupyter+git problem Jupyter notebooks are a powerful tool for scientists, engineers, technical writers, students, teachers, and more. They provide an ideal notebook environment for interact…  ( 7 min )
2023-08-01T00:56:02.910Z osmosfeed 1.15.1